Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

Enterprise Architecture (EA) Tools unterstützen Unternehmen und Organisationen dabei, mit ihren IT-Strategien die Geschäftsziele optimal zu unterstützen. Sie sorgen ebenfalls dafür, dass Unternehmen ihre Roadmaps für die digitale Transformation geordnet vorantreiben können. EA Tools bieten dafür unter anderem Collaboration-, Reporting-, Testing- und Simulationsfunktionen. Mit deren Hilfe lassen sich Modelle implementieren, die Geschäfts- und IT-Prozesse gezielt verbessern.

Um die beste Lösung für Ihr Unternehmen zu finden, sollten Sie zuerst prüfen, ob sich das jeweilige Tool mit Ihrem Technologie-Stack integrieren lässt. Anschließend gilt es abzuwägen, ob die Informationen, Diagramme und Tabellen, die die Software zur Verfügung stellt, für das Unternehmen auch einen echten Nutzwert haben.

Empfehlenswerte Enterprise-Architecture-Tools

Nachfolgend finden Sie einen Überblick über die wichtigsten Enterprise-Architecture-Tools – in alphabetischer Reihenfolge. Sie stellen einen Mix aus Visualisierungs-, Collaboration- und Project-Management-Funktionen bereit und unterstützen eine Vielzahl von Enterprise Architecture Frameworks.

Ardoq

Nachdem zuerst über einfache Formulare Informationen von Usern, Entwicklern und sonstigen Stakeholdern im Unternehmen eingesammelt wurden, lässt sich mithilfe von Ardoq ein digitaler Zwilling der gesamten Organisation erstellen. Der Ansatz setzt also darauf, die Menschen, die in ihren Rollen mit den verschiedensten Systemen arbeiten, realistisch in ihrer Arbeitswelt abzubilden.

Jede Mitarbeiterin und jeder Mitarbeiter im Unternehmen kann später von den Netzwerkvisualisierungen und Datenfluss-Diagrammen profitieren, um seine eigene Rolle optimal zu unterstützen und den Arbeitsplatz immer wieder anzupassen und zu modernisieren. Das Tool lässt sich mit den wichtigsten Cloud-Plattformen integrieren. Es bietet eine API, die individuelle Anpassungen in allen wichtigen Programmiersprachen (Python, C#, Java, etc.) ermöglicht.

Wichtigste Use Cases:

• “Architektonischen Stress” bei Lastspitzen simulieren, falls größere Veränderungen bevorstehen;

• Verstehen, wie verändertes Nutzerverhalten neue Anforderungen generiert;

• Application Portfolio Management, um besser strategisch zu planen.

Atoll Group SAMU

Das EA-Tool SAMU macht die Enterprise Architecture sichtbar, indem es tiefe Verknüpfungen zwischen On-Premises-Systemen, dem Cloud-Layer und Tools für das Business Process Management aufzeigt. Das Tool der Atoll Group bietet vielfältige Integrationsmöglichkeiten, zum Beispiel mit Monitoring-Tools (etwa Tivoli, ServiceNow), Configuration-Management-Datenbanken (zum Beispiel CA, BMC) oder Service-Organisations-Tools (BMC, HPE). Alle Informationen fließen in ein zentrales Datenmodell ein, das um den zusätzlichen Input der Stakeholder weiter angereichert wird.

Wichtigste Use Cases:

• Enterprise-Architektur visualisieren;

• strategische Planungsprozesse und Architektur-Reviews mit Informationen unterfüttern;

• mithilfe einer visuellen Verständnisgrundlage die Kommunikation verbessern.

Avolution Abacus

Dieses Tool erfasst die Breite und den Umfang der Unternehmensarchitektur mit Hilfe eines auf Diagrammen basierenden Dashboards. Die Integration mit gängigen Tools wie SharePoint, Excel, Visio, Google Sheets, Technopedia oder ServiceNow vereinfacht die Nutzung. Abacus wurde inzwischen auch um einen Machine-Learning-Layer ergänzt, der es Anwendern ermöglicht, ein Modell zu trainieren, das ihnen beispielsweise hilft zu erkennen, wer im Unternehmen für welches System verantwortlich ist.

Wichtigste Use Cases:

• die IT für das gesamte Unternehmen “öffnen”, um ein allgemeines Verständnis der Datenflüsse zu erzeugen;

• umfassendes Enterprise Modeling, um eine Roadmap für künftige Entwicklungen zu erstellen;

• Business-Metriken tracken, die mit der Unternehmens-Performance zusammenhängen.

BOC Group ADOIT

ADOIT soll Teams dabei unterstützen, Ressourcen zu verwalten, Bedarfe vorherzusagen und Assets zu tracken. Dazu mappt das Tool jedes System oder Softwarepaket mit einem Objekt. Die Datenflüsse zwischen den Systemen werden in Beziehungen umgewandelt, die von diesen Objekten mithilfe eines anpassbaren Metamodells erfasst werden. Geschäftsprozesse können auf ähnliche Weise über ein gut integriertes Begleitprodukt namens ADONIS modelliert werden. ADOIT ist Web-basiert und lässt sich auch mit Tools wie Atlassian Confluence integrieren, um die Datenerfassung und -entwicklung zu beschleunigen.

Wichtigste Use Cases:

• ein unternehmensweites Modell erstellen, das bei sämtlichen Teammitgliedern ein Verständnis über den Stack schafft – und wie man diesen verbessern kann;

• vollständiger Zugriff auf EA-Daten über eine Mobile-Anwendung;

• bei Fusionen und Übernahmen den Tech-Bereich durch genaues Asset-Mapping orchestrieren.

Bizzdesign Hopex

Nach der Übernahme von Mega International zählt die Hopex-Plattform zum Portfolio von Bizzdesign. Sie soll dabei unterstützen, Unternehmensanwendungen zu modellieren und dabei ein Verständnis der von ihnen unterstützten Geschäfts-Workflows schaffen. Dabei liegt ein Schwerpunkt auf den Bereichen Data Governance und Risikomanagement. Hopex basiert auf Microsoft Azure und stützt sich auf eine Reihe offener Standards wie GraphQL und REST Queries, um Informationen aus Komponentensystemen zu sammeln. Das Reporting ist mit den Office-Tools von Microsoft sowie mit grafischen Lösungen wie Tableau und Qlik integriert.

Wichtigste Use Cases:

• datengestützte Erkenntnisse herbeiführen, um Cloud- und Anwendungsbereitstellung zu steuern;

• akkurate Nutzungsmodelle erstellen, um Architekturanforderungen zu verstehen;

• eine Bedarfsschätzung mit Umfragen und anderen Tools vornehmen, um für die Zukunft zu planen.

Bizzdesign Horizzon

Das Tool dient dazu, Business Workflows und den zugrundeliegenden Tech-Stack zu modellieren. Dazu bietet Horizzon ein Graph-basiertes Modell, das Daten von sämtlichen Stakeholdern einsammelt und diese an eine Analytics-Engine weitergibt. Im Ergebnis entstehen Diagramme, die den aktuellen Systemzustand widerspiegeln. Wichtige Schwerpunkte dieses Tools sind Change Management und Zukunftsplanung: Horizzon ist nicht zuletzt dafür konzipiert worden, die Risiken eines Redesigns zu minimieren. Das Toolset unterstützt die wichtigsten Frameworks ArchiMate, TOGAF und BPMN. Neben Mega hat Bizzdesign im Januar 2025 auch den EA-Geschäftsbereich der Software AG – Alfabet – übernommen.

Wichtigste Use Cases:

• Vorhersage zukünftiger Anforderungen durch Predictive Modeling;

• Orchestrieren von Workflows auf der Basis der technischen und der Business-Architektur;

• Antizipieren von Risiken sowie Security- und Governance-Problemen durch die Modellierung von Datensicherheitsanforderungen.

Capstera

Das Tool von Capstera fokussiert darauf, die Business Architecture selbst abzubilden. Value und Process Maps helfen dabei, die Rollen der verschiedenen Unternehmensbereiche zu definieren und nachzuverfolgen. Dabei können im laufenden Prozess Verknüpfungen mit den zugrundeliegenden Softwarprodukten und Tools hinzugefügt werden.

Wichtigste Use Cases:

• Reports erstellen, die sich erst einmal mit der Business-Architektur selbst beschäftigen;

• Beziehungen zwischen Menschen, Abteilungen und Rollen analysieren;

• die langfristige strategische Planung vorantreiben.

Clausmark Bee360

Teammitglieder, die Clausmarks Flaggschiffprodukt Bee360 (früher Bee4IT) verwenden, wollen eine einfache “Single Source of Truth” über die Workflows im Unternehmen. Ziel ist es, verschiedenen betrieblichen Rollen intelligentere Entscheidungen zu ermöglichen. Das Modul Bee360 FM (Finanzmanagement) bietet etwa die Möglichkeit, Kosten nachzuvollziehen und zuzuordnen. Die Anwender können verschiedene solcher Module miteinander verknüpfen, um EAM, Finanzmanagement, Portfolio Management und Agile Planning nahtlos zu integrieren – bei maximaler Transparenz.

Wichtigste Use Cases:

• C-Suite-Ebene befähigen, Projekte zu managen und Assets zuzuweisen;

• präzise digitale Zwillinge entwickeln, um ein Verständnis über Datenflüsse zu schaffen und künftige Erweiterungen zu planen;

• integrierte Wissensdatenbank aufbauen, um alle digitalen Workflows zu tracken.

EAS

Das Essential-Paket von EAS (Enterprise Architecture Solutions) nahm als Open-Source-Projekt seinen Anfang und hat sich inzwischen zu einer kommerziell verfügbaren Cloud-Lösung weiterentwickelt. Das Tool erstellt ein Metamodell, das die Interaktionen zwischen Systemen und Geschäftsprozessen beschreibt. Ebenfalls enthalten sind Pakete, um gängige Business Workflows wie Datenmanagement oder DSGVO-Compliance zu tracken.

Wichtigste Use Cases:

• den technischen Reifegrad der eigenen Architektur evaluieren;

• Sicherheit und Governance durch besseres Asset Tracking optimieren;

• wachsende Systemkomplexität kontrollieren und managen.

OrbusInfinity

Orbus Software hat Anfang 2025 die Akquisition seines Konkurrenten Capsifi abgeschlossen. Der Anbieter stellt mit OrbusInfinity eine Enterprise-Transformation-Plattform auf KI-Basis zur Verfügung, die schnellere, bessere Entscheidungen, Kosteinesparungen und Risikominimierung verspricht. Architecture-Teams sollen mit Hifle von OrbusInfinity mit einer Vielzahl von Stakeholdern interagieren können, um eine “digitale Blaupause” ihres Unternehmens zu generieren, die eine einheitliche Sicht auf das aktuelle und künftige Geschäft realisieren soll. Diverse Drittanbieter-Tools lassen sich außerdem mit der Plattform integrieren, darunter etwa von Microsoft, Flexera, ManageEngine oder ServiceNow.

Wichtigste Use Cases:

• Stakeholder-Management;

• Enterprise-Landschaften visualisieren;

• Entscheidungsfindung und Datenanalyse automatisieren.

Planview Enterprise One

Planview bietet eine ganze Reihe von Produkten, mit denen Unternehmen Teamwork, Prozesse und die Enterprise Architecture nachvollziehen können. Die Enterprise Tools sind in drei Kategorien unterteilt: strategisches Portfolio-Management, Produktportfolio-Management und Projektportfolio-Management. Im Zusammenspiel entstehen hardware- und Software-übergreifende Layer, die rollenbasierte Perspektiven für Führungskräfte und Teammitglieder eröffnen. Das Toolset integriert mit gängigen Ticket-Tracking-Systemen wie Jira, um Workflow-Analysen und Reports zu erstellen. Inzwischen hat Planview nach einer Übernahme neue Tools in sein Portfolio integriert, die früher unter den Namen Daptiv, Barometer und Projectplace bekannt waren.

Wichtigste Use Cases:

• eine langfristige, strategische Vision für die Architekturentwicklung aufbauen;

• Entwicklungsarbeit auf Projektebene tracken und in eine beliebige Strategie integrieren;

• mit Fokus auf die Customer Experience und die Produktstruktur den Change vorantreiben.

QualiWare Enterprise Architecture

Das Enterprise Architecture Tool von QualiWare ist Teil einer größeren Sammlung von Modellierungswerkzeugen, die darauf abzielt, sämtliche Geschäftsprozesse zu erfassen. Beispielsweise ist es möglich, einen digitalen Zwillinge zu bauen, mit dem sich Customer Journeys nachvollziehen lassen. Qualiware hat diverse KI-Algorithmen integriert, um Dokumentation und Process Discovery zu optimieren.

Wichtigste Use Cases:

• ein kollaboratives Ökosystem für Business Manager aufbauen, das ein Verständnis von der Enterprise Architecture vermittelt;

• architektonische Designelemente erfassen, um ein Wissens-Ökosystem rund um den Stack aufzubauen;

• eine breite Beteiligung in Sachen Dokumentationserstellung und -überprüfung fördern.

Quest Erwin Evolve

Das Erwin Evolve Tool von Quest hat sich von einem Datenmodellierungs-Tool zu einem System für Enterprise-Architecture- und Geschäftsprozess-Modellierung weiterentwickelt. Um die Komplexität moderner, ineinandergreifender Softwaresysteme und der von ihnen gemanagten Geschäftsprozesse zu durchdringen, können Anwender auf benutzerdefinierte Datenstrukturen zurückgreifen. Das Web-Tool erstellt Modelle, rollenbasierte Diagramme und andere Visualisierungen, die in allgemein zugängliche Dashboards einfließen. Zum Paket gehört ein KI-basiertes Modellierungs-Tool, das Whiteboard-Skizzen integrieren kann.

Wichtigste Use Cases:

• einen digitalen Zwilling für die strategische Modellierung der Enterprise Data Architecture erstellen;

• Customer Journeys verstehen;

• Services und Systeme mit Application Portfolio Management tracken.

SAP LeanIX Enterprise Architecture Suite

Die Tool-Sammlung von LeanIX umfasst unter anderem Enterprise Architecture Management und andere Bereiche, die für Aufgaben wie SaaS– und Value-Stream-Management wichtig sind – etwa um Cloud-Deployments und darauf laufende Services zu tracken. Die Daten die dabei über die IT-Infrastruktur gesammelt werden, fließen in ein grafisches Dashboard ein. Das Tool ist eng mit wichtigen Cloud-Workflow-Tools wie Confluence, Jira, Signavio und Lucidchart integriert. Das ist für Teams von Vorteil, die diese Tools bereits nutzen, um ihre Entwicklungsstrategien zu planen und umzusetzen. Seit November 2023 ist LeanIX Teil von SAP.

Wichtigste Use Cases:

• Anwendungsmodernisierung und Cloud-Migration managen;

• Obsoleszenz von Software-Services evaluieren;

• Kosten kontrollieren und managen.

ServiceNow

Die Tool-Sammlung von ServiceNow lässt sich auf verschiedene Architekturtypen herunterbrechen, darunter Assets, DevOps, Security und Service. Die Tools katalogisieren die unterschiedlichen Hardware- und Softwareplattformen, um Workflows und Datenflüsse im Unternehmen abzubilden und zu verstehen. Ausführliche Reportings und detaillierte Dashboards ermöglichen Analysen, auf deren Grundlage Risiken minimiert und die Ausfallsicherheit der Systeme erhöht werden können.

Wichtigste Use Cases:

• Tracken von Assets, Services und Systemen, die das Unternehmen ausmachen;

• Governance-Themen, Risikobegrenzung, IT-Management und Security Operations werden in einer Plattform zusammengeführt;

• durch die Integration von CRM-Tools lassen sich auch kundenorientierte Services managen.

Sparx Systems

Um Teams und Projekte verschiedener Größe und Komplexität zu unterstützen, hat Sparx vier Versionen seines EA-Tools entwickelt. Allen gemeinsam ist eine UML-basierte Modellierung, mit der sich die Komponenten komplexer Systeme tracken lassen. Eine Simulations-Engine ermöglicht “War Gaming” und vermittelt ein Verständnis darüber, wie sich Fehler ausbreiten und kaskadieren können. Sparx stellt zudem eine Vielzahl von vorgefertigten Design Patterns bereit, um Teams bei der Modellierung zu unterstützen.

Wichtigste Use Cases:

• Nachfrage- und Lastveränderungen zur Prognose künftiger Anforderungen simulieren;

• (potenzielle) Probleme durch eine Verbindungs-Matrix im Auge behalten;

• Dokumentation erstellen.

Unicom System Architect

System Architect ist eines der Angebote aus Unicoms Team Blue. Es handelt sich um ein Tool, das ein Metamodell verwendet, um automatisiert so viele Daten wie möglich über die laufenden Systeme zu sammeln – manchmal auch durch ein Reverse Engineering von Datenflüssen. Dieses systemweite Datenmodell kann über benutzerdefinierte Dashboards Teammitgliedern aller Rollen zugänglich gemacht werden. Ein weiteres erwähnenswertes Feature: Die Ressourcenzuweisung lässt sich mit Hilfe von Simulationen optimieren.

Wichtigste Use Cases:

• Was-wäre-wenn-Fragen zum Architekturmodell stellen;

• ein Metamodell von Daten und Systemen aufbauen;

• Migrations- und Transformationspläne erstellen.

ValueBlue BlueDolphin

Dieses EA-Tool sammelt Daten auf dreierlei Art:

1. Es importiert Basisdaten auf der Grundlage standardgesteuerter Automatisierung (ITSM, SAM).

2. Es arbeitet mit den Dateiformaten von Architekten und Systemdesignern – etwa ArchiMate oder BPMN.

3. Es gibt Fragebögen an andere Stakeholder heraus, die auf anpassbaren Vorlagen basieren.

Die aufbereiteten Informationen werden in einer visuellen Umgebung bereitgestellt, die Auskunft über die historische Entwicklung von Systemen gibt.

Wichtigste Use Cases:

• systemweite Daten von internen und externen Stakeholdern automatisiert und formularbasiert erfassen;

• zukunftsorientierte Reportings erzeugen, um den Change zu überwachen und voranzutreiben;

• Kooperation und Zusammenarbeit durch offenes Data Reporting fördern.

(fm)

Dieser Artikel ist im Original bei unserer Schwesterpublikation CIO.com erschienen.

ISC2 has begun developing a vendor-neutral AI security certification aimed at cybersecurity professionals working to secure AI systems and manage emerging AI risks.

The nonprofit organization, best known for the CISSP certification, says it is seeking volunteers worldwide to help define the knowledge and skills that will shape the new credential. While ISC2 has not finalized the certification domains, the certification is expected to address both technical AI security and governance topics, with a pilot exam planned before the end of 2026.

According to Casey Marks, ISC2 chief operating officer, feedback from cybersecurity practitioners led ISC2 to conclude that AI security had grown beyond expanding AI content within existing certifications.

“AI has reached a tipping point,” Marks says. “AI no longer is just another tool; instead, it has fundamentally changed the cybersecurity practice itself.”

ISC2 already includes AI-related content in certifications including CISSP and CCSP, but Marks says practitioners have identified new responsibilities and risks that extend beyond those programs. “Enterprise security teams are currently grappling with significant knowledge gaps, particularly around securing model architectures against new vulnerabilities like prompt injection, data poisoning, and model inversion,” Marks adds.

Organizations are working to understand emerging governance frameworks, including the NIST AI Risk Management Framework and ISO/IEC 42001, while adapting traditional application security and security operations workflows to AI systems, he says.

ISC2 has not finalized the certification domains, but Marks says the organization expects the credential to address both technical controls and governance practices for securing AI systems and managing AI risk. The certification will use ISC2’s established certification development process, which relies on cybersecurity practitioners to define job roles, develop exam content, and validate competencies.

Marks says ISC2 will continue to update the certification through ongoing input from cybersecurity professionals, in addition to its regular certification review process.

The organization is also determining which professionals the certification will target. Marks says AI security responsibilities are emerging across security architecture, risk management, security operations, software development security, governance and compliance, communication and network security, and security assessment and testing. ISC2 says the certification will reflect how those roles are evolving.

For organizations that are building AI security programs now, Marks recommends using existing AI training resources, adopting established governance frameworks, creating cross-functional AI security working groups, and participating in the certification development process.

Marks says ISC2 expects AI knowledge to become part of most cybersecurity roles while a more specialized AI security discipline continues to develop. He says organizations will increasingly need professionals with foundational AI security knowledge, as well as specialists in areas such as adversarial machine learning, model architectures, and AI data pipelines.

Looking ahead, Marks says he expects AI security expertise to evolve into both a foundational skill for cybersecurity professionals and a specialized discipline of its own.

“At this time, we are seeing a hybrid evolution occurring in real time: AI security is simultaneously becoming a baseline expectation for all security roles, while also carving out a dedicated, highly specialized discipline,” Marks says.

Among a range of factors leading to a wave of tech sector layoffs in 2026 is the rapid rise of artificial intelligence and automation. Companies are reconfiguring their workforces to leverage AI for increased efficiency and reduced operating costs. This realignment and reduction is implemented even by companies reporting strong financial performance.

But it’s not just AI leading to workforce cuts. Complementing this technological shift are ongoing economic uncertainty, inflation, and higher interest rates, compounded by a chip shortage and rising energy costs. This mix is driving companies to cut costs and streamline operations for increased efficiency.

According to data compiled by Layoffs.fyi, an online tracker that keep tabs on job losses in the technology sector, 123,941 tech employees were laid off at 269 companies in 2025. The site also reports that 71,981 government employees were laid off by DOGE alone, with 182,528 total federal workers laid off.

Here is a list — to be updated regularly — of some of the most prominent technology layoffs the industry has experienced recently.

Notable tech layoffs in 2026

• Monday.com
• Microsoft
• Meta
• Cisco
• Cloudflare
• Oracle
• Atlassian
• Salesforce
• Amazon
• Ericsson

July 22, 2026: Monday.com cuts 20% of its workforce to restructure for the AI era

The company says the decision to cut 620 jobs isn’t about margins, but about creating a flatter organization built around AI agents, autonomous teams, and deeper customer engagement.

July 6, 2026: Microsoft cuts 4,800 jobs, primarily in sales and Xbox teams

As the company trims thousands of jobs, it’s also investing in embedded engineering teams and AI infrastructure. The layoffs come several weeks after the company offered 8,750 US employees voluntary retirement buyouts.

June 5, 2026: Tech industry cut 38,242 jobs in May, worst since 2024

AI was blamed for 40% of the job cuts in May, up from 7% in January, according to research by employment placement company Challenger, Gray & Christmas.

May 20, 2026: Meta cuts 8,000 jobs, around 10% of workforce

The cuts are expected to expected to hit Meta’s engineering and product teams the hardest, arriving as Meta pivots toward AI to boost efficiency across its organization, according to Yahoo Tech.

May 13, 2026: Cisco to cut nearly 4,000 jobs despite strong growth in AI, enterprise networking

Despite reporting positive financial news — including record third-quarter revenue of $15.8 billion, a 12% year-over-year increase — Cisco said it will eliminate almost 4,000 jobs.

May 7, 2026: Cloudflare to cut 1,100 jobs in AI-focused restructuring

About 20% of Cloudflare’s global workforce will be culled as the company pivots for the agentic AI era, Reuters reported.

April 1, 2026: Oracle to cut up to 30,000 jobs globally, putting enterprise support and roadmaps at risk

Oracle began laying off employees on March 31 in what could be the largest workforce reduction in the company’s history. Employees received termination emails at 6 a.m. local time with immediate system lockouts and no prior warning. (Note: in June, CNBC put the final layoff tally at 21,000.)

March 12, 2026: Atlassian cuts 1,600 jobs to fund AI and enterprise expansion

Atlassian will reduce its global workforce by approximately 10%, eliminating around 1,600 roles, as the collaboration software maker redirects capital toward artificial intelligence development and enterprise sales.

March 11, 2026: Tech layoffs surpass 45,000 in early 2026

A recent analysis by RationalFX found 45,363 job cuts globally so far this year—with roughly 68% or more than 30,000 occurring in the U.S. — highlighting ongoing workforce cuts even as many tech companies report strong revenue growth.

February 10, 2026: Salesforce lays off staffers as executive leadership churn continues

Salesforce has reduced close to 1,000 roles earlier this month across teams, including marketing, product management, data analytics, and its Agentforce AI unit, Business Insider reported, quoting employees familiar with the matter.

January 23, 2026: Amazon layoffs expected to disproportionately hit AWS and tech talent

As the market slows down, AWS and other Amazon units are preparing for another round of layoffs, which is expected to overwhelmingly impact tech talent. An email from HR leader Beth Galetti on Jan. 28 confirmed 16,000 job cuts.

January 15, 2026: Ericsson plans to shed 1,600 jobs in Sweden

 Ericsson lans to cut some 1,600 jobs in Sweden, the telecommunications equipment maker said doubling down on recent cost-saving measures that have helped it weather a prolonged downturn in telecoms spending, Reuters reports.

January 13, 2026: Meta plans to cut around 10% of employees in Reality Labs business

Meta plans to cut around 10% of the employees in its Reality Labs division who work on products including the metaverse, according to three people with knowledge of the discussions, according to The New York Times.

Layoffs in 2025

• Cisco
• Oracle
• Windsurf
• Intel
• Microsoft
• Crowdstrike
• HPE
• Autodesk
• HPE
• CISA
• Workday
• Salesforce
• Meta

Global tech-sector layoffs surpass 244,000 in 2025

Economic uncertainty, elevated interest rates, and AI adoption have driven workforce reductions across tech companies worldwide, according to a RationalFX report.

October 28, 2025: Amazon to cut 14,000 jobs across company

Amazon will reduce its overall workforce by 14,000, cutting layers of management across the company and hiring in some areas to support its “biggest bets”.

August 18, 2025: Cisco and Oracle to cut hundreds of Bay Area jobs

Tech companies Cisco and Oracle are cutting hundreds of jobs across the Bay Area. Cisco will eliminate 221 positions at its Milpitas and San Francisco offices, effective Oct. 13. Oracle is reducing 101 positions in Santa Clara on the same date 

August 5, 2025: 3 weeks after acquiring Windsurf, Cognition offers staff the exit door

Cognition, the AI coding startup that acquired rival company Windsurf three weeks ago, laid off 30 employees last week and is offering buyouts to the roughly 200 remaining employees on the team, reports The Information.

July 25, 2025, Intel to lay off 22% of workforce, CEO Tan signals ‘no more blank checks’

Intel will reduce its workforce to 75,000 employees by the end of 2025 as new CEO Lip-Bu Tan implements sweeping changes designed to transform the struggling chipmaker

July 8, 2025, Intel layoffs begin: Chipmaker is cutting many thousands of jobs

Intel has begun laying off employees across the company. CEO Lip-Bu Tan told workers back in April to expect major layoffs at Intel in the coming months as the chipmaker slashes costs and overhauls its organization after years of technical setbacks and falling sales.

July 2, 2025: Microsoft will cut 9,000 workers

Microsoft will lay off about 9,000 employees, a source familiar with the workforce cut told CNBC.  The cuts will reportedly affect less than 4% of Microsoft’s global workforce and will impact different teams, geographies and levels of experience. This is the latest in a string of cuts the tech giant has made this year.

June 17, 2025: Intel looks to factory layoffs to return to profitability

Intel will lay off up to 20% of its manufacturing sector employees starting in July, according to media reports, as the company looks for options as it seeks a return to profitability. The cuts reportedly will be made around the world, but some of the layoffs will be closer to home, according to a report in The Oregonian citing an internal company memo from Intel manufacturing Vice President Naga Chandrasekaran.

May 7, 2025: CrowdStrike to lay off 5% of staff

CrowdStrike announced a plan to cut about 500 roles, roughly 5% of its workforce, to streamline operations and reduce costs. The cybersecurity company will incur about $36 million to $53 million in charges related to the layoffs

March 6, 2025: HPE cuts 2,500 jobs, remains committed to Juniper buy

CEO Antonio Neri told Wall Street analysts that HPE would begin implementing a cost-cutting program involving layoffs of about 2,500 employees over the next 18 months. HPE employs about 61,000 people worldwide.

Feb. 27, 2025: Autodesk to lay off 9% of workforce

Software maker Autodesk is laying off 1,350 staff. With the rise of subscription and multi-year contracts billed annually, and self-service enablement, it finds it needs fewer sales staff, CEO Andrew Anagnost said in a message to employees. And with its cloud, platform, and AI products proving most profitable, it’s concentrating its staff and investments there.

Feb. 27, 2025: HP to lay off 2,000 more

As part of an ongoing restructuring, HP plans to lay off up to another 2,000 workers. In recent weeks, the company has tried — unsuccessfully — to do away with telephone support staff by forcing callers to wait for at least 15 minutes if they refuse to use self-service support resources online. The company swiftly backtracked, but wider job cuts are still on.

Feb. 21, 2025: CISA lays off 130

Government employees get laid off too: In this case, 130 workers at the US Cybersecurity and Infrastructure Security Agency are being shown the door as a result of a DOGE decision. Cybersecurity experts are concerned that the cuts will harm the international collaborations that CISA has fostered, quite apart from their concerns about the security of the DOGE layoff process itself.

Feb. 5, 2025: Workday lays off 1,750

As it moves to invest more in AI and international growth, Workday is laying off 8.5% of its workforce and disposing of unused office space. Some analysts fear the cutbacks will affect the company’s customer service — unless AI can pick up the slack.

Feb. 4, 2025: Salesforce lays off over 1,000

At the same time as it’s hiring sales staff for its new artificial intelligence products, Salesforce is laying off over 1,000 workers across the company, according to Bloomberg. As of June, 2024, the company had over 72,000 employees, according to its website. Salesforce did not comment on the report. In 2024 the company reportedly laid off around 1,000 staff too, in two waves: January and July.

Jan. 14, 2025: Meta will lay off 5% of workforce

Mark Zuckerberg told Meta employees he intended to “move out the low performers faster” in an internal memo reported by Bloomberg. The memo announced that the company will lay off 5% of its staff, or around 3,600 staff, beginning Feb. 10. The company had already reduced its headcount by 5% in 2024 through natural attrition, the memo said. Among those leaving the company will be staff previously responsible for fact checking of posts on its social media platforms in the US, as the company begins relying on its users to police content.

Tech layoffs in 2024

• Equinix
• AMD
• Freshworks
• Cisco
• General Motors
• Intel
• OpenText
• Microsoft
• AWS
• Dell

Nov. 26, 2024: Equinix to cut 3% of staff

Despite intense demand for its data center capacity, Equinix is planning to lay off 3% of its workforce, or around 400 employees. The announcement followed the appointment of Adaire Fox-Martin to replace Charles Meyers as CEO and the departures of two other senior executives, CIO Milind Wagle and CISO Michael Montoya.

Nov. 13, 2024: AMD to cut 4% of workforce

AMD will lay off around 1,000 employees as it pivots towards developing AI-focused chips, it said. The move came as a surprise to staff, as the company also reported strong quarterly earnings.

Nov. 7, 2024: Freshworks lays off 660

Enterprise software vendor Freshworks laid off around 660 staff, or around 13% of its headcount, despite reporting increased revenue and profits in its fourth fiscal quarter. The company described the layoffs as a realignment of its global workforce.

Sept. 17, 2024: Cisco lays off 6,000

After laying off around 4,200 staff in February, Cisco is at it again, laying off another 6,000 or around 7% of its workforce. Among the divisions affected were its threat intelligence unit, Talos Security.

Aug. 20, 2024: General Motors lays off 1,000 software staff

More than 1,000 software and services staff are on the way out at General Motors, signalling that it could be rethinking its digital transformation strategy. In an internal memo, the company said that it was moving resources to its highest-priority work and flattening hierarchies.

August 1, 2024: Intel removes 15,000 roles

Intel plans to cut its workforce by around 15% to reduce costs after a disastrous second quarter. Revenue for the three months to June 29 stagnated at around $12.8 billion, but net income fell 85% to $83 million, prompting CEO Pat Gelsinger to bring forward a company-wide meeting in order to announce that 15,000 staff would lose their jobs. “This is an incredibly hard day for Intel as we are making some of the most consequential changes in our company’s history,” Gelsinger wrote in an email to staff, continuing: “Our revenues have not grown as expected — and we’ve yet to fully benefit from powerful trends, like AI. Our costs are too high, our margins are too low. We need bolder actions to address both — particularly given our financial results and outlook for the second half of 2024, which is tougher than previously expected.”

July 4, 2024: OpenText to lay off 1,200

OpenText said it will lay off 1,200 staff, or about 1.7% of its workforce, in a bid to save around $100 million annually. It plans to hire new sales and engineering staff in other areas in 2025, it said.

June 4, 2024: Microsoft lays off staff in Azure division

Microsoft laid off staff in several teams supporting its cloud services, including Azure for Operations and Mission Engineering. The company didn’t say exactly how many staff were leaving.

April 4, 2024: Amazon downsizes AWS in a fresh cost-cutting round

Amazon announced hundreds of layoffs in the sales and marketing teams of its AWS cloud services division — and also in the technology development teams for its physical retail stores, as it stepped back from efforts to generalize the “Just Walk Out” technology built for its Amazon Fresh grocery stores.

April 1, 2024: Dell acknowledges 13,000 job cuts

Dell Technologies’ latest 10K filing with the US Securities and Exchange Commission disclosed that the company had laid off 13,000 employees over the course of the 2023 fiscal year; it characterized the layoffs and other reorganizational moves as cost-cutting measures. “These actions resulted in a reduction in our overall headcount,” the company said. A comparison to the previous year’s 10K filing, performed by The Register, found that Dell employed 133,000 people at that point, compared to 120,000 as of February 2024. Dell announced layoffs of 6,650 staffers on Feb. 6, but it is unclear whether those cuts were reflected in the numbers from this year’s 10K statement.

See news of earlier layoffs.

GitLab has released GitLab 19.2, an update to the company’s devsecops platform that allows teams to fix vulnerable dependencies automatically, use Security Review Flow to catch logic flaws that scanners miss, and run AI agents straight from the terminal, the company said.

Highlights in GitLab 19.2 include the following:

• Dependency Scanning Auto-Remediation, in public beta, uses AI to fix build-breaking changes and iterates until your pipeline passes, with every change governed by your existing gates and audit trail.
• Security Review Flow, also in public beta, analyzes code changes as a security engineer would and catches authorization gaps, business-logic errors, and race conditions that static scanners structurally cannot see.
• GitLab Duo CLI, now generally available, gives developers access to agents and multi-step agentic flows for all software life cycle tasks without leaving the terminal. 
• Custom Flows, now generally available, let teams replace manual multi-step workflows with agentic automations for software development, triggered by GitLab events.

“Coding agents made it possible to generate far more code and moved the bottleneck downstream to reviews and security,” said Manav Khurana, chief product and marketing officer at GitLab, in a statement. “GitLab 19.2 puts agents to work on that bottleneck: fixing vulnerable dependencies, catching the flaws scanners miss, and automating the steps in between with a person still approving what ships.”

Outsourcing worked – until it didn’t.

After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution.

The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business growth. Product quality expectations increased. Infrastructure and security requirements became stricter. Investors started asking difficult but fair questions about IP ownership, operational dependencies and long-term scalability.

Most importantly, engineering execution was no longer just an operational function – it became part of the company’s strategic advantage. That was the moment when the founders decided the company needed dedicated technology leadership to address these challenges. This is how I joined the company at the beginning of 2023. As VP of Engineering and a bit later as CTO, I led the transformation (usually known as insourcing, repatriating or backsourcing) from an outsourced model to an internal engineering organization while maintaining product delivery continuity and preparing the company for the next growth stage. The process took roughly a year and involved not only technical migration, but also organizational design, hiring, process development, infrastructure modernization and cultural transformation – everything from the ground up.

Building an internal engineering organization while still delivering

One of the biggest misconceptions about insourcing is that it is primarily a technical project. It is a leadership and execution challenge.

When I joined the company, there was effectively no internal engineering structure, limited visibility into the existing system and no clear long-term technical strategy. My first months were dedicated to understanding reality and I began with a comprehensive assessment of the codebase, operational risks, documentation quality and knowledge dependencies to determine the most viable transition strategy.

Very early in the process, I faced a critical strategic decision: whether to gradually assume ownership of the existing platform or rebuild it internally. To make that decision, I evaluated four distinct transition models ranging from limited management insourcing to a complete internal rebuild.

After assessing the technical, operational and long-term business implications of each approach, I selected the most demanding option: rebuilding the product internally while maintaining uninterrupted delivery for existing customers. Although riskier in the short term, a full rebuild offered the clearest route to complete IP ownership, architectural flexibility and long-term scalability.

At the time, this decision ran counter to the approach typically taken by startups in similar situations. Most organizations gradually assume ownership of an existing codebase to minimize short-term risk and preserve delivery capacity. My assessment was that the accumulated architectural debt, fragmented knowledge distribution and long-term maintenance risks would ultimately make a phased takeover more expensive and less scalable than a controlled rebuild. The strategy required significantly higher execution discipline, but it allowed us to establish complete ownership of the platform, eliminate inherited constraints and create an architecture capable of supporting enterprise-scale growth.

The next challenge was hiring.

In Germany, hiring can easily take four to six months – mostly due to a typical 3-month notice period, which is incompatible with startup timelines. We solved this by building a hybrid organization structure early: a lean internal core team combined with carefully selected contractors. Instead of hiring only narrow specialists, we prioritized experienced generalists capable of operating across architecture, infrastructure, security and compliance discussions. Later, we evolved toward a product engineering model, where engineers owned broader product outcomes rather than narrowly defined technical functions.

During the first three months, we established a core engineering team of four senior engineers. Over the following nine months, the organization expanded to roughly fifteen engineers while I strategically designed and executed the transformation of the platform’s architecture to meet the rigorous deployment and compliance standards of our first enterprise clients, including Raiffeisen Bank International and Bertelsmann. This structural overhaul allowed the company to meet the deployment, security and compliance requirements of enterprise customers that had previously been inaccessible under the outsourced model. At that point, we had already achieved complete coverage across backend, frontend, DevOps, QA and security.

I also intentionally kept processes lightweight during the transition. Instead of introducing heavyweight frameworks, we focused on clarity of priorities, fast decision-making and execution discipline. We used Kanban over Scrum, eliminated unnecessary meetings, shortened the remaining ones and emphasized engineering culture over process overhead.

Another major challenge was project estimation. Because dual-track development was unavoidable until the in-house platform reached production readiness, estimation accuracy had a direct impact on budget efficiency. Despite all challenges, my initial estimate ultimately proved remarkably close to the final delivery date, differing by only about a week. Accurate forecasting under conditions of parallel development streams, ongoing customer commitments and active team formation became a critical leadership challenge. Maintaining this level of predictability throughout the transition helped align engineering execution with business planning, hiring decisions and investor expectations.

The engineering transformation enabled capabilities that contributed to Akirolabs being recognized as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024, Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025 and inclusion in 2024-2026 in ProcureTech100 annual recognition of procurement technology providers shaping the future of digital procurement.

Managing risk without slowing down the business

The hardest part of insourcing is not writing code, selecting the technology stack, designing architecture or configuring infrastructure. It is avoiding disruption while the company is changing underneath the product. I successfully orchestrated the concurrent overhaul of product architecture, cross-functional engineering recruitment, infrastructure modernization and live customer operations under exceptionally tight margins.

To reduce delivery risk, we approached the transition in layers.

First, we focused on infrastructure reliability and operational readiness before feature expansion. Cloud architecture, recovery testing, permission segregation and incident management processes were implemented early, not after launch. We also introduced multiple testing stages and dedicated QA functions after learning the hard way that a “developers-only” quality control approach does not scale for complex web platforms and business domains.

Second, we established a structured knowledge-transfer process to rapidly onboard engineers and reduce external dependencies.

Third, we became extremely disciplined about scope management. One of the most common reasons insourcing initiatives fail is uncontrolled change during the rebuild phase. Every new feature request increases uncertainty non-linearly. We learned to separate strategic improvements from distractions and protect the core delivery roadmap aggressively. Throughout the transition, we successfully maintained uninterrupted customer operations by utilizing planned maintenance windows, achieved a near-zero-downtime migration and permanently doubled product velocity immediately following the migration.

Beyond the technical migration itself, the transition established a repeatable operating model for scaling technology organizations beyond the product-market-fit stage. The framework combined organizational redesign, controlled knowledge repatriation, architecture modernization and enterprise-grade operational practices while maintaining uninterrupted customer delivery throughout the transformation. While the implementation was specific to Akirolabs, the underlying principles are broadly applicable to organizations seeking to transition from outsourced development to internal product ownership without disrupting business operations.

By the time the new platform reached production readiness, I had established not only a functioning engineering organization, but also a stable operational model: internal ownership, production-grade infrastructure, security processes, scalable hiring practices and clear technology and product roadmaps.

A positive side effect of the transition was the creation of internal UI/UX and Data Science capabilities, which later became strategically important for AI product initiatives and created a foundation for the third version of the product, which we released in mid-2025.

My technical restructuring and migration to a secure proprietary platform reduced architectural risk, established full in-house ownership and helped strengthen investor confidence during the company’s successful €5M fundraising round in 2024.

The transition created a stronger foundation for scale and supported the company’s continued expansion among enterprise organizations operating at Fortune 500 scale, including Ahold Delhaize, Workday, IFF, Deutsche Bahn and others.

Lessons learned for CTOs considering insourcing

Looking back, several decisions made the transition successful, and several mistakes made it harder than necessary.

The first lesson is simple: decisiveness in strategic transition is paramount to maintaining business momentum. Rapidly evaluating insourcing frameworks and defining clear boundaries with the external partner allowed us to mitigate operational downtime and execute a highly efficient migration ahead of critical market deadlines.

Second, hire more senior people and do it as early as possible. Strong technical leaders multiply execution capacity far beyond their individual contribution. In our case, the quality of the first hires influenced architecture quality, hiring standards, delivery discipline and engineering culture for the entire organization.

Finally, culture matters more than frameworks. Processes can be added later. Ownership mentality cannot.

The biggest long-term advantage of bringing development in-house was not simply faster execution, not better code quality or operational cost optimization by over 30% after the transition which we also achieved. It was an alignment. Product strategy, engineering decisions, customer priorities and business goals became part of the same conversation instead of being separated by organizational boundaries. For technology companies operating in highly competitive markets, that alignment becomes a compounding advantage over time.

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Site reliability engineers (SREs) have the tough assignment of resolving thorny performance and reliability issues. But their primary mission is to provide devops teams with operational insights and to suggest implementation improvements on business system performance, security, and overall robustness.

Google introduced its SRE playbook in 2003, but it took some time for the role’s definition, tools, and techniques to become mainstream. Startups were the first to adopt observability for cloud-native applications and create dedicated SRE positions. As tools matured and SRE responsibilities became more clearly defined, larger enterprises assigned SREs to work as a bridge between devops and IT ops teams to improve resilience across a wider range of applications, APIs, and data pipelines.

SRE is a career path for multidisciplinary engineers with strong investigative instincts, sharp data analytics skills, and the temperament to perform under pressure. It has become a critical responsibility as tech became mission-critical for enterprises, and it is a growing role in the genAI era as more businesses deploy AI agents.

But the critical need for resiliency and greater technological complexity brings new challenges for SREs. According to the 2026 State of Production Reliability and AI Adoption report, 44% of respondents experienced an outage linked to ignored or suppressed alerts in the past year, and 35% report their engineers occasionally ignore or dismiss alerts due to alert fatigue. More than 70% of alerts received are not actionable, according to 57% of organizations.

So, is AI making the SRE’s role easier and helping businesses run more reliable technology operations? On the other hand, AI is also driving complexity, as companies deploy genAI tools and AI agents across more business functions and seek to automate more decision-making across operations.

AIops and agentic ops aid SREs

Over the past decade, SRE responsibilities have become somewhat easier through improvements in monitoring platforms, observability practices, tools for centralizing operational data, and AI applied in IT operations (AIops). But during the heat of resolving an outage or performance issue, it’s not easy to correctly identify what system triggered the issue versus other downstream systems impacted by it.

According to the Komodore 2025 Enterprise Kubernetes Report, 79% of production incidents originate from recent system changes, including deployments and changes to compute environments. But the other 21% of incidents stem from issues outside of the business’s control, including network failures, third-party changes, and cloud provider failures.

“SREs using AI capabilities succeed or fail in the moment an incident unfolds, when engineers are deciding what to investigate next,” says Itiel Shwartz, CTO at Komodor. “If the system streamlines root cause detection, connects signals to recent changes, and explains its reasoning in a way engineers recognize, it earns trust. If it adds uncertainty or demands extra validation, it gets sidelined, regardless of how bespoke the model behind it may be. What’s less obvious is what it takes to make AI for SREs work in production, and how different that reality is from prototypes, demos, or early internal builds.”

AIops is not a new capability, especially in using machine learning to correlate logs, metrics, and traces across monitoring and alerting systems. IT service management and SREs have been using AIops to reduce the mean time to resolve incidents and to perform accurate root cause analysis (RCA) efficiently. Agentic ops is the next wave of genAI operational capabilities, including tools for monitoring AI agents, managing their access rights, and detecting AI model accuracy drift.

 “AI is useful during major incidents because it can pull together a lot of context into a few clear sentences, which is exactly what an SRE needs in the moment,” suggests Shani Shoham, chief revenue officer at OpenObserve. “The complexity of architecture and the different tooling make it easier for AI than for a human, but autonomous resolution is still a way off.”

AI’s impact on people and burnout

The business pressure to keep systems up, secure, and performing well is a 24/7 stressful responsibility. According to The SRE Report 2025 from Catchpoint, 36% of SREs often or always experience elevated stress during an incident, and 28% said the stress persists even after the incident is resolved. AI capabilities may prove to be a game-changer in helping SREs avoid burnout and reduce stress.

“AI can improve RCA by taking in a much larger incident context than any engineer can hold at 3am, reasoning across traces, logs, metrics, deploys, config changes, alerts, ownership, and recent production behavior,” says Noam Levy, founding engineer and field CTO at Groundcover. “Beyond attempting a full RCA, its immediate value is distilling the signals that actually matter, reconstructing a clear timeline of cause and effect, and helping engineers separate correlation from likely causality. Once a fix is deployed, agents can also verify remediation by comparing pre- and post-fix behavior, but this depends on broad access to rich, correlated production signals and a cost model that does not discourage adoption or experimentation.”

Not only are incidents resolved faster and with less stress, but AI can also free up SRE time to focus on proactive work and create a career path for junior developers into SRE roles. Quais Taraki, CTO at EDB Postgres AI, adds, “AI reduces toil by automating repetitive tasks while accelerating incident resolution through copilots that correlate signals across distributed systems, allowing SREs to focus more on resilience strategies like chaos engineering and failure analysis.”

AI can have long-lasting operational impacts, especially for organizations looking to deploy more mission-critical technology and AI capabilities. Two longer-term benefits of AI for SREs are reducing the number of bridge calls needed for incident response and the number of engineers required in “war rooms” to coordinate root cause analyses.

“When something goes wrong, AI that guides SREs can do the full analysis, get to the root cause, and perform the remediation,” says Spiros Xanthos, founder and CEO of Resolve AI. “AI also helps avoid many escalations, and when escalations are needed, it targets the right people from the network, infrastructure, and the application teams. AI for SREs centralizes operational intelligence, exposes tribal knowledge, and can guide more junior developers.” 

AI agent reliability

While AI capabilities have been a net positive in helping SREs improve system reliability, the growth of AI code generators, vibe coding, and spec-driven development is adding to their workloads. According to one study, 41% of all global code is now AI-generated, and Gartner predicts that 40% of new enterprise production software will be created using vibe coding techniques by 2028.

But coding velocity is creating new issues for SREs as AI pull requests have 1.4 times more critical issues and 1.7 times more major issues, according to CodeRabbit. “AI-assisted development has created an unprecedented velocity of code reaching production, expanding surface area, edge cases, and failure rates faster than traditional SRE practices can absorb,” says Vinod Jayaraman, cofounder and CTO at NeuBird AI. “The speed of shipping has far outpaced the speed of understanding what breaks in production. To close this loop, SREs need enterprise agents that can capture precise diagnostic context, including correlated traces, service dependencies, and anomaly timelines, and structure it as actionable input for the engineers and AI coding tools responsible for the fix.”

The growing number of AI agents deployed to production creates new challenges. AI agents are not just code; they have multiple failure points. They are built using language models, connect to proprietary sources for context, and integrate with Model Context Protocol servers to support more complex workflows. Changes are ongoing and not deployment events, so the SRE’s job of identifying the source of performance and accuracy drifts isn’t trivial. 

“Traditional SRE was built for systems that fail in reproducible ways, but agents fail differently and drift when a model provider pushes an update, and behavior shifts silently with no baseline for comparison,” says Mohammed Aboul-Magd, vice president of product at SandboxAQ. “Most organizations can’t even answer the basics: how many agents are running, what they have access to, and whether they’re still doing what they were built to do.”

“Every time a senior engineer leaves, they take years of learned failure patterns with them, and the next outage starts from square one,” adds Ronak Desai, cofounder and CEO at Ciroos. “Using AI for compounding operational memory changes that, and every incident your system resolves, the AI learns it.”

SREs should take a leadership role in emerging best practices, including defining their standards for AI agent non-functional acceptance criteria, observability practices, and release-readiness criteria. SREs should update their service-level objectives (SLOs) and define error budgets for AI agents in production.

Ryan Downing, vice president and CIO of enterprise business solutions at Principal Financial Group, says, “Standard SLOs and error budgets give teams the guardrails, and AI helps interpret the telemetry against those targets, reducing noise so engineers can get to the real issue faster and automate parts of remediation before customers are impacted.”

AI raises the SRE’s business impact

The more dramatic shift in site reliability engineering is an evolution of its business scope. IT leaders focus on uptime, performance, and issue resolution, as well as understanding their impacts. Business leaders will look to IT and SREs to identify, determine root cause, and remediate a broader class of issues, including rogue AI agents and the impacts of rapidly deploying new agentic capabilities. 

“AI agents are handing SREs categories of problems they’ve never had to solve before, specifically failures defined in business terms, not technical ones,” says Blake Sherwood, distinguished technologist for AI and platform strategy at Smarsh. “Traditional reliability engineering is built around latency, errors, and crashes, but agents now fail due to skipped compliance steps or outcomes that looked fine technically but were wrong contextually. Most SRE teams aren’t wired for that yet.”

The question is whether SREs with AI-augmented tools can keep up with the velocity, complexity, and business urgency of deploying new AI business capabilities.

Google DeepMind CEO Demis Hassabis is pushing for the US AI industry to self-regulate, with the support of government, as a starting point for an international creating shared international standards. In a blog post, he called for a focus on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis proposed that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants be encouraged to adopt best practices such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. 

DeepMind was involved in an earlier US government initiative evaluating AI safety, alongside Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

Google DeepMind CEO Demis Hassabis is pushing for the US AI industry to self-regulate, with the support of government, as a starting point for an international creating shared international standards. In a blog post, he called for a focus on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis proposed that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants be encouraged to adopt best practices such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. 

DeepMind was involved in an earlier US government initiative evaluating AI safety, alongside Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

This article first appeared on CIO.

With AI increasingly tucked into every cranny of the enterprise, someone has had to step up and provide the tools necessary to discover, track, and monitor all the agents and LLMs and keep them humming along in their various workflows. Thankfully, the DevOps world answered the call, building the tools to support our new overlords in an emerging subdiscipline interchangeably called “AIOps,” “AgentOps,” and sometimes “agent observability.”

Many of the challenges involved in AgentOps are similar to those tackled by traditional DevOps tools and processes. After all, at their foundation, LLMs are just software running on hardware somewhere. Typical issues involving RAM and disk space are just as important in the agent world, maybe more so because AI operations are even more greedy about consuming storage than regular software is.

Many of the companies supporting agent observability are big names in DevOps circles, having adapted their stacks to address the idiosyncrasies of modern LLMs. IT teams maintaining enterprise agents can treat the LLMs as just one node in a big graph filled with services that are constantly swapping packets and triggering software jobs. Latency and resource constraints must be managed because end-users don’t care whether it’s an LLM, a database, or a plain-old Python script that’s failing, bringing their work to a grinding halt.

But new AI-specific challenges are opening the door to newcomers that are building tools with the peculiarities of LLMs in mind — for example, keeping deeper logs filled with records of prompts. LLMs are also often very non-deterministic by design, making it trickier to pinpoint failure modes. And then there’s the fact that an agent will give a perfectly intelligent answer one minute and hallucinate the next.

Relying on many of the same approaches that DevOps tools do, AgentOps tools watch for misbehavior and flag anything out of the ordinary for deeper analysis. This may be as simple as fixing slow responses, but it can also include AI hallucinations and other issues born of LLMs’ non-determanism.

Teams trying to choose which agent observability tools is best for their use case should look at the size and nature of their agentic systems and projects. Are they adding AI agent features to an existing product or application, or are they building agentic systems from scratch? Are they more focused on maintaining a stable LLM operation or iterating on new approaches? Is AI the center of attention or just an add-on that’s meant to improve an existing stack?

The AgentOps and agent observability options listed below share many of the same features but differ in their focus and their attention to the challenges organizations will encounter when incorporating agents into their stacks. Each tool offers a worthwhile place to start understanding how to care for the growing presence of AI in the production world.

AgentOps.ai

When teams of agents work together, tracking the conversations are essential for understanding and debugging what’s happening. The SDK from AgentOps.ai records events so that the creators can replay past behavior to track details such as token counts, spending, latency, and more. Available as a service and on-premises.

Pricing: Starts at $40 per month plus usage costs at $0.20 per 1M tokens

Standout feature: Replay analytics with “time-travel debugging”

Best suited for: Complex agent debugging

Arize Phoenix

Debugging prompts and LLM responses requires a nuanced understanding of just what’s happening, in part because of the non-determinism that often enters the process. Phoenix from Arize supports this process with robust tracing and the ability to score the results for more precise iteration. Their system can track the results and tool calls from a variety of major platforms (Anthropic, AWS, OpenAI, etc.) that are initiated by the major frameworks (LangChain, LlamaIndex, DSPy, etc.). The result is insight into what data is triggering what chain of responses.

Pricing: Small free tier; Pro plan starts at $50 per month plus costs tied to events

Standout feature: LLM-as-a-Judge metrics for tracking quality

Best suited for: Teams focusing on iterating for accuracy and quality

BigPanda

BigPanda has always offered solutions for tracking performance of complex systems. Now the company is drilling deeper into the challenge of detecting and ending the problems that come from models that go awry. BigPanda’s main system relies on historical data and machine learning algorithms to flag issues. Its own agent layer connects the problematic nodes and errant models while dispatching alerts to the right team members.

Pricing: “Value-based” table on request

Standout feature: Automated triage for faster response

Best suited for: Large teams seeking to reduce alert fatigue from large customer base

Braintrust

Setting up an effective improvement cycle for an AI agent requires a strong feedback loop from production data to the agent’s next generation. Braintrust watches the production workload and creates test vectors that expose how an agent may be drifting, regressing, or departing from its path. The tool automates much of the testing and scoring feedback loop so problematic patterns can be discovered and addressed. A core part of the offering is a specialized data store that can track large and sometimes deeply nested collections of tests and their results. Their approach may be summarized by one of their tag lines: “trace everything.”

Pricing: Free starter tier; Pro plan starts at $249 with some usage-based costs covered

Standout feature: Highly scalable trace ingestion

Best for: Teams developing strong guardrails through continuous testing

Chronicle Labs

When it’s time to release a new version of an agent into the wild, the platform from Chronicle Labs specializes in staging it and testing it with a collection of use tests and regression cases. The tools are also helpful during development cycles. “Backtest your agent against reality,” their sales material promises, with a set of tools that mines the production telemetry for solid test vectors that stress every part of the agent with prompts and challenges that the agent will encounter after leaving the safety of the lab.

Pricing: On request

Standout feature: Back-testing options for complex testing regimes

Best for: Teams chasing strong models with good fidelity to reality

Comet Opik

Building a dashboard for tracking every in-flow and out-flow to agents is one way to be ready to watch for and solve problems. Opik from Comet is just such a tool. The DevOps teams can track each call and add its own automated routines to examine the results, score them based on 30-plus metrics, and if desired, send it off to another LLM to evaluate the results. Agents that are constantly failing stand out. DevOps teams can also ask questions like, “Who is using this model and racking up all of the bills?” The same goes for MCP skills and other cogs in the machine.

Pricing: Free tiers for open source and small projects; Pro plan starts at $19 per month with usage limits

Standout feature: Auto-scoring with 30-plus metrics for evaluating traces

Best for: Teams focusing on RAG and agentic workflows

Datadog

DevOps teams that rely on Datadog to track logs across collections of services can also use it to track LLM operations, which are, of course, just another source and sink for data. It will track performance such as time to first token and offer insight into what might be causing an issue, such as lack of memory. Results then get plugged into the same cost-tracking mechanism so the bean counters can predict when the budget will run out. After all, the CFO likely doesn’t care whether the bill comes from an LLM or an old-school S3 storage bucket. Datadog integrates AI into their tools by treating these models as just another source of data.

Pricing: Small free tier with multiple paid tiers for various levels of enterprise monitoring

Standout feature: Large installed base with broad focus on more than LLMs

Best for: Large enterprise teams working with established infrastructure

Dynatrace

For more than 20 years, Dynatrace has been delivering tools that track dataflows across the full stack. Now that AIs are finding roles in many of the nodes in this complex graph, they’re expanding to track how various AI agents can interact. They want to build one platform that helps track the root cause and, often now, deploy solutions autonomously. They want to focus on being ready to support complex networks of agents that detect problems in either performance or security and then work within defined guardrails to fix them. Determining the right role for their own AI-powered agents is a key part of the product.

Pricing: Plans start at $7 per month with larger plans designed for full enterprise monitoring

Standout feature: High level of autonomous monitoring designed for large installations

Best for: Complex, hybrid environments mixing LLMs with traditional services

Galileo

Placing some AI systems into production is often a harrowing experience because the actual performance is impossible to predict, even with the most rigorous tests. Galileo offers guardrails that track performance and watch for any behavior that deviates from the ground truth. Their “LLM-as-judge” systems are distilled into compact models that can be run locally for lower costs and faster performance.

Pricing: Small free tier; Pro plans start at $50 per month with usage-based limits and costs

Standout feature: Real-time guardrails for deployed agents

Best for: Security-conscious installations that need to defend against hallucination and data leakage

Grafana Labs

Long the go-to source for open source telemetry, Grafana Labs now tracks performance of AI models in constellations of services. Grafana tracks the evolution of answers across the agentic network to recognize how small changes or hallucinations can spin out of control. It bills its system as “actually useful AI” and has even trademarked it. Its cloud assistant can configure and reconfigure the Grafana dash to offer the right level of observability. Its system includes AI-level analysis that can flag models that are responding quickly but offering bad answers because of problems such as model drift or context degradation.

Pricing: Basic free tier; Pro plan begins at $19 per month, includes better retention and some usage-based fees 

Standout feature: Full-stack tool with fully integrated LLM tools

Best for: Large, enterprise-scale system adding AI

Helicone

Sometimes shoehorning in another tool into the chain can be tricky. Helicone is designed as a smart network proxy that will route all model requests while keeping solid debugging records from the data as it goes by. The data it captures can be turned into nice charts that make it easy to spot latency issues or model failures. Naturally, tracking AI spend is also a feature in much demand as bills continue to climb.

Pricing: Small free tier; Pro plan starts at $79 per month, includes features such as team collaboration and improved querying

Standout feature: Proxy-based integration

Best for: Development teams who want to add better monitoring features quickly

Laminar

Tracking agents in development and production means building strong storehouses of data enumerating what happened. Laminar works closely with OpenTelemetry to follow agents operating in production so that flaws and failure modes can be understood from log files stored efficiently with their own compression scheme. Developers can search through traces with an SQL-ish language and Laminar’s transcript view illuminates what happened. When necessary, the traces can enable developers to scroll back in time and replay the same inputs for debugging. The goal is to offer deep insights with high-level visibility of how well the agents are meeting business objectives.

Pricing: Small free tier; “Hobby” tier that adds more features at $30; Pro level starts at $150 per month

Standout feature: Open-source license makes self-hosting a viable option

Best for: Teams fully able to leverage open-source responsibilities

LangChain LangSmith

Real-time data from agents is essential for managing any mutli-agent system in production. LangSmith from LangChain traces costs, tools, and progress toward solutions for a wide collection of agents using SDKs for Python, TypeScript, Go, and Java. The OpenTelemetry-based solution watches for anomalies, issuing warnings and alerts through dashboards and communication channels such as PagerDuty. Deeper analysis can reveal issues such as topic clustering or odd patterns of failure. Coordination with agent deployment platforms such as LangGraph and deepagents ensures greater focus on successful resolution of assignments.

Pricing: Free for solo developers; Pro teams start at $39 per person per month 

Standout feature: Systematic approach to regression testing of prompts

Best for: Teams relying on LangChain and LangGraph frameworks for supporting complex agentic behavior

Lunary

Watching the user experience is essential for building AI applications such as chatbots and assistants. Lunary offers a proxy that traces all interactions and then builds analytical dashboards for measuring metrics such as user satisfaction or model costs. One common usage is finding frequent topics and looking at the responses to ensure they deliver. When prompts aren’t perfect, Lunary lets teams iterate on the prompt text until the right answers are coming out. Its proxy structure and common API format enables Lunary to promise to work with “any LLM, any framework.”

Pricing: Free tier; Pro plan starts at $20 per month

Standout feature: Deep integration with humans for reviewing and optimizing results

Best for: Startups focused on rapid prompt innovation

NewRelic

The platform that began tracking performance of some web applications is now powerful enough to track the flows of data through complex agentic ecologies. NewRelic’s AI-driven monitoring watches for golden signals that can indicate misbehavior or worse throughout the entire lifecycle. It tracks every detail of the interactions through protocols such as MCP and then makes this available to the AI engineers responsible for performance. The dashboard provides the insights necessary to watch for toxic behavior, overt bias, drift, and overblown hallucinations. Predicting and maybe even controlling the cost is also a growing role as tokenomics becomes as important as response time.

Pricing: Free tier; Pro plan fees available through website

Standout feature: Full-stack support with hundreds of integrations with other tools

Best for: Established enterprise teams mixing in AI

Nova AI Ops

The goal of Nova AI Ops is to deliver a team of agents that watch over a cloud and make it, at least partially, self-healing. Each agent uses a mixture of predictive AI and machine learning to watch cloud telemetry reports for anomalies. Then they calculate the “blast radius” and decide whether this is a problem that can be fixed automatically “while you sleep” or saved for the human supervisors. These tools are aimed not just on LLM operations but on the stack as a whole.

Pricing: Small free tier; Standard pricing  begins at $40 per user per month with usage billing

Standout feature: Focus on software reliability engineering helps teams deliver stable stacks

Best for: Teams that want to integrate LLMs into incident response and stability management

Splunk

The platform that began delivering smart logging is now fully AI capable, offering solutions that can watch over agents with much the same way that it continues to track microservices. Splunk now includes a fairly large amount of predictive AI for learning from the information in the logs and then turning this learning into fast solutions. This AI assistant can track deployed AI models connected by protocols such as MCP and watch over behavior while delivering the ability for users to drill down and explore what’s working and what’s failing. Their AI Canvas is meant to offer a central hub where the AI scientists can track both the local behavior of the models as well as their role in a larger data ecosystem.

Pricing: Activity-based pricing tracks usage of LLM backends and storage

Standout feature: Ready to scale to large enterprise stacks

Best for: Teams with legacy systems that are folding in agentic options

SuperPenguin

One of the most important parts of an AI service is the bill. SuperPenguin is a product designed to track consumption and make predictions so that the CFO won’t be surprised. The goal is to provide solid estimates about the total cost of each product by allocating costs to customers, features, and teams. If there’s a sudden shift, a “spike detector” will raise an alarm so that dev teams can ensure that the AI spend is worth it.

Pricing: Small free tier for experimentation; Growth tier for teams, starting at $30 per month; Pro tier offers deeper options starting at $200 per month

Standout feature: Strong accounting with invoice reconciliation and PR-level usage tracking

Best for: Teams that need precise cost accounting

Vellum

Prompt engineers spend time fussing over the details of tweaking, improving, and enhancing the words that guide the LLM. Vellum started as a company that would provide the pipeline so that you could manage and improve the prompts that ran again and again. Now the system is growing more powerful, offering a higher level of automation that lets you meta-manage the prompt chain. They’ve also begun marketing it as a form of personal assistant with pre-built connections to many of the major services such as Gmail. Its llm-cost-optimizer can juggle multiple options while finding a cheaper way to execute a prompt, a process the company suggests can save 60% or more.

Pricing: Open-source free tier; Pro plan starts at $35 per month

Standout feature: Focus on multi-model pipelines for true agentic solutions

Best for: Product teams with complex prompt engineering workflows

Data Products tragen dazu bei, die Art und Weise zu standardisieren, wie Rohdaten, Data-Warehouse-, sowie logische Data-Lake-Ansichten kombiniert und genutzt werden, um Analyse- und KI-Funktionen bereitzustellen. Indem sie Datenprodukte entwickeln, können Teams in Unternehmen einen Großteil der im Vorfeld erforderlichen Daten-Pipelines sowie Governance- und Management-Tasks optimieren. Darüber hinaus gewährleisten diese auch, dass Mensch und KI auf vertrauenswürdige Datenressourcen zugreifen.  

Kochen bietet an dieser Stelle eine hilfreiche Analogie: Sie könnten sich dazu entscheiden, für Ihr Lieblingsgericht ausschließlich auf frische Zutaten zu setzen. Dieser Ansatz funktioniert gut, wenn Sie sowohl die Zeit als auch die nötigen Fähigkeiten dafür mitbringen. Wenn nicht, setzen Sie eventuell lieber auf Convenience-Bestandteile – insbesondere unter Zeitdruck. Datenprodukte bieten eine vergleichbare Zeitersparnis – Analytics- und KI-Funktionen bauen in diesem Fall auf konsistenten, (vor)optimierten „Zutaten“ auf.

Die folgenden fünf Tipps sollten Sie bei Ihrer Data-Product-Initiative unbedingt verinnerlichen.

1. Data Products strategisch nutzen

Die meisten Unternehmen können es sich nicht leisten, für jede Datenvisualisierung, jedes Machine-Learning-Modell oder jeden KI-Agenten eigene Datenprodukte zu entwickeln. Schließlich ist das mit Kosten und Zeitaufwand verbunden. Dazu kommt: Sobald ein Data Product bereitgestellt ist, müssen die Produktmanager für fortlaufenden Support und ein entsprechendes Lifecycle-Management sorgen. Die erste entscheidende Frage ist also, in welchen Fällen es für agile Daten-Teams Sinn macht, Datenprodukte zu entwickeln – und wie dabei priorisiert werden sollte.   

Ein Ansatzpunkt besteht darin, das Data Product auf einen einzelnen Datensatz herunterzubrechen und sich zu überlegen, was es bedeutet, diesen zum Produkt zu machen. Danielle Ben-Gera, Vice President of Engineering bei Crunchbase, erklärt: „Ein Datensatz sollte erst dann zu einem Datenprodukt werden, wenn sich mehrere Teams bei Entscheidungen – oder zum Support von Anwendungen – darauf verlassen.“

Dabei seien eine angemessene Governance, klare Zuständigkeiten, Versionierungen und ein kontrollierter Lebenszyklus für Änderungen essenziell, warnt die Managerin: „Ansonsten liefert man nur instabile Pipelines aus, die die nachgelagerten Workflows zum Erliegen bringen.“

Eine andere Überlegung, die zu Data Products führt, ist die Nutzung von Daten außerhalb der Governance. An dieser Stelle kann ein Datenprodukt einen taktischen Ansatz darstellen, wie Yaad Oren, Global Head of Research and Innovation bei SAP, nahelegt: „Wenn Datensätze teamübergreifend ohne strenge Governance, klar definierte Prozesse oder eindeutige Zuständigkeiten genutzt werden, ist Unternehmen zu empfehlen, ein Data Product zu entwickeln. Datenprodukte, die in einer einheitlichen Datenbasis verankert sind, beseitigen Silos, schaffen ein gemeinsames Verständnis über die Daten und etablieren einen sicheren, standardisierten Zugriff auf diese.“

Eine dritte Möglichkeit, Datenprodukte strategisch zu nutzen, ist, diese für definierte Kunden in wiederverwendbarer Form zu entwickeln, um Effizienzgewinne einzufahren. Wenn ein solches Data Product erfordert, mehrere Datenquellen miteinander zu kombinieren, ist das Vision Statement und qualifizierter Business Value besonders wichtig. Christopher Zangrilli, Vice President of Technology Strategy beim Compliance-Dienstleister Vertex, erklärt: „Führungskräfte sollten sich fragen, ob die Daten die Cycle Times optimieren, die Entscheidungsgenauigkeit verbessern oder Compliance-Risiken mindern, um den Business Impact einzuordnen. Wenn Governance, Change Management, Qualität und Messverfahren von Beginn an integriert sind, wandeln sich Datenprodukte von experimentellen Tools zu strategischen Ressourcen.“

2. Datenprodukte standardisieren

Produkte im Supermarkt sind mit einer Verpackung versehen, auf der eine detaillierte Liste der Inhaltsstoffe, ein Verfallsdatum und ein Preis angegeben sind. Ganz ähnlich sollten Data-Governance-Verantwortliche vorgehen – und standardisieren, wie Data Products definiert, katalogisiert und gemanagt werden. Wie und warum, erklärt Abhi Sharma, Mitbegründer und CEO des KI-Anbieters Relyance AI: „Jedes moderne Datenprodukt sollte vier Fragen klar beantworten: Woher stammen die Daten, wie werden sie systemübergreifend transformiert, wer oder was nutzt sie und welche Governance-Verpflichtungen fallen dabei an? Ohne diesen durchgängigen Kontext entwickeln Teams Funktionen auf der Grundlage von Daten, die sie nicht vollständig verstehen.“

Obwohl Lebensmittelhersteller ihre Inhaltsstoffe veröffentlichen und mit Blick auf Gefahren wie allergische Reaktionen kennzeichnen, dokumentieren nur wenige die Herkunft ihrer Rohstoffe und welchen Weg diese vom Erzeuger zum Händler nehmen. Geht es darum, Data Products in streng regulierten Branchen zu entwickeln, kann es allerdings erforderlich sein, genau das zu tun – und die Data Lineage zu erfassen. Besonders wichtig ist das, wenn es darum geht, Datenquellen für KI-Applikationen zu standardisieren.

Carter Page, Executive Vice President of Research and Development beim Dev-Spezialisten Astronomer, weiß, was anderenfalls droht: „Ohne Data Lineage arbeiten Teams im Blindflug und Governance verkommt zu reaktiver Fehlerbehebung. Wenn Teams dagegen nachvollziehen können, woher die Daten stammen, wie sie transformiert wurden und welche Systeme darauf angewiesen sind, werden Aktualisierungen vorhersehbar, die richtigen Pipelines getestet, die betroffenen Stakeholder benachrichtigt und grundlegende Änderungen dokumentiert. Bevor es dadurch zu Incidents kommt.“

3. Data Products nachhaltig managen

Lebenszyklusmanagement erfordert bei APIs, Anwendungen oder KI-Modellen, einen Release-Plan für Optimierungen, Fehlerbehebungen und andere notwendige Updates festzulegen. Geht es hingegen um Datenprodukte, kommen mehrere, verwandte Disziplinen zusammen, wie Ulf Viney, EVP of Engineering beim KI-Datenspezialisten Precisely, erklärt: „Um den Lebenszyklus von Data Products zu managen, braucht es Versionierung, Testing, strukturierte Deployments und Stakeolder-Kommunikation.“

Ein weiterer grundlegender Unterschied bei Datenprodukten: Ihr Lifecycle Management ist eng damit verbunden, wie die zugrundeliegenden Datensätze wachsen – beziehungsweise, welche strukturelle Veränderungen diese durchlaufen. Ein Data Product, das zwar funktioniert, aber nicht veränderungsresistent ist oder keine Warnmeldungen ausgibt, wenn Fehlerbehebungen erforderlich sind, kann nachgelagerte Anwendungsfälle beeinträchtigen und das Vertrauen der Stakeholder und Nutzer in die Daten untergraben. Insbesondere letzteres gilt es zu verhindern. Wie, weiß Bethany Sehon, Senior Director of Enterprise Data bei Capital One: „Ein nachhaltiges und skalierbares Governance-Framework kann sicherstellen, dass Daten leicht zu finden, zu verstehen und zu nutzen sind.“

Teams, die geschäftskritische Echtzeit-Datenprodukte managen, die mehrere nachgelagerte Analytics- und KI-Anwendungsfälle unterfüttern, sind die folgenden DevOps- und Data-Governance-Praktiken zu empfehlen:

• Legen Sie unverhandelbare Data-Governance-Kriterien fest – insbesondere, wenn es darum geht, Datenqualitäts-Benchmarks zu setzen, etwaige Verzerrungen zu identifizieren und Datenschutzrichtlinien einzuhalten.
• Nutzen Sie fortschrittliche CI/CD-Pipelines, Continuous Deployment sowie Continuous Testing und automatisieren Sie Produktions-Deployments.
• Stellen Sie sicher, dass sämtliche Datenintegrationen über „observable“ DataOps verfügen, Datenqualitätsprobleme überprüft werden und Alerts ausgesendet werden, wenn die Pipelines zum Erliegen kommen. Um Requests und Incidents zu bearbeiten, sollten IT-Services zudem entsprechend definiert werden.
• Stützen Sie sich auf Plattform-Strategien wenn es um Datenmanagement geht – zum Beispiel im Hinblick auf Data Fabrics, DSPM, Dokumentenverarbeitung und Vektordatenbanken.

4. Datenprodukte verargumentieren

Ein Data Product auf die Beine zu stellen, ist leider kein Garant dafür, dass dieses auch angenommen wird. Das verdeutlichen auch die Beispiele von Reusable Code, API-Nutzung oder DevOps-Tools: Sie alle zielten darauf ab, Entwicklern das Arbeitsleben leichter zu machen und die Qualität zu verbessern. Trotzdem nahmen viele Teams lieber eine „Not invented here“-Haltung ein und setzten lieber auf Eigenentwicklungen statt die Standards anderer.

Datenprodukte stehen allerdings vor noch größeren Herausforderungen. Ganz besonders, wenn sie darauf abzielen, Datensilos zu konsolidieren oder Tabellenkalkulationen zu eliminieren. Um die Akzetanz zu fördern (und Feedback einzuholen), sollten die für die jeweiligen Data Products verantwortlichen Produktmanager deshalb ein Change-Management-Programm entwickeln. Förderlich ist dabei, darzulegen, wie das Datenprodukt auf den kulturellen Change und die KI-Strategie des Unternehmens einzahlt – etwa indem es die Demokratisierung von KI vorantreibt und die Kompetenz im Umgang mit der Technologie optimiert.

5. Data Products richtig evaluieren

Der Geschäftswert eines kundenorientierten Produkts wird häufig gemessen anhand der Auswirkungen auf den Umsatz, der Nutzungs-Metriken sowie der Kundenzufriedenheit. Interne, mitarbeiterorientierte Produkte lassen sich hingegen anhand ihrer Workflow-Effizienz, ihrem Potenzial für Produktivitätssteigerungen und der Mitarbeiterzufriedenheit evaluieren.

„Zu viele Unternehmen behandeln Datenprodukte immer noch als technische Outputs und nicht als strategische Assets“, kritisiert Daniel Ziv, Global Vice President of AI and Analytics beim KI-Anbieter Verint. Der wahre Wert von Data Products lasse sich daran ablesen, wie einzigartig die generierten Daten sind, wie viel messbaren Einfluss sie auf Entscheidungen nehmen, meint der Manager: „Wenn jedes Unternehmen Zugang zu denselben KI-Modellen hat, ergibt sich der Wettbewerbsvorteil aus ‚uniquen‘ Daten und der Geschwindigkeit, mit der diese in Maßnahmen umgesetzt werden können.“

Eine Best Practice auf die IT-Entscheider in diesem Zusammenhang zurückgreifen können, ist es, Metriken heranzuziehen, die Aufschluss über die Geschwindigkeit digitaler Transformationsvorhaben geben. Dazu gehören etwa:  

• „Time to Data“,
• „Time to Decision“,
• „Time to Innovation“, und
• „Time to Value“.

(fm)

Dieser Artikel ist im Original bei unserer Schwesterpublikation Infoworld.com erschienen.

Google DeepMind CEO Demis Hassabis on Tuesday reiterated his push for an AI industry self-regulation effort, led by the US government, that is particularly focused on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis said he would propose that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants would be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. He has already worked on a US government initiative evaluating AI safety, which involved DeepMind, Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

Google DeepMind CEO Demis Hassabis on Tuesday reiterated his push for an AI industry self-regulation effort, led by the US government, that is particularly focused on artificial general intelligence (AGI) and national security. 

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

He noted, however, that the funding would need to be substantial, and would most likely come from industry, to allow the new body to attract world-class technical talent and obtain the necessary compute resources for large-scale testing.

Hassabis said he would propose that the organization “be responsible for developing assessment protocols and working with appropriate federal agencies and the US National Labs to conduct testing in areas relevant to national security,” and that AI vendor participants would be encouraged to adopt best practices, such as publishing model cards with technical details, maintaining strong internal cybersecurity, vetting key personnel, and providing sufficient resourcing for safety and security research.

This is not the first time Hassabis has expressed worries about AGI. He has already worked on a US government initiative evaluating AI safety, which involved DeepMind, Microsoft and xAI (now SpaceXAI) working with the Center for AI Standards and Innovation (CAISI), a division of the US Department of Commerce. It allowed CAISI to conduct pre-deployment evaluations and targeted research to “better assess frontier AI capabilities and advance the state of AI security.”  

The rest of the world may have concerns

Analysts and consultants were mixed about the move, with most expressing concerns about whether an industry-focused group would prioritize the public’s best interests.

“Self-regulation is not viable because it implies everyone is able to regulate themselves and will do so in line with the best interests of the public. Most tech vendors don’t have the capacity to self-regulate. They would just prefer a set of rules within which they can operate,” said Gartner VP analyst Nader Henein. “For-profit organizations are required to do what is best for their shareholders, and external regulation ensures that those organizations are never in a conflict of interest where they have to choose between what is good for their shareholders and what is good for the public.”

And, said Sanchit Vir Gogia, chief analyst at Greyhound Research, given the international nature of AI models, an effort coordinated by the US government might alienate other countries. 

“National security is the proposal’s accelerator in Washington and its poison pill abroad: the framing that opens the only gate available at home invites foreign capitals to read the institution as an instrument of American strategy,” he pointed out.

“The map is already plural,” he said. “Brussels switches on enforcement powers over general-purpose models [starting in August 2026], London runs the AI Security Institute, and Beijing licenses on its own terms. California and New York have legislated for frontier models at home. The durable route is shared technical evidence with sovereign enforcement, sealed through mutual recognition rather than deference, with India and the other major non-Western markets holding authorship rather than seats.”

Gogia added that the rules enacted by even such a group may not address all of the key concerns of enterprise IT. A US government effort along the lines that Hassabis is proposing would result in testing that “sits close to intelligence and industrial policy, and those functions will not stay neatly separated. A model can pass every catastrophic-risk test and still fail the enterprise on privacy, reliability, and liability,” he noted.

Walmart’s former director of cybersecurity Steven Eric Fisher, who is now an independent cybersecurity consultant, said he found the proposal “well-intentioned, but it addresses a highly polarized topic at a time when commercial interests carry unprecedented political influence, which is not always applied benevolently.”

He added, “an exclusive US standard that is not globally respected or enforceable would likely fail to achieve its core purpose and would place US companies at a competitive disadvantage.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that a deep dive into how FINRA operates today is illustrative of what IT leaders can expect from this effort, assuming the industry adopts that model.

“When the CEOs of the five companies that would be regulated are also the primary drafters of the standards, the standards will reflect those companies’ interests. FINRA has an independent board, but the operational reality is that member firm perspectives dominate the working groups that write the actual rules,” he said. “There is no reason to expect an AI equivalent to work differently, and every reason to expect it to work worse, because AI standardization is happening faster than any industry has ever attempted to standardize itself, and speed is the enemy of independent oversight.”

Carmi Levy, an independent technology analyst, was even more emphatically opposed to the Hassabis proposal.

“Asking Big Tech companies to self-police is analogous to allowing foxes to guard the henhouse. It hasn’t worked to date, and it won’t work going forward. Expecting these organizations to somehow change their ways at this point in time represents the height of naïve thinking,” Levy said. “The framework proposed by Demis Hassabis is a self-serving roadmap for an industry bent on racing to the AI horizon regardless of the harms caused along the way. It is impossible to quantify the dangers to broader society should frameworks allowing self-regulation become the norm.”

Some love the proposal

An almost completely opposite stance came from Yuri Goryunov, CIO of consulting firm Acceligence, who applauded the proposed move.

“This is one of the rare setups where industry self-regulation has a real shot, and enterprise IT should be enthusiastically rooting for it,” he said. “It fails when harms are externalized, such as in social media content moderation. Or when the overseer outsources judgment to the overseen, such as the FAA’s delegation to Boeing before the 737 MAX. It works when everyone in the industry shares the catastrophic downside.”

He suggested, however, that the best precedent here isn’t FINRA, it’s INPO, the Institute of Nuclear Power Operations, which the nuclear industry created within months of the 1979 Three Mile Island partial reactor meltdown “on the logic that an accident anywhere is an accident everywhere. INPO peer-reviews every US plant, its evaluations move insurance premiums, and it sits on top of the NRC’s statutory floor. That is a public-private stack very close to what Hassabis is describing. Frontier AI has the same structure: one lab’s catastrophic failure brings regulation down on all of them.”

For enterprise CIOs and other IT executives, Goryunov said, that model has the potential for being a big win.

“Today, every enterprise duplicates the same AI diligence of red-teaming, eval suites, governance committees and each does so with less information than any certifying body would have,” Goryunov said. “A credible standards regime does for AI what UL did for electrical equipment and SOC2 did for cloud: it converts an unknowable risk into a procurable product and gives boards a defensible standard of care. That’s not red tape. That’s peace of mind with an audit trail.”

However, Mahapatra said, “the countervailing view is that the alternative to industry-led standards is probably not thoughtful legislation. It is probably no standards, or state-by-state fragmentation, or the current pattern of ex-post enforcement actions where regulators surface concerns years after harm has already occurred.”

Thus, he noted, “Hassabis is making the reasonable argument that imperfect fast standards are better than perfect slow ones, and there is genuine merit to that view for topics like agent identity, evaluation methodology, and interoperability, which are exactly the areas OpenClaw is also targeting.”

This article originally appeared on CIO.com.

OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. 

“Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post. “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.”

The statement, co-authored by OpenClaw creator Peter Steinberger, pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.”

But it reassured users that the original OpenClaw leadership is still in charge.

“Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.”

However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. 

Neutrality claim in question

“The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney, principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’

He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed.

Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. 

“There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability.

“But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.”

Good news, bad news

Jason Andersen, principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. 

“We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.”

Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.”

But not everyone sees the promised structure as entirely good for IT.

Ishraq Khan, CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.”

He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.”

Will it remain a nonprofit?

However, said Justin Greis, CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives. 

“OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.”

He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.”

Risks are ‘squarely in IT’s lap’

Consultant Brian Levine, executive director of FormerGov, echoed Greis’ concerns. 

“CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.”

Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost.

“OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements, can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”

This article originally appeared on Computerworld.

Port has rolled out Port AI Builder, a vibe coding platform designed for software development and platform engineering teams.

Announced July 14, Port AI Builder lets teams create and run agentic workflows in natural language, with built-in human-in-the-loop review and approval. The platform lets organizations apply AI agents across the SDLC (software development life cycle), drawing on domain skills spanning site reliability engineering, devops, architecture, security, AI governance, data modeling, and UX, while maintaining governance and visibility, the company said. Port AI Builder is available through free and paid subscriptions.

Port AI Builder works on top of Port’s Agentic SDLC Platform, which provides the context lake, workflow orchestration, agent management, and governance that enterprises need to operationalize AI SDLC, according to the company. The new AI Builder lets teams build production-grade workflows in minutes, for use cases such as autonomous resolution, AI cost management, and engineering performance tracking, without losing control, Port said.

These days, application developers can take their pick from a vast menu of architectural solutions. We can choose from the well-understood to the experimental, and from blended solutions in between. Several powerful middle-ground technologies that emerged during the cloud revolution have really come of age. Here we’ll take a look at putting together three of the most impressive: GitHub, Vercel, and Firestore.

Each of these is an important tool in its own right that can be used to attack specific problems. In combination, they not only meet the needs of several important application scenarios, but they have a superpower—the ability to dramatically shorten the distance between development and deployment.

There is nothing quite as gratifying as putting your hands on just the right mix of tools for a given need.

A ‘no-ops’ stack built for speed

If your primary goal is sheer development velocity, you would be hard-pressed to top this architecture. This “no-ops” stack collapses the distance between your local IDE and a globally distributed production environment. You are essentially trading the overhead of managing VMs and load balancers for the sheer speed of committing code and watching it deploy automatically.

While each component is highly flexible, adopting them requires a specific, event-driven mindset. There are a few finicky bits to manage, mostly around routing environment variables securely and designing around stateless back-end functions. But the constraints are obvious and well-documented.

Before we look more closely, let’s quickly identify the kinds of apps that are a perfect fit here, along with those that are workable and those that really merit a different approach.

• The sweet spot (deploy and go): AI-mediated applications, asynchronous game back ends, and real-time collaborative B2B dashboards. This architecture perfectly absorbs the unpredictable latency of LLM APIs and instantly syncs state across multiple clients without requiring you to build custom WebSocket infrastructure.
• The middle ground (workable, with trade-offs): Headless e-commerce, moderate IoT telemetry, and apps requiring scheduled batch processing. You will encounter friction if your catalog relies on deeply relational SQL constraints, or if your background reporting jobs take longer than a few minutes and hit serverless execution limits.
• The danger zone (look elsewhere): High-frequency trading, fast-paced action multiplayer games, heavy data ETL pipelines, and core financial ledgers. Serverless architectures cannot natively hold open the persistent WebSockets required for twitch-reflex data, and heavy compute tasks will abruptly time out.

We should mention that these categories are not mutually exclusive. Many enterprise applications, such as a full-scale e-commerce platform, straddle these lines. You might use Vercel and Firestore to build a lightning-fast, reactive storefront that handles ephemeral user state like shopping carts, while simultaneously “stitching in” a managed SQL database like Supabase or PlanetScale. This hybrid approach allows you to maintain the relational integrity required for back-office inventory and financial ledgers and pair it with the front-end velocity this stack provides.

GitHub: the bedrock

I don’t need to introduce you to GitHub. It is a central element of the development landscape. I still remember CVS and SVN with a certain nostalgia, but the enhancements of Git speak for themselves. When combined with the orchestration powers of GitHub, it is no wonder that virtually the whole industry has adopted this type of platform.

Git plus GitHub gives you an enormous amount of power already, in terms of how you can organize and automate your projects. But there is a next-level experience in combining GitHub and Vercel. For JavaScript-based projects, you can take simple GitHub pushes and turn them into instantly deployed clients and serverless functions. It is one of the cleanest and least fiddly ways to move from raw code on your local machine to a globally deployed, full-stack architecture.

Vercel: the nexus

Vercel is more than just a deployment host. It is a control plane that ties this high-velocity, no-ops architecture together. Alongside GitHub and Firestore, Vercel’s deeper strength is its ability to act as an orchestration layer between your reactive front end and external stateful services.

Vercel has a great amount of facility in fine-tuning what branches go to what environment and helpful features like instant rollback. You can just log into Vercel’s dashboard for your project and see the history of deployments and any errors and logs. It’s a simple menu choice to roll back to a historical version or compare one version against another.

When you “stitch in” third-party services (such as a managed SQL database like Supabase or a payment processor like Stripe), Vercel’s serverless functions become the lightweight interface, and Vercel’s the adapters handle the communication. You offload the integration logic (the service layer) to Vercel’s global Edge Network, keeping your UI and back end clean, responsive, and decoupled. 

In short, Vercel allows you to get the speed of the “no-ops” development life cycle without sacrificing the complex transactional integrity required for some applications like enterprise inventory systems.

Firestore: the datastore

Firestore is an extremely lightweight, NoSQL, cloud datastore. It has a great deal of add-on power, but its core value proposition is that it accepts virtually any data you stuff into it and it provides event-driven subscriptions to data changes.

These two capabilities together make Firestore about as straightforward a solution to a managed back end as you can imagine. You subscribe to collections or even fields and then you simply stick “unstructured” data (read: JSON with variable fields) in and the client waits for the changes it is interested in.

This is so streamlined that one can just point the browser (or native mobile app) directly at Firestore and listen for events. Which immediately raises the question of identity, for auth and for data visibility, but hold on—Firestore’s third superpower is that it has an authentication module that actually works. What I mean is, it is actually pretty simple and yet confidently secures your app.

Sometimes auth solutions seem either too simple (and yet opaque) or too mired in the nitty gritty. Firestore auth will let you do some basic configuration and start using a reasonable auth almost immediately. 

Not to belabor the point, but having a realistic and attainable auth solution elevates your stack to a production grade—one that can handle many real-world applications. Firestore auth plays nicely with other important APIs, like Stripe. Typically, auth is a major feature that feels like off-roading in a Honda Civic, but Firestore’s approach to auth, added to this particular stack, feels like a normal speed bump. It’s just another component you plug in, rather than a tentacled alien you weave into the your code.

The limits of the velocity stack

This architecture combines components that are optimized for flexibility. That same character also introduces distinct limitations. Understanding these is essential before committing production workloads.

The serverless life cycle

Serverless functions are spun up to handle requests. They close out soon afterward and lose any state. For that reason, they cannot natively hold open persistent WebSockets. If your system requires continuous, sub-millisecond, bidirectional streams—like a real-time multiplayer action game or a high-frequency trading dashboard—pure serverless will fight you all the way. You are forced to introduce a third-party managed WebSocket service to route messages back to your stateless endpoints via HTTP webhooks.

The execution time ceiling

Vercel (like all serverless platforms) enforces strict timeouts on operations. While enterprise tiers might grant you up to 15 minutes, standard functions often time out after 10 to 60 seconds. Long-running tasks like video transcoding, database scripts, or orchestrating multi-step AI agent workflows, which might take 20 minutes to resolve, will run up against these limits. Heavy-lifting tasks must be offloaded to a dedicated, long-running service like Google Cloud Run, or broken into smaller, asynchronous chunks via message queues.

The cold start reality

While the industry has made massive strides in minimizing initialization times—particularly with lightweight edge networks—traditional Node.js-based serverless functions still experience cold starts. If a function has not been invoked recently, or if traffic spikes require a new instance to spin up concurrently, the first request will take a noticeable latency hit as the container provisions and the code loads.

API instead of RAM

In a traditional server environment, you can store transient data in global RAM, allowing subsequent requests to access shared context instantly. In the serverless model, every request might hit a fresh container. Therefore, all shared context must be externalized. Although Firestore serves brilliantly as the state manager, relying on a database for high-frequency, sub-millisecond, ephemeral caching introduces network latency and per-operation costs. That said, using a shared RAM state on a server is non-trivial also, unless you are using a single app server and VM (because high-availability or fail-over requirements will lessen the RAM win on a traditional server).

Tuning for velocity and control

Every architectural decision is a trade-off. There are no cost-free choices. By adopting the GitHub, Vercel, and Firestore stack, you are explicitly maximizing feature velocity over fine-grained control.

You lose the ability to tweak the underlying operating system, hold open persistent sockets, or run hour-long back-end scripts. In exchange, you gain an architecture that scales from zero to global distribution instantly, requires virtually no devops maintenance, and perfectly absorbs the asynchronous, event-driven realities of modern application development.

For the right application—whether it is a fast-moving prototype or an enterprise AI copilot—this stack doesn’t just save time; it fundamentally changes how quickly a small team (or a single person) can impact the market. You stop worrying about build chains, load balancers, and server patches, and you focus on the central mission: shipping features.

I attended several major technology conferences in 2025 where the first AI agents embedded in enterprise SaaS platforms were announced. Some of these agents showed promise and a glimpse into the future of work, while others looked like natural language extensions of a platform’s existing functionality.  

At the end of 2025, Anthropic and OpenAI launched new AI models and code-generating capabilities. More developers tried vibe coding, and some platforms launched spec-driven development capabilities. By February 2026, even The New York Times reported that the AI disruption had arrived, noting that code generators were building “apps that may be flawed, but credible.”

Wall Street investors took notice of the code-generation improvements and other disruptive factors, driving a selloff in SaaS stocks, now referred to as the “SaaSpocalypse.” Part of their concern stemmed from the belief that CIOs would use AI to write software that would replace SaaS solutions.

AI innovations from SaaS and solution providers

But I thought differently and wrote a response in my article asking whether AI is the end of SaaS as we know it. CIOs might use AI to accelerate application modernization, but I doubt they would replace their ERP, CRM, and even smaller SaaS point solutions by building them.

Instead, I believed it would be SaaS companies that would take the most advantage of AI code-generation capabilities.

This hypothesis drove me to attend nine conferences this spring to see how SaaS companies were launching AI agents and defining a new future of work. I wrote eight articles on what CIOs need to know about data management, agile organizations, marketing, ERPs, critical process management, and other evolutions to plan for in the AI era.

Now, looking across all nine conferences, I can draw some conclusions about how AI agents are shaping the future of work. Here are my learnings and what CIOs need to consider when evaluating and deploying AI agents in the workplace.

Agentic, human-in-the-middle, or augmenting human?

SaaS companies have very distinct perspectives on the future of work, including the extent to which humans will play which roles and whether and how quickly we’ll see agentic, fully automated work.

For example, Atlassian proclaimed, “step into the future of human-AI collaboration,” while SAP unveiled “the autonomous enterprise.” Snowflake aimed to “make AI real for business,” while Appian targeted “serious AI built on process.”

These vendors’ marketers had to decide whether to lead with AI, people, or business in their messaging, but so must CIOs as they contemplate their AI strategies and how to get employees to fully adopt AI agents.

Some CIOs see a fully automated agentic AI as the future, with human-in-the-middle as a transitional phase as departments build trust in AI agents’ decision-making and automation capabilities.

Other CIOs see AI more as a tool that delivers productivity improvements by augmenting human decision-making capabilities. Many of these CIOs see human augmentation as essential to supporting critical thinking, innovation, and creativity.

Deloitte’s State of AI Report, published in January, provides a benchmark. It states that 36% of IT leaders expect at least 10% of their jobs to be fully automated in the next year, and 82% expect to reach that benchmark in three years.

Many organizations will have a mix of AI agents, choosing automation where reliability at scale is possible, but opting for human augmentation in operationally critical or customer-facing domains. But how CIOs position AI agents is not only an operational strategy; it’s also a cultural statement that shapes employees’ embrace of AI and whether detractors vocalize job-loss fears.

In the short term, it will also weigh in on which AI agents to use from different partners and which areas to build in-house.

Many options to test and deploy AI agents

Many solution providers are demonstrating significantly more AI agents this year. For example, SAP went from 40 Joule Agents in 2025 to over 200 in 2026. Three technology capabilities are fueling this significant growth:

• Adobe, Appian, Boomi, Cisco, Domo, Salesforce, SAP, Snowflake, and others offer data fabrics and data-pipeline capabilities to connect data sources outside the primary workflows supported by their platforms. Appian, Pega, Quickbase, and SAP also centralize business process automation, an important starting point for developing AI agents.  
• MCP servers enable integration and communication between AI agents and are used to facilitate multistep agentic workflows. Virtually all the companies announcing major investments in AI agents are also announcing MCP integration capabilities and related partnerships.
• Solution providers are not just using AI code-generating capabilities; many are launching their own AI agent development tools. The first beneficiaries of these development tools are the solution providers themselves and their integration partners, who use them to accelerate the development of AI agents and make them available to customers.

The result is that CIOs will have many options about which agents to test, but will have to dedicate analysts to understand the capability, cost, and compliance trade-offs. Additionally, expect AI agent capabilities to evolve significantly over the next few years, so CIOs should continuously revisit their decisions regarding deployed AI agents, focusing on performance, benefits, and ROI.

CIOs should also watch for signs of shadow AI and employee confusion about which AI agents to experiment with on different platforms. The AI strategy should include a transparent, defined process for selecting, reviewing, evaluating, procuring, deploying, driving adoption, monitoring, and collecting end-user feedback around AI agents.

AI development capabilities for engineers and citizen builders

The apparent ease-of-use of AI code generators may lead some engineering teams to build AI agents rather than buy them from SaaS providers. But CIOs should quickly realize that coding is just one step in developing AI agents, and that aggressively pursuing a build strategy can lead to AI debt and increased AI costs.

DevOps teams can code AI agents using tools such as Claude, Codex, Lovable, and Replit — a do-it-yourself approach. Some SaaS companies are providing an alternative, with AI agent development tools that leverage the data, infrastructure, and governance baked into their platforms. Many of these development tools offer flexibility, allowing developer teams to select AI models and development environments.

Examples of new and enhanced AI development tools I saw at conferences this quarter include:

• Appian Composer and Agent Studio
• Atlassian Rovo Dev
• Boomi Companion
• Cisco Cloud Control Studio
• Domo App Catalyst
• Pega Infinity Studio
• Quickbase Pave
• Snowflake CoCo
• SAP Joule Studio.

I also reviewed Nutanix Agentic AI, a platform-as-a-service for accelerating the deployment of agentic AI workloads, and Adobe Firefly AI Assistant for creatives.

These development tools can target different audiences. Some look like low-code development tools targeted at software developers, whereas others are no-code and enable citizen developers, i.e., businesspeople, to develop applications and agents. Additionally, some of these tools support spec-driven development and generate artifacts such as product requirement documents (PRDs), data models, and testing capabilities.

Before commissioning AI development for apps and agents, CIOs should sponsor proofs of technical, data, modeling, security, and governance capabilities.

The context layer powering AI agents

Between AI agents and the enterprise’s intelligence, including structured data sources, defined business processes, and agent interactions (both human-to-agent and agent-to-agent), lies an evolving “context layer.”

This layer refers to the enterprise knowledge that AI agents draw on when evaluating signals and recommending or taking actions. Context may include a knowledge graph, a semantic layer, cleansed document repositories, and other knowledge bases.

The context layer, skills, tools, out-of-the-box agents, and governance capabilities are some areas to review where solution providers differentiate. Some examples: 

• Many support the Open Semantic Interchange, and some brand their context layers, such as the Atlassian Teamwork Graph, Boomi Knowledge Hub, and the SAP Knowledge Graph.
• Some are branding their guardrails, such as Adobe’s AI Brand Intelligence, Appian’s Private AI, and Quickbase AI Control Center.
• To manage AI agents at scale, some are extending the notion of data catalogs and other governance tools to the AI domain with products such as Boomi Connect, SAP AI Agent Hub, and Snowflake Horizon Catalog.

CIOs should recognize that while solution providers will compete on capabilities, the real “secret sauce” of the context layer lies in the company’s trusted data, well-defined business processes, and employee adoption of AI agents.

Conversational user experiences and coworkers

AI agents use the context layer, but also tap into skills, which encode the procedures they can follow, and tools, which prescribe the actions they can take. Before AI agents are ready to pilot, their governance, including permissions, approval gates, and other guardrails, must be defined. Other capabilities to look for when defining AI agents include orchestration, testing evals, and observability.

In 2025, many solution providers bolted on AI agents to their existing user experiences. This year, many solution providers showcased new conversational user experiences that employees can use instead of traditional ones built with forms, flows, reports, and static dashboards. Conversational user experiences are where AI agents and people come together, whether it’s human-in-the-middle or human augmentation.

Solution providers also grouped their AI agents into assistants or coworkers. For example, Adobe CX Coworker illustrates human augmentation, helping marketers manage campaigns with prompts and monitor their performance. SAP launched Joule Assistants across several business functions, including finance, human capital, supply chain, and customer experience. Other assistants, such as Appian AI Copilot, Atlassian Rovo, Cisco AI Assistant, Nutanix NIVA, and Snowflake CoWork, offer AI-first user experiences to assist different end-user types.

CIOs should demo these AI-first user experiences to glimpse the future of work.

Developers are already getting used to these experiences through code generators and vibe coding tools. Now, similar capabilities are being tailored across all business functions. CIOs should ramp up their change management programs to accelerate the adoption of these AI capabilities.

Solution providers are showcasing AI capabilities that can help CIOs reshape their businesses. But in Q2, there were only a few examples of how AI can help CIOs drive growth, evolve business models, or embed AI into customer-facing products. I expect to see a wave of further AI innovations that will go beyond productivity improvements and efficiencies and help CIOs pursue growth-driving digital transformation strategies.  

Sacolick travelled to conferences mentioned in this article as a guest of Adobe, Appian, Atlassian, Domo, Nutanix, SAP, and Snowflake. In addition, he was hired by Quickbase to speak at its conference.

OpenClaw’s announcement that it has become a nonprofit foundation is generating IT excitement because of the potential for governance and development consistency that the popular platform has thus far lacked. Still, some worry about the risks created by the move. 

“Our ambition is for OpenClaw to be the Switzerland of AI. Neutral ground where every model and every lab can plug into the technology and collaborate on standards in the era of agents,” OpenClaw said in a post. “That work is already underway in Foundation-convened councils on agent identity, agent profiles, evals, and enterprise deployment.”

The statement, co-authored by OpenClaw creator Peter Steinberger, pointed out, “the great open source projects of our time — Linux, Apache, Mozilla — endure because a neutral steward stands behind them. That is the role we are taking on to keep OpenClaw MIT licensed, open, and independent so that everyone building on it can trust it will be here for the long term.”

But it reassured users that the original OpenClaw leadership is still in charge.

“Peter built this thing and Peter keeps making the calls, especially the technical ones. Since joining OpenAI earlier this year, he has continued to steward OpenClaw as an open and independent project, and OpenAI has made a commitment to keep it that way,” the post said. “The foundation is here to serve: good governance, stable funding, and paying the people who keep the claws alive.”

However, some analysts and consultants were skeptical about how much true independence Steinberger would have, given his salaried role with OpenAI. 

Neutrality claim in question

“The Switzerland of AI neutrality claim collapses under its own announcement,” said Noah Kenney, principal consultant at Digital 520. “OpenAI runs a team [at OpenAI] called Claw Labs that Peter leads and OpenAI is a major donor to OpenClaw. The ‘neutral steward’s’ chief technical decision maker is employed by one of the competing labs it is supposed to be neutral with.” To OpenAI, he said, OpenClaw is closer to a tax-exempt nonprofit subsidiary than it is to a neutral ‘Switzerland of AI.’

He pointed out that, in addition, Microsoft is shipping the enterprise version of OpenClaw, and Nvidia is shipping the hardware bundle. “This is being called the Switzerland of AI, but Switzerland does not have its central bank run by France,” he observed.

Kenney said that what the new OpenClaw has actually built is “a shared dependency that several competitors fund, staff, and steer, wrapped in a nonprofit structure. Enterprise IT should understand that structure, because treating OpenClaw as neutral is a mistake,” adding that CIOs need to look at this development devoid of the emotional component. 

“There is a strategic irony here that CIOs should sit with,” Kenney said. “If OpenClaw succeeds at becoming the universal agent substrate, then every model plugs into the same identity layer, the same profiles, and the same deployment plumbing. The thing every vendor is racing to own becomes a commodity that nobody owns.” He pointed out that, in the short term, that is genuinely good news for buyers because it means less lock-in and more portability.

“But,” he said, “when the connective tissue is free and natural, the only labs that benefit are the ones with the best models and the deepest distribution. Commoditize the layer below you and you compete on the layer where you are already strongest. The foundation is not a charity. It is the biggest players agreeing to stop fighting over the plumbing so they can fight over the water, and the enterprise is the one paying the water bill either way.”

Good news, bad news

Jason Andersen, principal analyst at Moor Insights & Strategy, liked the potential consistency that could emerge from the structural change, given the complexity of agent development today. 

“We are seeing a lot of OpenClaw variants hit the market, such as those from Nvidia as well as competing products from cloud and SaaS vendors. A common base helps solidify the common parts,” Andersen noted. “That said, a common challenge is the sustainability of these open source foundations over time. In addition to releasing code, these foundations need funding to evolve and grow. And that funding needs to come from continued momentum to incentivize existing members to increase investment and recruit new members to join.”

Andersen stressed that IT buyers need to keep an eye on the roadmap for any OpenClaw variant they choose to deploy, “as that will directly impact the foundation, and the momentum of the foundation and common base. If the common base loses momentum, it can lead to forks, or just a loss of innovation. When that happens, members tend to back away, which puts customers in limbo.”

But not everyone sees the promised structure as entirely good for IT.

Ishraq Khan, CEO at coding productivity tool vendor Kodezi, said, “most CIOs do not want to bet their future entirely on a single model vendor. They want Claude for some workloads, GPT for others, open models for sensitive environments, and potentially internally fine-tuned systems for specific use cases. The problem is that every vendor currently brings its own identity system, tool interfaces, permissions model, and operational assumptions. That fragmentation does not scale.”

He said, “the risk if standards fail is straightforward: every vendor builds its own closed ecosystem, enterprises become locked into individual stacks, and security becomes dramatically harder. The opportunity if OpenClaw succeeds is equally significant: enterprises get portable agents, common identity standards, interoperable tooling, and a healthier competitive market around models rather than ecosystems.”

Will it remain a nonprofit?

However, said Justin Greis, CEO of consulting firm Acceligence, one of the key details that IT executives will want to keep in mind is that OpenAI also began as a nonprofit, but it was quickly seen as not adhering to nonprofit objectives. 

“OpenAI’s transition from a nonprofit research organization into a more complex structure highlighted the challenge of maintaining mission alignment while scaling technology, capital, partnerships, and commercial operations,” Greis said. “OpenClaw has the opportunity to address some of those governance questions earlier by establishing clear principles around neutrality, transparency, and decision-making before the ecosystem becomes even larger and more valuable.”

He noted, “we have seen this pattern before with technologies like Linux and Kubernetes. The strongest open ecosystems succeeded because they created trusted foundations that enterprises could build upon. The technology was important, but the governance model that underpinned it was equally critical.”

Risks are ‘squarely in IT’s lap’

Consultant Brian Levine, executive director of FormerGov, echoed Greis’ concerns. 

“CIOs shouldn’t assume that this nonprofit will always be a nonprofit, or confuse being a nonprofit with actually being neutral or unbiased,” he said. “The risks are squarely in IT’s lap: autonomous agents ‘with their own identity’ acting on a user’s behalf blow straight through traditional IAM assumptions. Issues, such as agent identity, auditability, secret handling. Identity boundaries have not yet been reliably solved. Until they are, enterprises should treat OpenClaw agents like privileged service accounts, not like a browser plugin.”

Independent cybersecurity and risk advisor Steven Eric Fisher pointed to another IT exposure that might come from this OpenClaw transition: Cost.

“OpenClaw currently has a very high token burn rate in usage, which presents a significant cost consideration for large-scale enterprise adoption,” he said. “The skills marketplace introduces a new supply chain threat that enterprises will need to manage. Threat management, and specifically handling external marketplace elements, can be highly challenging for open-source operations. Ultimately, at scale, enterprise adoption could become a difficult balancing act between managing high operational costs and securing an expanded security surface.”

If you’re a software developer or architect, you might wonder if programming certifications are still worth the effort, especially in the era of rapid AI-driven evolution. The short answer is, it depends.

“Certifications are shifting from a checkbox to a compass. They’re less about proving you memorized syntax and more about proving you can architect systems, instruct AI coding assistants, and solve problems end-to-end,” says Faizel Khan, lead AI engineer at Landing Point, an executive search and recruiting firm.

“In the AI era, fewer students will get trained on the job, which means they have to train themselves,” Khan says. “Certifications—especially architectural ones like AWS, Kubernetes, Terraform—are still the clearest path to do that.”

Pros and cons of programming certifications

It’s not all black and white when it comes to deciding whether to pursue programming certifications. The effort involves both pros and cons.

“In terms of pros, certifications concretely demonstrate that you have a skillset at a documented level,” says Chris Riccio, vice president of engineering at Uplevel, an engineering optimization system provider. “They also show that you’ve put in the time and effort to learn, study, and prepare.”

Programming certifications are “a useful way to validate foundational skills and show that someone understands core concepts,” says Greg Fuller, vice president of Skillsoft’s training provider, Codecademy. “They’re especially helpful for people entering the field or shifting from adjacent roles.”

Certifications offer a structured path to demonstrate proficiency, and they can confirm your ability to build and deploy in various environments, Fuller says.

These types of certifications often demonstrate baseline proficiency and continuous learning, says Reshmi Ramachandran, head of partnerships and GTM strategy for Cprime, a consultancy. “These are often key indications of proficiency for companies looking to filter large candidate pools,” she says.

Certifications really do two things, Khan adds. “First, they force you to learn by doing,” he says. “If you’re taking AWS Solutions Architect or Terraform, you don’t pass by guessing—you plan, build, and test systems. That practice matters. Second, they act as a public signal. Think of it like a micro-degree. You’re not just saying, ‘I know cloud.’ You’re showing you’ve crossed a bar that thousands of other engineers recognize.”

But there are cons, too. “In tech, employers don’t just want credentials, they want proof you can deliver,” says Kevin Miller, CTO at IFS, a maker of factory automation software. “Programming certifications can be a valuable indicator of your baseline knowledge and competencies, especially if you’re early in your career or pivoting into tech, but their importance is dwindling.”

AI tools that can generate, debug, and optimize code are already performing tasks once done by entry-level developers, “which means fewer traditional programming roles are available,” Miller says. “As a result, the job market is becoming more competitive, and certifications aren’t seen as the noteworthy achievement they once were.”

What’s more, not all certifications carry the same weight, Riccio says. “Some may reflect only familiarity rather than true expertise,” he says. “Certifications also often measure ‘book knowledge’ rather than practical experience, and they don’t always map clearly to the requirements of a specific role.”

Programming certifications “can be a helpful signal, especially for confirming baseline knowledge in areas like cloud, security, or devops, but they’re not the full picture,” says Morgan Watts, vice president of IT at 8×8, a contact center platform developer.

“I’m more interested in a candidate’s attitude and aptitude: what problems they’ve solved, what they’ve built, and how they’ve approached challenges,” Watts says. “Certifications can show commitment and discipline, and they’re especially useful in highly specialized roles. But I’m cautious when someone presents a laundry list of certifications with little evidence of real-world application.”

A certification without experience doesn’t carry much weight, Watts says, and over-certification can sometimes signal the wrong focus. “Ultimately, it’s the ability to apply knowledge, collaborate, and adapt that sets great developers apart,” he says.

Finally, certifications can age fast, Khan says. “Tech stacks evolve and a badge from two years ago may already feel dusty,” he says. “And some certifications are paper-thin—multiple-choice exams that don’t prove you can debug production at 2 a.m. So, the risk is you collect badges but still can’t ship.”

Which certifications will get you noticed?

Despite the drawbacks, certifications are still very much in demand, and some carry more weight than others.

The most in-demand certifications are typically platform-based—Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, and others, Riccio says. “Many of these platforms provide managed services that integrate with existing systems or serve as the glue between them,” he says. “Today’s engineering teams aren’t just building standalone systems in isolation; they’re using other systems to store data, orchestrate business workflows, and connect applications.”

A certification that demonstrates the ability to build solutions on these platforms can put a development professional ahead of the competition, Riccio says.

“The certifications I see in highest demand tend to reflect the evolving tech landscape,” Watts says. “Cloud certifications from AWS, Azure, and GCP are incredibly valuable, especially as distributed systems become the norm.”

Also in demand are certifications for devops and CI/CD tools including Kubernetes, Docker, and Jenkins, Watts says, “because deployment automation and reliability are critical at scale. Also, with AI reshaping development, we’re seeing growing interest in certifications around machine learning, data science, and AI model integration. These certifications stand out because they align directly with the skills that teams need to move faster and more intelligently.”

On the AI front, certifications in TensorFlow and other machine learning platforms are gaining traction as organizations look to embed AI across the development process, Watts says. “These are the certifications that align closely with where modern engineering is headed—scalable, secure, and AI-enabled,” he says.

And then there are cybersecurity credentials that continue to be in high demand. Security certifications, such as CompTIA Security+ or Certified Ethical Hacker, “have become essential as every company faces increasing cyber threats and compliance requirements,” Miller says.

“Core programming certifications are still a bit niche, but the adjacent skills, like those that help developers deploy, secure, and scale their code, are driving demand,” Fuller says. “Companies want developers who understand the full lifecycle, not just how to write code.”

Also see: The best Java certifications for software developers.

Certifications in the hiring process

Experts are clear that programming certifications alone will not get you the job. But they do play a role in the hiring process.

“The information technology world is characterized by rapid and continuous evolution, including the skills and knowledge required to work in the field,” says Diane Rafferty, managing director of the National Technology Group at Atrium, a global talent solutions and extended workforce management firm.

“Certifications not only prove that you have the skills and knowledge needed, but they also show employers that you’re invested in your education and career growth,” Rafferty says. “They can give you a competitive edge when looking for a job, as many companies now require candidates to have them.”

Certifications are one part of the hiring equation, “but never the only part,” Watts says. “They help validate that a candidate has taken the time to build foundational knowledge, and that’s a good sign. But I put more weight on how a person thinks, solves problems, and contributes to the team. I look for people who are curious and proactive, who are learning because they want to, not just because a course told them to.”

Certifications can also play a valuable role in retention, Watts says. “I encourage team members to pursue growth, and when they invest in their own development, the whole organization benefits,” he says. “But again, it’s that balance of knowledge, attitude, and applied experience that really moves the needle.”

Certifications “may allow you to breeze through the initial résumé screening process, potentially getting you to the next stage faster,” Riccio says. “At a minimum, they will set your profile apart from the rest of the pack. They also demonstrate that you’ve reached a baseline level of expertise, allowing hiring managers to quickly evaluate whether you have the skills for the role.”

Employers today “care far less about whether someone has passed an exam and far more about whether they can apply knowledge effectively in real-world situations, leverage AI tools, and solve complex problems,” Miller says. “A certification might get someone an interview, but being able to demonstrate problem-solving skills, teamwork, and adaptability will really make them stand out.”

Popular programming certifications

The following certifications consistently rose to the top in my conversations with tech leaders and hiring managers.

AWS Certified Developer—Associate

Showcases skills and knowledge in developing, optimizing, packaging, and deploying applications, using CI/CD workflows, and identifying and resolving application issues, according to AWS. This certification is said to be a good starting point on the AWS certification journey for professionals in IT or cloud developer job roles.

Azure Developer Associate

This certificate from Microsoft is intended for developers participating in all phases of cloud development, including design, deployment, maintenance, and monitoring. The course teaches developers how to create end-to-end solutions in Microsoft Azure, using the Microsoft Learn Sandbox environment to access Azure resources and services.

Certified Kubernetes Application Developer (CKAD)

This certification was created by the Linux Foundation and Cloud Native Computing Foundation. It demonstrates that candidates can design, build, and deploy cloud-native applications for Kubernetes.

Certified Secure Software Lifecycle Professional (CSSLP)

This certification, from ISC2, focuses on secure software development practices. It recognizes leading application security skills and demonstrates advanced technical skills and knowledge needed for authentication, authorization, and auditing throughout the software development lifecycle.

Databricks Certified Machine Learning Professional

Professionals learn about the latest data and AI techniques and how they can use the Databricks Data Intelligence Platform to build a variety of solutions across data engineering, data warehousing, data science, and AI.

Professional Cloud Architect

This certification from Google assesses the ability to design and plan a cloud solution architecture, manage and provision the cloud solution infrastructure, design for security and compliance, analyze and optimize technical and business processes manage implementations of cloud architecture, and ensure solution and operations reliability.

Terraform Associate

This certification from HashiCorp is for cloud engineers specializing in operations, IT, or development who know the basic concepts and skills associated with Terraform. It validates foundational skills in using Terraform for infrastructure as code development.

A portmanteau of “development” and “operations,” devops emerged as a way of bringing together two previously separate groups responsible for the building and deploying of software.

In the old world, developers (devs) typically wrote code before throwing it over to the system administrators (operations, or ops) to deploy and integrate that code. But as the industry shifted towards agile development and cloud-native computing, many organizations reoriented around modern, cloud-native practices in the pursuit of faster, better releases.

This required a new way to perform these key functions in a more streamlined, efficient, and cohesive way, one where the old frustrations of disconnected dev and ops functions would be eliminated. With two groups working together, developers can rapidly roll out small code enhancements via continuous integration and delivery rather than spending years on “big bang” product releases.

Devops was born at cloud-native companies like Facebook, Netflix, Spotify, and Amazon; but it’s become one of the defining technology industry trends of the past decade, primarily because it bridges so many of the changes that have shaped modern software development.

As agile development and cloud-native computing have become ubiquitous, devops has enabled the entire industry to speed up its software development cycles. Thus, devops has now thoroughly infiltrated the enterprise, especially in organizations that rely on software to run their business, such as banks, airlines, and retailers. And it’s spawned a host of other “ops” practices, some of which we’ll touch on here.[JF1] 

Devops practices

Devops requires a shift in mindset from both sides of the dev and ops divide. Development teams should focus on learning and adopting agile processes, standardizing platforms, and helping drive operational efficiencies. Operations teams must now focus on improving stability and velocity, while also reducing costs by working hand in hand with the developer team.

Broadly speaking, these teams need to all speak a common language and there needs to be a shared goal and understanding of each other’s key skills for devops to thrive.

More specifically, engineers Damon Edwards and John Willis created the CALMS model to bring together what are commonly understood to be the key principles of devops:

• Culture: One that embraces agile methodologies and is open to change, constant improvement, and accountability for the end-to-end quality of software.
• Automation: Automating away toil is a key goal for any devops team.
• Lean: Ensuring the smooth flow of software through key steps as quickly as possible.
• Measurement: You can’t improve what you don’t measure. Devops pushes for a culture of constant measurement and feedback that can be used to improve and pivot as required, on the fly.
• Sharing: Knowledge sharing across an organization is a key tenet of devops.

“Who could go back to the old way of trying to figure out how to get your laptop environment looking the same as the production environment? All these things make it so clear that there’s a better way to work. I think it’s very tough to turn back once you’ve done things like continuous integration, like continuous delivery. Once you’ve experienced it, it’s really tough to go back to the old way of doing things,” Kim told InfoWorld.

What is a devops engineer?

Naturally, the emergence of devops has spawned a whole new set of job titles, most prominent of which is the catch-all devops engineer.

Generally speaking, this role is the natural evolution of the system administrator — but in a world where developers and ops work in close tandem to deliver better software. This person should have a blend of programming and system administrator skills so that he or she can effectively bridge those two sides of the team.

That bridging of the two sides requires strong social skills more than technical. As Kim put it, “one of the most important skills, abilities, traits needed in these pioneering rebellions — using devops to overthrow the ancient powerful order, who are very happy to do things the way they have for 30 to 40 years — are the cross-functional skills to be able to reach across the table to their business counterparts and help solve problems.”

This person, or team of people, will also have to be a born optimizer, tasked with continually improving the speed and quality of software delivery from the team, be that through better practices, removing bottlenecks, or applying automation to smooth out software delivery.

The good news is that these skills are valuable to the enterprise. Salaries for this set of job titles have risen steadily over the years, with 95% of devops practitioners making more than $75,000 a year in salary in 2020 in the United States. In Europe and the UK, where salaries are lower across the board, 71% made more than $50,000 a year in 2020, up from 67% in 2019.

Key devops tools

While devops is at its heart a cultural shift, a set of tools has emerged to help organizations adopt devops practices.

This stack typically includes infrastructure as code, configuration management, collaboration, version control, continuous integration and delivery (CI/CD), deployment automation, testing, and monitoring tools.

Here are some of the tools/categories that are increasingly relevant in 2025, and what is changing:

• CI/CD and delivery automation: Traditional tools like Jenkins remain in many stacks, but newer orchestration tools and CLI-driven or GitOps-centric platforms are growing in importance (e.g. ArgoCD, Flux, Tekton). Also, platforms that integrate more tightly with monitoring, secrets management, drift detection, and policy enforcement are gaining traction.
• Security, compliance, and devsecops tooling: Security tools are increasingly integrated into devops pipelines. Expect to see more use of static analysis (SAST), dynamic testing (DAST), dependency and supply chain scanning (SCA), secret management, and policy as code. The push is toward embedding security earlier and bridging gaps between dev, security, and machine learning teams. (InfoWorld:)
• AI  and automation augmentation: AI-assisted tools are increasingly part of tooling stacks: auto-suggestions in CI/CD, anomaly detection, predictive scaling, intelligent test suite selection, and more. The hope is that these tools will reduce manual interventions and improve reliability. Tools that are “AI ready”—that is, they integrate well with AI or have mature built-in automation or assistance—increasingly stand out from the pack.

Devops challenges

Even as devops becomes more widely adopted, there remain real obstacles that can slow progress or limit impact. One major challenge is the persistent skills gap. The modern devops engineer (or team) is expected to master not just source control, CI/CD, and scripting, but also cloud architecture, infrastructure as code, security best practices, observability, and strong cross-team communication. In many organizations these capabilities are uneven: some teams excel, others lag behind. A 2024 survey showed that while 83% of developers report participating in devops activities, using multiple CI/CD tools was correlated with worse performance — a sign that complexity without deep expertise can backfire.

Toolchain fragmentation and complexity is a related issue. Devops toolchains have sprouted into a sometimes bewildering array of packages and techniques to master: version control, CI build/test, security scanning, artifact management, monitoring, observability, deployment, secret management, and more.

The more tools you have, the more difficult it becomes to integrate them cleanly, manage their versions, ensure compatibility, and avoid duplicated effort. Organizations often get stuck with “tool sprawl” — tools chosen by different teams, legacy systems, or overlapping functionalities — which introduce friction, maintenance burden, and sometimes vulnerabilities.

Finally, although devops has spread far and wide, there is still cultural resistance and alignment. Devops isn’t just about tools and processes; it’s about collaboration, shared responsibility, and continuous feedback. Teams rooted in traditional silos (dev vs ops, or security separate) may resist changes to roles and workflows. Leadership support, communication of shared goals, trust, and allowance for continuous learning are all necessary.

Many CIOs focus too much on tools or implementation first, rather than organizational culture and behaviors; but without addressing culture, even the best tools or processes may not yield the hoped-for velocity, quality, or reliability. Organizations that succeed here tend to have proactive strategies: dedicated training programs, mentorship, internal “guilds,” pairing junior and senior engineers, and making sure leadership supports ongoing learning rather than one-off bootcamps.

Why do devops?

Whoever you ask will tell you that devops is a major culture shift for organizations, so why go through that pain at all?

Devops aims to combine the formerly conflicting aims of developers and system administrators. Under its principles, all software development aims to meet business demands, add functionality, and improve the usability of applications while also ensuring those applications are stable, secure, and reliable. Done right, this improves the velocity and quality of your output, while also improving the lives of those working on these outcomes.

Does devops save money — or add cost?

Devops teams are recognizing that speed and agility are only part of success — unchecked cloud bills and waste undermine long-term sustainability. Waste in devops often comes in the form of “devops debt”— idle cloud capacity, dead code, or false-positive security alerts—which was called a “hidden tax on innovation” in recent Java-environment studies.

 Embedding finops practices can help fight these costs. Teams should shift left on cost: estimating costs when spinning up new environments, resizing instances, and scaling down unused resources before they become runaway expenses.

How to start with devops

There are lots of resources for help getting started with devops, including Kim’s own Devops Handbook, or you can enlist the help of external consultants. But you have to be methodical and focus on your people more than on the tools and technology you will eventually use if you want to ensure lasting buy-in across the business.

A proven route to achieving this is a “land and expand” strategy, where a small group starts by mapping key value streams and identifying a single product team or workload for trialing devops practices. If this team is successful in proving the value of the shift, you will likely start to get interest from other teams and from senior leadership.

If you are at the start of your devops journey, however, make sure you are prepared for the disruption a change like this can have on your organization, and keep your eye on the prize of building better, faster, stronger software.

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More on devops:

• Devops debt: The hidden tax on innovation
• 10 big devops mistakes and how to avoid them
• Smarter devops: How to avoid deployment horrors

What is cloud native? Cloud native defined

The term “cloud-native computing” encompasses the modern approach to building and running software applications that exploit the flexibility, scalability, and resilience of cloud computing. The phrase is a catch-all that encompasses not just the specific architecture choices and environments used to build applications for the public cloud, but also the software engineering techniques and philosophies used by cloud developers.

The Cloud Native Computing Foundation (CNCF) is an open source organization that hosts many important cloud-related projects and helps set the tone for the world of cloud development. The CNCF offers its own definition of cloud native:

Cloud native practices empower organizations to develop, build, and deploy workloads in computing environments (public, private, hybrid cloud) to meet their organizational needs at scale in a programmatic and repeatable manner. It is characterized by loosely coupled systems that interoperate in a manner that is secure, resilient, manageable, sustainable, and observable.

Cloud native technologies and architectures typically consist of some combination of containers, service meshes, multi-tenancy, microservices, immutable infrastructure, serverless, and declarative APIs — this list is not exhaustive.

This definition is a good start, but as cloud infrastructure becomes ubiquitous, the cloud native world is beginning to spread behind the core of this definition. We’ll explore that evolution as well, and look into the near future of cloud-native computing.

Cloud native architectural principles

Let’s start by exploring the pillars of cloud-native architecture. Many of these technologies and techniques were considered innovative and even revolutionary when they hit the market over the past few decades, but now have become widely accepted across the software development landscape.

Microservices. One of the huge cultural shifts that made cloud-native computing possible was the move from huge, monolithic applications to microservices: small, loosely coupled, and independently deployable components that work together to form a cloud-native application. These microservices can be scaled across cloud environments, though (as we’ll see in a moment) this makes systems more complex.

Containers and orchestration. In could-native architectures, individual microservices are executed inside containers — lightweight, portable virtual execution environments that can run on a variety of servers and cloud platforms. Containers insulate the developers from having to worry about the underlying machines on which their code will execute. That is, all they have to do is write to the container environment. 

Getting the containers to run properly and communicate with one another is where the complexity of cloud native computing starts to emerge. Initially, containers were created and managed by relatively simple platforms, the most common of which was Docker. But as cloud-native applications got more complex, container orchestration platforms that augmented Docker’s functionality emerged, such as Kubernetes, which allows you to deploy and manage multi-container applications at scale. Kubernetes is critical to cloud native computing as we know it — it’s worth noting that the CNCF was set up as a spinoff of the Linux Foundation on the same day that Kubernetes 1.0 was announced — and adhering to Kubernetes best practices is an important key to cloud native success. 

Open standards and APIs. The fact that containers and cloud platforms are largely defined by open standards and open source technologies is the secret sauce that makes all this modularity and orchestration possible, and standardized and documented APIs offer the means of communication between distributed components of a larger application. In theory, anyway, this standardization means that every component should be able to communicate with other components of an application without knowing about their inner workings, or about the inner workings of the various platform layers on which everything operates.

DevOps, agile methodologies, and infrastructure as code. Because cloud-native applications exist as a series of small, discrete units of functionality, cloud-native teams can build and update them using agile philosophies like DevOps, which promotes rapid, iterative CI/CD development. This enables teams to deliver business value more quickly and more reliably.

The virtualized nature of cloud environments also make them great candidates for infrastructure as code (IaC), a practice in which teams use tools like Terraform, Pulumi, and AWS CloudFormation, to manage infrastructure declaratively and version those declarations just like application code. IaC boosts automation, repeatability, and resilience across environments—all big advantages in the cloud world. IaC also goes hand-in-hand with the concept of immutable infrastructure—the idea that, once deployed, infastructure-level entities like virtual machines, containers, or network appliances don’t change, which makes them easier to manage and secure. IaC stores declarative configuration code in version control, which creates an audit log of any changes.

How the cloud-native stack is expanding

As cloud-native development becomes the norm, the cloud-native ecosystem is expanding; the CNCF maintains a graphical representation of what it calls the  cloud native landscape that hammers home to expansive and bewildering variety of products, services, and open source projects that contribute to (and seek to profit from) to cloud-native computing. And there are a number of areas where new and developing tools are complicating the picture sketched out by the pillars we discussed above.   

An expanding Kubernetes ecosystem. Kubernetes is complex, and teams now rely on an entire ecosystem of projects to get the most out of it: Helm for packaging, ArgoCD for GitOps-style deployments, and Kustomize for configuration management. And just as Kubernetes augmented Docker for enterprise-scale deployments. Kubernetes itself has been augmented and expanded by service mesh offerings like Istio and Linkerd, which offer fine-grained traffic control and improved security

Observability needs. The complex and distributed world of cloud-native computing requires in-depth observability to ensure that developers and admins have a handle on what’s happening with their applications. Cloud-native observability uses distributed tracing and aggregated logs to provide deep insight into performance and reliability. Tools like Prometheus, Grafana, Jaeger, and OpenTelemetry support comprehensive, real-time observability across the stack.

Serverless computing.  Serverless computing, particularly in its function-as-a-service guise, offers to strip needed compute resources down to their bare minimum, with functions running on service provider clouds using exactly as much as they need and no more. Because these services can be exposed as endpoints via APIs, they are increasingly integrated into distributed applications, operating side-by-side with functionality provided by containerized microservices. Watch out, though: the big FaaS providers (Amazon, Microsoft, and Google) would love to lock you in to their ecosystems.  

FinOps. Cloud computing was initially billed as a way to cut costs — no need to pay for an in-house data center that you barely use — but in practice it replaces capex with opex, and sometimes you can run up truly shocking cloud service bills if you aren’t careful. Serverless computing is one way to cut down on those costs, but financial operations, or FinOps, is a more systematic discipline that aims to aligns engineering, finance, and product to optimize cloud spending. FinOps best practices make use of those observability tools to best determine what departments and applications are eating up resources.

How cloud-native architecture is adapting to AI workloads

Enterprises deploy larger AI models and make use of more and more real-time inference services. That’s putting demands on cloud-native systems and forcing them to adapt to remain scalable and reliable.

For instance, organizations are re-engineering cloud environments around GPU-accelerated clusters, low-latency networking, and predictable orchestration. These needs align with established cloud-native patterns: containers package AI services consistently, while Kubernetes provides resilient scheduling and horizontal scale for inference workloads that can spike without warning.

Kubernetes itself is changing to better support AI inference, adding hardware-aware scheduling for GPUs, model-specific autoscaling behavior, and deeper observability into inference pipelines. These enhancements make Kubernetes a more natural platform for serving generative AI workloads.

AI’s resource demands are amplifying traditional cloud-native challenges. Observability becomes more complex as inference paths span GPUs, CPUs, vector databases, and distributed storage. FinOps teams contend with cost volatility from training and inference bursts. And security teams must track new risks around model provenance, data access, and supply-chain integrity.

Application frameworks for building distributed cloud-native apps

Microsoft’s Aspire is one of the most visible examples of a shift towards application frameworks to simplify how teams build distributed systems. Opinionated frameworks like Aspire provide structure, observability, and integration out of the box so developer don’t need to stitch together containers, microservices, and orchestration tooling by hand.

Aspire in particular is a prescriptive framework for cloud-native applications, bundling containerized services, environment configuration, health checks, and observability into a unified development model. Aspire provides defaults for service-to-service communication, configuration, and deployment, along with a built-in dashboard for visibility across distributed components.

While Aspire was originally aligned with Microsoft’s .NET platform,Redmond now sees it as having a  polyglot future. This positions Aspire as part of a broader trend: frameworks that help teams build cloud-native, service-oriented systems without being locked into a single language ecosystem. Several other frameworks are gaining traction: Dapr provides a portable runtime that abstracts many of the plumbing tasks in cloud-native distributed applications, and Orleans offers an actor-model-based framework for large-scale systems in the .NET world, and Akka gives JVM teams a mature, reactive toolkit for elastic, resilient services.

Frameworks and tools in the expanding cloud-native ecosystem

While frameworks like Aspire simplify how developers compose and structure distributed applications, most cloud-native systems still depend on a broader ecosystem of platforms and operational tooling. This deeper layer is where much of the complexity—and innovation—of cloud-native computing lives, particularly as Kubernetes continues to serve as the industry’s control plane for modern infrastructure.

Kubernetes provides the core abstractions for deploying and orchestrating containerized workloads at scale. Managed distributions such as Google Kubernetes Engine (GKE), Amazon EKS, Azure AKS, and Red Hat OpenShift build on these primitives with security, lifecycle automation, and enterprise support. Platform vendors are increasingly automating cluster operations—upgrades, scaling, remediation—to reduce the operational burden on engineering teams.

Surrounding Kubernetes is a rapidly expanding ecosystem of complementary frameworks and tools. Service meshes like Istio and Linkerd provide fine-grained traffic management, policy enforcement, and mTLS-based security across microservices. GitOps platforms such as Argo CD and Flux bring declarative, version-controlled deployments to cloud-native environments. Meanwhile, projects like Crossplane turn Kubernetes into a universal control plane for cloud infrastructure, letting teams provision databases, queues, and storage through familiar Kubernetes APIs. These tools illustrate how cloud-native development now spans multiple layers: developer-focused application frameworks like Aspire at the top, and a powerful, evolving Kubernetes ecosystem underneath that keeps modern distributed applications running.

Advantages and challenges for cloud-native development

Cloud native has become so ubiquitous that its advantages are almost taken for granted at this point, but it’s worth reflecting on the beneficial shift the cloud native paradigm represents. Huge, monolithic codebases that saw updates rolled out once every couple of years have been replaced by microservice-based applications that can be improved continuously. Cloud-based deployments, when managed correctly, make better use of compute resources and allow companies to offer their products as SaaS or PaaS services. 

But cloud-native deployments come with a number of challenges, too:

• Complexity and operational overhead: You’ll have noticed by now that many of the cloud-native tools we’ve discussed, like service meshes and observability tools, are needed to deal with the complexity of cloud-native applications and environments. Individual microservices are deceptively simple, but coordinating them all in a distributed environment is a big lift.
• Security: More services executing on more machines, communicating by open APIs, all adds up to a bigger attack surface for hackers. Containers and APIs each have their own special security needs, and a policy engine can be an important tool for imposing a security baseline on a sprawling cloud-native app. DevSecOps, which adds security to DevOps, has become an important cloud-native development practice to try to close these gaps.
• Vendor lock-in: This may come as a surprise, since cloud-native is based on open standards and open source. But there are differences in how the big cloud and serverless providers works, and once you’ve written code with one provider in mind, it can be hard to migrate elsewhere.
• A persistent skills gap: Cloud-native computing and development may have years under its belt at this point, but the number of developers who are truly skilled in this arena is a smaller portion of the workforce than you’d think. Companies face difficult choices in bridging this skills gap, whether that’s bidding up salaries, working to upskill current workers, or allowing remote work so they can cast a wide net. 

Cloud native in the real world

Cloud native computing is often associated with giants like Netflix, Spotify, Uber, and AirBNB, where many of its technologies were pioneered in the early ’10s. But the CNCF’s Case Studies page provides an in-depth look at how cloud native technologies are helping companies. Examples include the following:

• A UK-based payment technology company that can switch between data centers and clouds with zero downtime
• A software company whose product collects and analyzes data from IoT devices — and can scale up as the number of gadgets grows
• A Czech web service company that managed to improve performance while reducing costs by migrating to the cloud

Cloud-native infrastructure’s capability to quickly scale up to large workloads also make it an attractive platform for developing AI/ML applications: another one of those CNCF case studies looks at how IBM uses Kubernetes to train its Watsonx assistant. The big three providers are putting a lot of effort into pitching their platforms as the place for you to develop your own generative AI tools, with offerings like Azure AI Foundry,Google Firebase Studio, and Amazon Bedrock. It seems clear that cloud native technology is ready for what comes next.

Learn more about related cloud-native technologies:

• Platform-as-a-service (PaaS) explained
• What is cloud computing
• Multicloud explained
• Agile methodology explained
• Agile development best practices
• Devops explained
• Devops best practices
• Microservices explained
• Microservices tutorial
• Docker and Linux containers explained
• Kubernetes tutorial
• CI/CD (continuous integration and continuous delivery) explained
• CI/CD best practices

Cloud computing continues to be the platform of choice for large applications and a driver of innovation in enterprise technology. Gartner forecasts public cloud spending alone to the public cloud services market alone will reach $1.42 trillion in current U.S. dollars, driven by AI workloads and enterprise modernization.

Driving this growth are the rise of AI and machine learning on the cloud, adoption of edge computing, the maturation of serverless computing, the emergence of multicloud strategies, improved security and privacy, and more sustainable cloud practices.

What is cloud computing?

While often used broadly, the term cloud computing is defined as an abstraction of compute, storage, and network infrastructure assembled as a platform on which applications and systems are deployed quickly and scaled on the fly.

Most cloud customers consume public cloud computing services over the internet, which are hosted in large, remote data centers maintained by cloud providers. The most common type of cloud computing, SaaS (software as service), delivers prebuilt applications to the browsers of customers who pay per seat or by usage, exemplified by such popular apps as Salesforce, Google Docs, or Microsoft Teams.

5 top trends in cloud computing

1. Agentic cloud ecosystems: The shift from AI as a tool to AI as an autonomous operator within cloud environments.
2. Sovereign and localized clouds: Meeting strict national data residency and digital sovereignty laws.
3. Specialized AI hardware access: Navigating the GPU capacity crunch through reserved instances and boutique AI clouds.
4. Integrated greenOps: Merging cost optimization with mandatory carbon-footprint reporting.
5. Industry-specific walled gardens: The maturation of vertical clouds into highly regulated, precompliant environments for finance and healthcare.

Next in line is IaaS (infrastructure as a service), which offers vast, virtualized compute, storage, and network infrastructure upon which customers build their own applications, often with the aid of providers’ API-accessible services.

When people refer to the “the cloud” today, they most often mean the big IaaS providers: AWS (Amazon Web Services), Google Cloud Platform, or Microsoft Azure. All three have become ecosystems of services that go way beyond infrastructure and include developer tools, serverless computing, machine learning services and APIs, data warehouses, and thousands of other services. With both SaaS and IaaS, a key benefit is agility. Customers gain new capabilities almost instantly without the capital investment in hardware or software on-premises — and they can instantly scale the cloud resources they consume up or down as needed.

According to Foundry’s Cloud Computing Study, 2025, enterprises are moving to the cloud to improve security and/or governance, increase scalability​, accelerate adoption of artificial intelligence and machine learning and other new technologies, replace on-premises legacy technology, ​improve employee productivity, and ensure disaster recovery and business continuity.

Hyperscalers now dominate cloud services

The largest cloud service providers are often described as hyperscalers, due to their capability to provide large-scale data centers across the globe. Hyperscalers typically offer a wide range of cloud services, including IaaS, PaaS, SaaS, and more.

As mentioned above, notable hyperscalers include Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure. They offer the following capabilities.

• Scalability: Hyperscalers can handle massive workloads and scale resources up or down quickly.
• Cost-effectiveness: Hyperscalers often offer competitive pricing and economies of scale.
• Global reach: Hyperscalers operate data centers around the world, providing low-latency access to customers in different regions.
• Innovation: Hyperscalers are at the forefront of cloud innovation, offering new services and features.

Challenges of working with hyperscalers

• Vendor lock-in: Relying heavily on a single hyperscaler can create vendor lock-in, making it difficult to switch to another provider and charging large egress fees if you do move.
• Complexity: Hyperscalers offer a vast array of services, which can be overwhelming for some customers.
• Security concerns: Because hyperscalers handle sensitive data, security is a major concern.

AI, Agents, and the Sovereign Cloud

The AI-enabled enterprise has moved beyond simple chatbots. The focus has shifted to agentic workflows — autonomous systems that reside in the cloud and possess the authority to execute business processes, manage cloud spend, and self-patch security vulnerabilities without human intervention.

The shift to agentic infrastructure

Cloud providers are no longer just selling compute. They are selling inference-as-a-service. Modern cloud budgets are now dominated by the high cost of specialized GPU clusters (such as Nvidia’s Blackwell architecture). This has led to the rise of boutique AI clouds that compete with hyperscalers by offering bare-metal access to the latest silicon specifically for model training and fine-tuning.

Data sovereignty and private AI

A major shift in late 2025 is the move away from public AI models for sensitive data. Organizations are increasingly using retrieval-augmented generation (RAG) within walled garden environments. This ensures that a company’s proprietary data never leaves their specific cloud instance to train a provider’s base model.

Furthermore, sovereign AI has become a requirement for global operations. Governments now demand that the AI models processing their citizens’ data be hosted on infrastructure that is owned, operated, and governed within their own borders.

The challenges of ghost AI

Just as shadow IT plagued the 2010s, ghost AI—unauthorized AI agents running on corporate cloud accounts — has become a primary security risk. Managing these autonomous entities requires a new layer of AI governance, where the cloud provider automatically audits the intent and permissions of every running agent to prevent runaway costs or data leaks.

Cloud computing definitions

In 2011, NIST posted a PDF that divided cloud computing into three “service models” — SaaS, IaaS, and PaaS (platform as a service) — the latter being a controlled environment within which customers develop and run applications. These three categories have largely stood the test of time, although most PaaS solutions now are made available as services within IaaS ecosystems rather than as dedicated PaaS clouds.

Two evolutionary trends stand out since NIST’s threefold definition. One is the long and growing list of subcategories within SaaS, IaaS, and PaaS, some of which blur the lines between categories. The other is the explosion of API-accessible services available in the cloud, particularly within IaaS ecosystems. The cloud has become a crucible of innovation where many emerging technologies appear first as services, a big attraction for business customers who understand the potential competitive advantages of early adoption.

SaaS (software as a service) definition

This type of cloud computing delivers applications over the internet, typically with a browser-based user interface. Today, most software companies offer their wares via SaaS — if not exclusively, then at least as an option.

The most popular SaaS applications for business are Google’s G Suite and Microsoft’s Office 365. Most enterprise applications, including giant ERP suites from Oracle and SAP, come in both SaaS and on-premises versions. SaaS applications typically offer extensive configuration options as well as development environments that enable customers to code their own modifications and additions. They also enable data integration with on-prem applications.

IaaS (infrastructure as a service) definition

At a basic level, IaaS cloud providers offer virtualized compute, storage, and networking over the internet on a pay-per-use basis. Think of it as a data center maintained by someone else, remotely, but with a software layer that virtualizes all those resources and automates customers’ ability to allocate them with little trouble.

But that’s just the basics. The full array of services offered by the major public IaaS providers is staggering: highly scalable databases, virtual private networks, big data analytics, AI and machine learning services, application platforms, developer tools, devops tools, and so on. Amazon Web Services was the first IaaS provider and remains the leader, followed by Microsoft Azure, Google Cloud Platform, IBM Cloud, and Oracle Cloud.

PaaS (platform as a service) definition

PaaS provides sets of services and workflows that specifically target developers, who can use shared tools, processes, and APIs to accelerate the development, testing, and deployment of applications. Salesforce’s Heroku and Salesforce Platform (formerly Force.com) are popular public cloud PaaS offerings; Cloud Foundry and Red Hat’s OpenShift can be deployed on premises or accessed through the major public clouds. For enterprises, PaaS can ensure that developers have ready access to resources, follow certain processes, and use only a specific array of services, while operators maintain the underlying infrastructure.

FaaS (function as a service) definition

FaaS, the original and most basic version of serverless computing, adds another layer of abstraction to PaaS, so that developers are insulated from everything in the stack below their code. Instead of futzing with virtual servers, containers, and application runtimes, developers upload narrowly functional blocks of code, and set them to be triggered by a certain event (such as a form submission or uploaded file). All of the major clouds offer FaaS on top of IaaS: AWS Lambda, Azure Functions, Google Cloud Functions, and IBM Cloud Functions. A special benefit of FaaS applications is that they consume no IaaS resources until an event occurs, reducing pay-per-use fees.

Private cloud definition

A private cloud downsizes the technologies used to run IaaS public clouds into software that can be deployed and operated in a customer’s data center. As with a public cloud, internal customers can provision their own virtual resources to build, test, and run applications, with metering to charge back departments for resource consumption. For administrators, the private cloud amounts to the ultimate in data center automation, minimizing manual provisioning and management.

VMware remains a force in the private cloud software market, but the acquisition by Broadcom has created confusion and raised concerns among some customers about potential changes in pricing, licensing, and support. This could lead some organizations to explore alternative solutions.

OpenStack continues to be a popular open-source choice for building private clouds. It offers a flexible and customizable platform that can be tailored to specific needs. However, OpenStack can be complex to deploy and manage, and it may require significant expertise to maintain.

Kubernetes, a container orchestration platform that has gained significant traction in recent years, is often used in conjunction with other technologies like OpenStack to build cloud-native applications. Red Hat OpenShift is a comprehensive cloud platform based on Kubernetes that provides a managed experience for deploying and managing container-based, applications.

Many cloud providers offer their own cloud-native platforms and tools, such as AWS Outposts, Azure Stack, and Google Cloud Anthos.

Common factors to consider when evaluating private cloud platforms include the following:

1. Pricing: The initial cost of deployment and ongoing maintenance costs.
2. Complexity: The level of technical expertise needed to manage the platform.
3. Flexibility: The ability to customize the platform to meet specific needs.
4. Vendor lock-in: The degree to which the organization is tied to a particular vendor.
5. Security: The security features and capabilities of the platform.
6. Scalability: The capability to expand the platform to meet future needs.

Hybrid cloud definition

A hybrid cloud is the integration of a private cloud with a public cloud. At its most developed, the hybrid cloud involves creating parallel environments in which applications can move easily between private and public clouds. In other instances, databases may stay in the customer data center and integrate with public cloud applications — or virtualized data center workloads may be replicated to the cloud during times of peak demand. The types of integrations between private and public clouds vary widely, but they must be extensive to earn a hybrid cloud designation.

Public APIs (application programming interfaces) definition

Just as SaaS delivers applications to users over the internet, public APIs offer developers application functionality that can be accessed programmatically. For example, in building web applications, developers often tap into the Google Maps API to provide driving directions; to integrate with social media, developers may call upon APIs maintained by Twitter, Facebook, or LinkedIn. Twilio has built a successful business delivering telephony and messaging services via public APIs. Ultimately, any business can provision its own public APIs to enable customers to consume data or access application functionality.

iPaaS (integration platform as a service) definition

Data integration is a key issue for any sizeable company, but particularly for those that adopt SaaS at scale. iPaaS providers typically offer prebuilt connectors for sharing data among popular SaaS applications and on-premises enterprise applications, though providers may focus more or less on business-to-business and e-commerce integrations, cloud integrations, or traditional SOA-style integrations. iPaaS offerings in the cloud from such providers as Dell Boomi, Informatica, MuleSoft, and SnapLogic also let users implement data mapping, transformations, and workflows as part of the integration-building process.

IDaaS (identity as a service) definition

The most difficult security issue related to cloud computing is managing user identity and its associated rights and permissions across data centers and pubic cloud sites. IDaaS providers maintain cloud-based user profiles that authenticate users and enable access to resources or applications based on security policies, user groups, and individual privileges. The ability to integrate with various directory services (Active Directory, LDAP, etc.) and provide single sign-on across business-oriented SaaS applications is essential.

Leaders in IDaaS include Microsoft, IBM, Google, Oracle, Okta, Capgemini, Okta, Junio Corporation, OneLogin, and JumpCloud.  

Collaboration platforms

Collaboration solutions such as Slack and Microsoft Teams have become vital messaging platforms that enable groups to communicate and work together effectively. Basically, these solutions are relatively simple SaaS applications that support chat-style messaging along with file sharing and audio or video communication. Most offer APIs to facilitate integrations with other systems and enable third-party developers to create and share add-ins that augment functionality.

Vertical clouds

Key providers in such industries as financial services, healthcare, retail, life sciences, and manufacturing provide PaaS clouds to enable customers to build vertical applications that tap into industry-specific, API-accessible services. Vertical clouds can dramatically reduce the time to market for vertical applications and accelerate domain-specific B2B integrations. Most vertical clouds are built with the intent of nurturing partner ecosystems.

Other cloud computing considerations

The most widely accepted definition of cloud computing means that you run your workloads on someone else’s servers, but this is not the same as outsourcing. Virtual cloud resources and even SaaS applications must be configured and maintained by the customer. Consider these factors when planning a cloud initiative.

Cloud computing security considerations

Objections to the public cloud generally begin with cloud security, although the major public clouds have proven themselves much less susceptible to attack than the average enterprise data center.

Of greater concern is the integration of security policy and identity management between customers and public cloud providers. In addition, government regulation may forbid customers from allowing sensitive data off-premises. Other concerns include the risk of outages and the long-term operational costs of public cloud services.

Multicloud management considerations

To enhance their operational efficiency, reduce costs, and improve security, many companies are increasingly turning to multicloud strategies. By distributing workloads across multiple cloud providers, organizations can avoid vendor lock-in, optimize costs, and leverage the best-of-breed services offered by different providers.

This multicloud approach also improves performance and reliability by minimizing downtime and optimizing latency. Additionally, multicloud strategies strengthen security by diversifying the attack surface and facilitating compliance with industry regulations. Finally, by replicating critical workloads across multiple regions and providers, companies can establish robust disaster recovery and business continuity plans, ensuring minimal disruption in the event of catastrophic failures.

The bar to qualify as a multicloud adopter is low: A customer just needs to use more than one public cloud service. However, depending on the number and variety of cloud services involved, managing multiple clouds can become complex from both a cost optimization and a technology perspective.

In some cases, customers subscribe to multiple cloud services simply to avoid dependence on a single provider. A more sophisticated approach is to select public clouds based on the unique services they offer and, in some cases, integrate them. For example, developers might want to use Google’s Vertex AI Studio on Google Cloud Platform to build AI-driven applications, but prefer Jenkins hosted on the CloudBees platform for continuous integration.

To control costs and reduce management overhead, some customers opt for cloud management platforms (CMPs) and/or cloud service brokers (CSBs), which let you manage multiple clouds as if they were one cloud. The problem is that these solutions tend to limit customers to such common-denominator services as storage and compute, ignoring the panoply of services that make each cloud unique.

Edge computing considerations

You often see edge computing incorrectly described as an alternative to cloud computing. Edge computing is about moving compute to local devices in a highly distributed system, typically as a layer around a cloud computing core. There is typically a cloud involved to orchestrate all of the devices and take in their data, then analyze it or otherwise act on it. 

To the cloud and back – why repatriation is real

While public cloud offers scalability and flexibility, some enterprises are opting to return to on-premises infrastructure due to rising costs, data security concerns, performance issues, vendor lock-in, and regulatory compliance challenges. While the public cloud offers scalability and flexibility, on-premises infrastructure provides greater control, customization, and potential cost savings in certain scenarios leading some technology decision-makers to consider repatriation. However, a hybrid cloud approach, combining public and private cloud, often offers the best balance of benefits.

More specific reasons to repatriate including the following:

• Unanticipated costs, such as data transfer fees, storage charges, and egress fees, can quickly escalate, especially for large-scale cloud deployments.  
• Inaccurate resource provisioning or underutilization can lead to higher-than-expected costs.
• Stricter data privacy regulations require organizations to store and process data within specific geographic boundaries.  
• For highly sensitive data, companies may prefer to maintain greater control over security measures and access permissions. 
• On-premises infrastructure can offer lower latency, particularly for applications requiring real-time processing or high-performance computing.  
• Overreliance on a single cloud provider can limit flexibility and increase costs. Repatriation allows organizations to diversify their infrastructure and reduce vendor dependency.  
• Industries with stringent compliance requirements may find it easier to meet standards with on-premises infrastructure.  
• On-premises environments offer greater control over hardware, software, and network configurations, allowing for customized solutions.  

Benefits of cloud computing

The cloud’s main appeal is to reduce the time to market of applications that need to scale dynamically. Increasingly, however, developers are drawn to the cloud by the abundance of advanced new services that can be incorporated into applications, from machine learning to internet of things (IoT) connectivity.

Although businesses sometimes migrate legacy applications to the cloud to reduce data center resource requirements, the real benefits accrue to new applications that take advantage of cloud services and “cloud native” attributes. The latter include microservices architecture, Linux containers to enhance application portability, and container management solutions such as Kubernetes that orchestrate container-based services. Cloud-native approaches and solutions can be part of either public or private clouds and help enable highly efficient devops workflows.

Cloud computing, be it public or private or hybrid or multicloud, has become the platform of choice for large applications, particularly customer-facing ones that need to change frequently or scale dynamically. More significantly, the major public clouds now lead the way in enterprise technology development, debuting new advances before they appear anywhere else. Workload by workload, enterprises are opting for the cloud, where an endless parade of exciting new technologies invite innovative use.

SaaS has its roots in the ASP (application service provider) trend of the early 2000s, when providers would run applications for business customers in the provider’s data center, with dedicated instances for each customer. The ASP model was a spectacular failure because it quickly became impossible for providers to maintain so many separate instances, particularly as customers demanded customizations and updates.

Salesforce is widely considered the first company to launch a highly successful SaaS application using multitenancy — a defining characteristic of the SaaS model. Rather than each Salesforce customer getting its own application instance, customers who subscribe to the company’s salesforce automation software share a single, large, dynamically scaled instance of an application (like tenants sharing an apartment building), while storing their data in separate, secure repositories on the SaaS provider’s servers. Fixes can be rolled out behind the scenes with zero downtime and customers can receive UX or functionality improvements as they become available.

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery.

The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

While Kubernetes and similar platforms simplify many aspects of running distributed applications, operating these systems at scale is still complicated. Configuration sprawl, environment drift, and the need for rapid, reliable change all introduce operational challenges. GitOps emerged as a way to address those challenges by extending familiar devops and CI/CD techniques beyond application code and into infrastructure and system configuration.

At the heart of GitOps is the concept of infrastructure as code (IaC). In a GitOps model, not only application code but also infrastructure definitions, deployment configurations, and operational settings are described in files stored in a version control system. Automated processes continuously compare the running system with those declarations and work to bring the live environment back into alignment when differences appear.

In this approach, the version control repository serves as the system of record for how applications and their supporting infrastructure should look in production. Changes flow through the same review, approval, and automation pipelines that developers already use for software, bringing greater consistency, traceability, and repeatability to cloud-native operations.

At a high level, GitOps refers to a set of operational practices for managing cloud-native systems using declarative configuration, version control, and automated reconciliation. Rather than treating infrastructure and application configuration as mutable runtime state, GitOps treats them as versioned artifacts that move through the same review, testing, and deployment processes as application code.

GitOps defined

The term GitOps was originally coined and popularized by Weaveworks, which helped formalize the approach in the context of Kubernetes operations. While that early work shaped the way GitOps was discussed and implemented, GitOps has since evolved into a broadly adopted, vendor-neutral pattern. Today, it describes a shared set of ideas rather than a specific product or platform.

The defining characteristic of GitOps is its reliance on declarative configuration stored in a version control system. Instead of issuing imperative commands to change live systems, teams describe the desired state of applications and infrastructure in configuration files. Automated agents then continuously compare that declared state with what is actually running and work to reconcile any differences. This pull-based model—where systems converge toward the desired state defined in version control—provides built-in drift detection, repeatability, and a clear audit trail for every change.

Because GitOps centers on configuration files stored in a version control system, familiar software development practices carry over naturally. Changes are proposed through commits, reviewed before being accepted, and tracked over time. Rollbacks are accomplished by reverting to known-good versions, and the history of how a system evolved is preserved alongside the configuration itself.

While the use of Git as the version control system is not strictly required, it has become the default choice because of its ubiquity in modern devops workflows and its strong support for collaboration and change management, so its place in the name has stuck.

What is the CI/CD process?

A complete look at CI/CD is beyond the scope of this article—see the InfoWorld explainer on the subject—but we need to say a few words about CI/CD because it’s at the core of how GitOps works. The continuous integration half of CI/CD is enabled by version control repositories like Git: Developers can make constant small improvements to their codebase, rather than rolling out huge, monolithic new versions every few months or years. The continuous deployment piece is made possible by automated systems called pipelines that build, test, and deploy the new code to production.

Again, we keep talking about code here, and that usually summons up visions of executable code written in a programming language such as C or Java or JavaScript. But in GitOps, the “code” we’re managing is largely made up of configuration files. This isn’t just a minor detail — it’s at the heart of what GitOps does. These config files are, as we’ve said, the “single source of truth” describing what our system should look like. They are declarative rather than instructive. That means that instead of saying “start up ten servers,” the configuration file will simply say, “this system includes ten servers.”

GitOps and Kubernetes

GitOps first took hold in the Kubernetes ecosystem, where declarative configuration and continuous reconciliation are core design principles. As a result, Kubernetes remains the most common and best-understood environment for applying GitOps practices. A typical GitOps-driven update process for a Kubernetes application looks like this:

1. A developer proposes a change by committing updated application code or configuration to a version control repository, usually through a pull request.
2. That change is reviewed and approved, then merged into the main branch.
3. The merge triggers an automated CI/CD pipeline that tests the change, builds new artifacts if needed, and publishes them to a registry.
4. A GitOps controller or similar automated agent detects the updated desired state stored in version control.
5. The controller compares that desired state with the current state of the Kubernetes cluster and applies the necessary changes to bring the cluster back into alignment.

This pull-based reconciliation loop—where the cluster continuously converges toward the desired state defined in version control—is central to how GitOps works in practice. While Kubernetes provides a natural fit for this model, it represents just one canonical use case. The same patterns increasingly apply to infrastructure provisioning, policy enforcement, and multi-cluster operations beyond Kubernetes itself.

GitOps tooling in practice: Argo CD, Flux, and the ecosystem

GitOps is enabled by a set of tools that embody the principles we’ve outlined, with some open-source projects emerging as de facto standards in cloud-native environments.

At the center of the GitOps ecosystem is Argo CD, an open-source controller that continuously monitors a version control repository and ensures that the state of running systems matches the declared desired state. Argo CD is widely used in Kubernetes environments because it directly implements pull-based reconciliation: it compares the desired state stored in Git with the cluster’s actual state and applies changes to correct any drift.

Alongside Argo CD, Flux is another prominent open source GitOps engine. Both Flux and Argo CD help teams adopt GitOps workflows by managing the synchronization loop between code and runtime, but they differ in operational philosophy, integration surfaces, and ecosystem fit.

GitOps tooling often appears as part of broader platforms or integrated stacks rather than as isolated utilities. For example, multicloud and cluster management solutions now routinely include GitOps support, with Argo CD or compatible controllers bundled alongside deployment, policy, and governance capabilities.

In addition to Flux and Argo CD, a range of auxiliary tools contribute to a complete GitOps ecosystem: policy as code engines (e.g., Open Policy Agent), drift detection systems, and infrastructure provisioning tools that mesh with Git-centric workflows.

GitOps, devops, and normalization

GitOps grew out of the same forces that drove devops into mainstream IT practice, and in its early days, GitOps was often discussed as a distinct extension of devops, specifically tailored to managing declarative infrastructure and Kubernetes-centric systems. At the time, GitOps was still relatively new and not yet widely adopted outside cloud-native pioneers.

Over the last several years, however, GitOps practices have become deeply woven into how teams operate modern cloud environments. Rather than being treated as an optional add-on or marketing term, the core ideas of GitOps — using version-controlled, declarative configuration and automated reconciliation loops to continuously align running systems with intended state — are now part of standard operational practice in many Kubernetes-centric shops. In this sense, GitOps has shifted from a buzzword about what might be possible to a baseline pattern for cloud-native operations, much like devops itself did years earlier.

In environments where Kubernetes and declarative systems are the norm, GitOps workflows are the default way teams manage and deploy change. Many organizations now implement these patterns without explicitly calling them “GitOps,” just as few teams today explicitly say they do “CI/CD” even though continuous pipelines are taken for granted. The term has become less prominent in marketing, but its practices are often embedded in pipelines, controllers, and platform tooling.

That normalization shows up in how GitOps workflows are woven into broader operational frameworks. For example, platform engineering teams frequently build internal developer platforms that encapsulate GitOps patterns behind standardized developer APIs, making the pattern invisible to most application teams while still providing the auditability and automation that GitOps promises.

GitOps beyond Kubernetes: infrastructure, policy, and drift

While GitOps first gained traction as a way to manage Kubernetes deployments, its core principles apply broadly to infrastructure and operational concerns beyond any single orchestration platform. GitOps treats desired state as declarative configuration stored in version control and uses automated reconciliation to ensure running systems align with that state. That pattern naturally extends to infrastructure provisioning, policy enforcement, configuration drift detection, and governance workflows across diverse environments.

In modern operational stacks, infrastructure is increasingly defined declaratively, whether through Kubernetes manifests, Terraform modules, or other infrastructure-as-code formats. Storing these declarations in version control enables the same peer-review, auditability, and rollback practices developers already use for application code. Automated tooling then continuously detects when the live infrastructure diverges from the declared state and works to bring it back into alignment, reducing the risk of configuration drift and inadvertent misconfigurations.

Configuration drift — the state where an environment has diverged from what’s declared in version control — remains a major operational headache, especially in complex, dynamic systems. Drift can arise from ad hoc fixes, emergency updates, or manual changes made outside normal pipelines, and it can lead to inconsistencies, outages, and security gaps. By continually checking running systems against the desired state in Git and reconciling deviations automatically, GitOps workflows help teams keep environments predictable and auditable.

Policy enforcement and compliance are another natural extension of GitOps patterns. As organizations adopt declarative practices, policy-as-code engines and drift detection systems can be woven into GitOps pipelines to validate that proposed configurations meet security, compliance, or operational standards before they’re ever applied to running systems. Embedding policy checks into declarative workflows brings consistency to governance while preserving the automation and speed that devops teams expect.

GitOps – beyond Kubernetes

GitOps began as a way to bring devops discipline to Kubernetes operations, but its longer-term impact has been more subtle. In many ways, it’s been absorbed into the fabric of modern cloud-native operations, where declarative configuration, version control, and automated reconciliation are taken for granted. Today, GitOps is less about a specific set of tools or a named practice and more about an operational mindset. By treating infrastructure and configuration as versioned, auditable artifacts and relying on automation to enforce consistency, GitOps helps teams manage complexity at scale. Even as the term itself fades from the spotlight, the practices it introduced continue to shape how distributed systems are built, deployed, and operated.

While many technology companies have slowed hiring or even launched significant layoffs, that doesn’t mean job opportunities have dried up for software developers. In fact, skilled developers—particularly those with knowledge of AI—are in demand in other industries.

The key to success for developers looking to snatch up these roles is to be well-prepared to meet the needs of potential employers in a variety of sectors.

“The demand for developers in non-tech sectors is real and growing, but the roles look different from what you’d find at a software company,” says Pragati Awasthi, assistant teaching professor of AI and data science at Drexel University.

“Across all these sectors, the common thread is that software is no longer a support function; it is embedded in core operations,” Awasthi says. “The developer in these environments is often the person translating domain-specific business problems into technical solutions, which requires a different profile than a pure product engineer at a tech firm.”

Opportunity knocks

The tech industry has long been a mainstay as far as employing software developers. But as these businesses trim staffs in efforts to cut expenses, that has impacted the hiring landscape. Even as the tech sector scales back, however, companies in industries such as financial services/fintech, healthcare/healthtech, retail/ecommerce, and manufacturing are looking to acquire programming talent.

“The unifying factor is data complexity,” Awasthi says. “These industries generate large volumes of sensitive, regulated, or operationally critical data, and they need developers who can build and maintain systems that handle it responsibly.”

While recruiting firm Summit Search Group has placed developers in roles with technology companies, “it is just as common to recruit them for roles outside this niche,” says Matt Erhard, managing partner at the company. “There are actually a fairly wide variety of roles available for developers in industries beyond tech,” Erhard says.

For example, in financial services Summit Search Group has seen significant hiring for back-end and data engineers who can build and maintain fraud detection systems, digital banking platforms, and regulatory tools, Erhard says. In healthcare, companies are hiring developers to build AI-driven diagnostics platforms and patient portals, or to work with systems that manage electronic health records, he says.

In manufacturing and industrial companies, developers are needed for systems integration and embedded software related to predictive maintenance, Internet of Things (IoT) systems, and smart factories. And in retail and ecommerce, there’s strong demand for full-stack developers and data developers who can handle logistics systems, omni-channel platforms, and personalization engines, Erhard says.

“One significant function where we’ve been placing developer talent lately is in developing business systems and internal applications,” Erhard says. These roles often have titles such as systems engineer or application developer, and professionals are hired to handle tasks such as customizing customer relationship management (CRM) or enterprise resource planning (ERP) platforms, building workflow automation tools or modernizing legacy systems, he says.

Other core functions for which Summit Search Group has placed a lot of developers include data, analytics, and AI-enablement. “That could be directly involved with data engineering or in building tools like reporting systems and ETL [extract, transform, load] pipelines,” Erhard says.

The firm also has handled searches for developers who can build and maintain customer-facing products for banking, healthcare, and retail companies, such as mobile apps or digital platforms customers can use to interact with companies.

Randstad Digital, a provider of global technology talent, sees demand for roles including web developers, system developers, and app developers. “These professionals would work on anything from customer-facing platforms to internal tools,” says Michael Morris, global head of platform and talent at the company. “Non-tech companies are also often hiring roles like software architecture and devops to help scale existing technology. These involve being more ingrained in the business, like building a supply chain system for a retailer, rather than creating individual tech products like you would at a technology company.”

Prep for success

To increases the chances of success at landing developer jobs outside of the tech industry, development professionals would be wise to follow some good practices.

Boost AI skills

One best practice is to boost skills in using AI-powered tools and get familiar with all things AI.

“Get fluent with AI-assisted development and its limits,” Awasthi says. “This is not optional. Organizations across every sector expect developers to use AI coding tools productively. But the more durable skill is knowing when AI output is wrong, incomplete, or unsuitable for a regulated context. That critical evaluation capacity is what non-tech employers are increasingly trying to hire.”

AI does not necessarily replace the need for human developers so much as it changes the skills profile for those roles, Erhard says. “The biggest difference in recent years is that AI literacy is now a non-negotiable,” he says. “At minimum, developers today need to understand concepts like prompt engineering and how to use AI tools to improve their efficiency.”

One thing many job candidates don’t expect is that the rise of AI has also increased the importance of high-level skills such as problem framing, system design, and cross-functional communication,” Erhard says. “Essentially, if something is related to development but too complex or nuanced for an AI to handle effectively, then the demand is high for human developers who have that expertise,” he says.

Candidates who land roles consistently have experience building AI-augmented workflows along with standard coding skills, Erhard says. “Employers increasingly expect to hire developers who can leverage AI, so demonstrating this experience on your résumé can be very beneficial,” he says.

Gain domain knowledge

Summit Search Group is seeing high demand for developers with deep domain knowledge in an organization’s specific industry. “So, for instance, if someone is both an experienced developer and has expertise in healthcare compliance, or financial regulations, then those candidates tend to be very sought after,” Erhard says.

Domain fluency is an underrated skill, Awasthi says. “A developer who understands healthcare compliance, financial regulation, or manufacturing process logic is significantly harder to replace than one who only writes clean code,” she says. “AI can generate boilerplate. It cannot navigate a HIPAA audit or explain a model’s output to a compliance officer.”

Development professionals should “pick an industry and learn it seriously; not just the technology stack but the regulatory environment, the business model, and the actual problems practitioners face,” Awasthi says. “A developer who has read about HIPAA, or spent time understanding credit risk, is immediately more valuable in those hiring contexts.”

It’s also vital to demonstrate real-world, practical application of skills, not just credentials. “The strongest candidates have projects in their portfolio that directly tie to and solve real business problems,” Erhard says.

Acquire soft skills

And then there are the soft skills that are becoming more of a differentiator than they were in the past. As AI handles more routine coding, human developers are expected to make more architectural decisions and collaborate across departments, Erhard says. “Strong communication and problem-solving skills are critical for many of the developer roles that we’re filling today,” he says.

While technical skills are still relevant for developers using and managing AI tools, “they also need to develop the skill of ‘deeper thinking’ and learn how to think one step ahead,” Morris says. “This includes skills like system design mastery—understanding the macro view and learning how microservices, databases, and third-party APIs interact securely and efficiently.”

They also should become deeply fluent in the AI coding tools commonly used in their particular industry, with a strong understanding of how to prompt them for optimal output, Morris says. Product context awareness is also useful. “AI doesn’t know what the customer wants, but you do,” Morris says. “Understanding the business problem and the end-user experience is a requirement for being able to guide LLMs.”

Master debugging and incident response

Developers looking to break into non-tech sectors also should develop skills in debugging and incident response, Morris says. “Complex systems with multiple AI agents can, and will, fail, which means companies need humans to trace logic flaws to get the system back on track,” he says. “A mastery of root-cause analysis is a critical skill.”

“Security, compliance, and reliability are very important in non-tech industries like finance and healthcare,” says Rohit Agarwal, co-founder of Zenius, a remote hiring company. “So employers want developers who also know regulatory environments well.”

Network and keep learning

To successfully pivot from jobs at tech companies, “continuous learning, upskilling, and building hybrid skills that combine technical and business knowledge are essential,” Morris says. “With the right preparation, tech professionals can adapt and continue to thrive in meaningful, dynamic careers.”

It’s also a good idea to join talent communities in fields of interest and “engage with other members in conversations that increase your knowledge through the collective intelligence of the community,” Morris says. “Take advantage of AI skilling opportunities relevant for your role, or better yet, where you want to go next. Experiment with the technology either on your own or through structured programs.” Ultimately, be curious and proactive, he says.

“I’d also recommend developers not to ignore referrals, direct outreach, and industry-specific communities during job search,” Agarwal says. “There are often a lot more opportunities available than the ones posted online.”

Tech job postings rose for the sixth straight month in June, as employers are increasingly on the hunt for qualified talent to support major technological initiatives, according to the most recent CompTIA Tech Jobs Report.

“June’s employment data suggests that employers are ramping up their technology investments and hiring the talent needed to support them,” said Seth Robinson, vice president for industry research at CompTIA, in a press release. “Even as some tech companies announce layoffs, employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation.”

Data from Dice supports this growth, with its report showing AI/ML job title postings up 173% year over year from Q1 2025 to Q1 2026 — roles that typically offer median salaries 22% higher than the overall IT market. Hiring for jobs in data analysis (10%), cybersecurity (4%), and IT support (1%) have also seen increases. Tech postings in the finance and banking industry are up 47%, with manufacturing IT jobs up by 27% and IT roles in the insurance, aerospace, and defense sectors up by 23%, pointing to growth for IT across a range of sectors.

Software development jobs, however, are still on the decline from last year, down 22% from 2025, according to Dice, but it looks like that trend may soon shift. Data from CompTIA shows that software developer and software engineer job postings were the top roles for June (49k postings), followed by systems engineer (36k), tech support specialist (27k), data analyst (21k), and DevOps engineer (19k).

The catalyst for sparking a boost in software development hiring can be attributed to the release of Claude Code in late February 2025, according to data from the Indeed Hiring Lab. Since Claude Code’s release, Indeed has tracked a rise of 15% for software development job postings in the US, despite overall job postings declining by 7%. The report highlights how this rebound comes after a “low starting point,” so while the increases seem high, it’s because they had a long way to go.

Despite this increase in job postings, software development jobs still hover somewhere around 27.5% below their pre-pandemic levels, while overall job postings have remained relatively stable since 2020. It’s also important to note that 71% of the increase of software development job postings between May 2025 and May 2026 has been for senior-level roles, with 37% mentioning AI in the title.

This indicates that there is merit to the concerns around how AI automation is reshaping the IT leadership pipeline. IT leaders will need to remain mindful of how AI impacts hiring — and avoid leaving entry-level workers in the dust as hiring rebounds.

New research from Indeed Hiring Lab found that the number of U.S. job titles referencing AI has more than tripled since 2022, growing from 264 to 822 by the first quarter of 2026.

The research division of the global job search platform also discovered that nearly two-thirds (63%) of those AI-related job titles in the U.S. now appear in roles outside traditional technology occupations, in fields such as healthcare, education, marketing, logistics, and management.

“AI is no longer just a tech occupation story,” Indeed researchers wrote, noting that employers are increasingly incorporating AI skills into roles that historically had little connection to software development or data science. “The number of AI-touched jobs has risen across every market we track.”

Separately, according to CompTIA’s latest Tech Jobs Report, employers posted more than 280,000 new technology job postings in June, marking the sixth consecutive month of growth. Active technology job postings approached 600,000, while employment in tech occupations increased by 47,000 positions. The unemployment rate for tech occupations fell to 2.9%, compared with the national unemployment rate of 4.2%, according to CompTIA.

“June’s employment data suggests that employers are ramping up their technology investments and hiring the talent needed to support them,” said Seth Robinson, vice president for industry research at CompTIA, in a statement. “Even as some tech companies announce layoffs, employers in other industries are accelerating digital transformation initiatives and moving from AI experimentation to implementation.”

CompTIA’s report notes that new tech job postings increased for the sixth consecutive month amid what it described as a “broad-based movement toward reinvigorating digital transformation activities and clarifying AI strategies.”

Demand remains strongest for traditional IT roles. Software developers and engineers led all technology job postings in June with nearly 50,000 openings, followed by systems engineers, tech support specialists, data analysts, and DevOps engineers. Professional, scientific, and technical services firms generated the highest volume of technology job postings, followed by administrative services, manufacturing, information and media, and financial services organizations, according to CompTIA.

Employers are increasingly “writing AI into job titles for a wide range of roles—not just software and data jobs,” according to Indeed’s report. The reports show continued demand for technology workers while AI-related job titles become more common outside the traditional tech sector.

데이터독 코리아는 9일 서울에서 기자간담회를 열고 이 같은 사업 현황과 연례 컨퍼런스 ‘대시(Dash) 2026’에서 공개한 신제품 전략을 소개했다.

엄수창 데이터독 코리아 지사장은 이를 시장 변화의 신호로 해석했다.

엄 지사장은 “연초만 해도 ‘SaaS 아포칼립스’라는 말이 나올 정도로 SaaS 기업들의 미래를 부정적으로 보는 시각이 많았다”며 “하지만 데이터독은 AI와 함께 성장하면서 오히려 큰 미래 비전을 갖게 됐다”고 말했다. 이어 “글로벌 상위 AI 기업 10곳 모두 데이터독을 사용하고 있다는 사실 자체가 시장이 우리의 성장 가능성을 높게 평가하고 있다는 방증”이라고 강조했다.

이 같은 자신감의 배경에는 AI 시대 급증하는 운영 관리 수요가 있다. 과거에는 데브옵스(DevOps)와 SRE(Site Reliability Engineering) 조직이 장애 분석과 인프라 모니터링을 위해 주로 활용했다면, 이제는 AI를 활용해 운영을 자동화하는 기능과 AI 애플리케이션 자체를 관리하는 기능까지 제공하며 AI 시대에 맞춰 사업 영역을 확대하고 있다.

정영석 데이터독 기술총괄은 올해 대시에서 공개한 100여 개의 신기능을 ‘자율 IT 운영(Autonomous Operations)’과 ‘AI 거버넌스’라는 두 가지 축으로 설명했다.

자율 IT 운영 분야에서는 ‘비츠 AI(Bits AI)’가 장애 탐지부터 원인 분석, 해결까지 자동으로 수행한다. 정 총괄은 “장애가 발생하면 비츠 AI가 8가지 안팎의 가설을 세운 뒤 하나씩 검증해 근본 원인을 찾아내고, 코드 수정안까지 PR(Pull Request) 형태로 제안한다”며 “기존에는 엔지니어가 1~2시간 걸리던 분석 및 보고서 작성 작업을 빠르면 5분 이내로 단축할 수 있다”고 설명했다.

또 인프라 자원이 부족하면 슬랙 등을 통해 운영자 승인만 받아 메모리와 CPU를 자동으로 증설하고, 사전에 정의한 가드레일 안에서는 무인 복구도 수행한다. 코드 변경부터 스테이징 배포, 프로덕션 환경에 이르기까지 애플리케이션이 의도대로 동작하는지도 AI가 지속적으로 검증한다.

AI 거버넌스 분야에서는 ▲에이전트 옵저버빌리티(Agent Observability) ▲AI 게이트웨이(AI Gateway) ▲AI 가드(AI Guard) ▲LLM 비용 관리 콘솔 등의 기능 공개했다.

에이전트 옵저버빌리티는 AI 에이전트 내부에서 어떤 LLM과 도구를 사용했고, 토큰과 비용이 얼마나 발생했는지 시각화한다. AI 게이트웨이는 여러 LLM을 통합 관리하고 감사(Audit)를 수행하며, AI 가드는 프롬프트 인젝션과 민감정보 유출을 차단한다.

정 총괄은 “코파일럿, 커서(Cursor), 클로드 등 여러 AI 모델을 함께 사용하는 기업이 늘면서 비용과 보안, 신뢰성을 통제하는 것이 C레벨 경영진의 공통 과제가 됐다”며 “개발자별, 모델별 사용량과 비용을 세분화해 보여주기 때문에 임원들도 최적화 지점을 쉽게 찾을 수 있다”고 말했다.

최근 많은 기업이 멀티 LLM 전략을 채택하는 만큼 이러한 통합 관리 플랫폼의 필요성도 커질 것이라는 설명이다.

CIO 코리아가 AI 기능 추가로 관련 비용이 늘어나는 것 아니냐고 묻자 정 총괄 “데이터독이 제공하는 수백 개의 외부 서비스 연동 기능은 모두 기본 호스트 사용료에 포함돼 있어 AI 기능이 추가됐다고 모니터링 비용이 늘어나는 것은 아니다”며 “다만 로그는 저장량이 늘어나면 비용이 증가하는 구조인데 이는 어느 벤더나 비슷하다”고 답했다.

이어 “AI가 추가됐다고 인프라 모니터링 비용이 올라가는 것은 아니지만 AI SRE처럼 자동 분석 기능을 사용할 경우 토큰(크레딧)이 소모돼 비용이 추가될 수 있다”며 “반면 엔지니어의 업무 시간을 크게 줄일 수 있기 때문에 ROI 측면에서는 충분히 상쇄할 수 있고, UI 대신 MCP(Model Context Protocol)를 활용하면 비용을 낮출 수 있어 국내 여러 대형 고객도 MCP 기반 AI옵스를 구축하고 있다”고 덧붙였다.

AI가 문제 해결책까지 제시하는 것에 대한 고객사의 거부감은 없는지 묻는 질문에는 “잘못 분석할 가능성이 있는 것은 사실이지만 지금까지 고객 반응은 매우 긍정적”이라며 “AI가 잘못 탐지하더라도 사용자가 대화를 통해 추가 분석을 요청하면 계속 수정하면서 근본 원인에 더 가까운 결과를 제시한다”고 설명했다.

이어 “이 경험은 ‘비츠 메모리(Bits Memory)’ 기능에 축적돼 이후 유사한 장애가 발생하면 더욱 정확하게 분석하도록 학습된다”고 말했다.

내부 AI는 자체 모델과 업계 최신 모델을 함께 사용하는 하이브리드 구조다.

정 총괄은 “프론티어 모델과 자체 모델인 ‘토토(Toto)’를 함께 운영하고 있으며 작업 특성에 따라 가장 적합한 모델을 선택해 사용한다”고 밝혔다. 클로드, GPT, 제미나이 등 다양한 외부 최신 모델을 활용하며, AI SRE는 내부적으로 최적 모델이 자동 선택되지만 에이전트 빌더에서는 고객이 MCP와 사용할 모델을 직접 선택할 수 있다. 또한 LLM 옵저버빌리티에서는 환각을 탐지하기 위해 교차 검증용 모델을 별도로 지정하는 기능도 제공한다.

데이터독은 앞으로 옵저버빌리티(Observability), 보안, 핀옵스(FinOps), 비즈니스 인텔리전스(BI)를 하나의 플랫폼에서 통합 제공하는 차별성을 앞세워 국내 시장 공략을 확대하겠다고 밝혔다.
jihyun.lee@foundryco.com

AI promised to make software development faster, but for many enterprises, it has also created a new management challenge: developers increasingly rely on a mix of coding assistants, AI agents, and models that operate in isolation, making it harder for engineering leaders to govern usage, share knowledge, and control costs.

JetBrains has sought to address these challenges with a new suite of tools and capabilities named JetBrains AI for Teams and Organizations that supports nearly all coding tools, their respective CLIs, and most IDEs, such as Claude, Codex, Gemini, Junie, IntelliJ, Pycharm, and Rider, with support for VS Code to be added soon.

The suite, which consists of capabilities like team automations and cloud agents, JetBrains Context, JetBrains Central, and JetBrains Central CLI, will allow enterprises to manage AI-assisted software development from a single control layer while allowing developers to continue using their preferred coding assistants and IDEs, Oleg Koverznev, head of agent systems at JetBrains, wrote in a blog post.

While team automations and cloud agents are intended to let AI agents run long-running engineering tasks in managed cloud environments and trigger workflows based on repository events or schedules, JetBrains Context is designed to provide a shared understanding of project code, documentation, and development activity across tools, Koverznev added.

Complementing those collaboration capabilities, JetBrains Central serves as the administrative layer for governance, access control, and usage management, with the Central CLI extending those controls to the command-line and automation workflows, Kovernznev further added.

Tool sprawl to reduced operational friction

Fragmentation of AI-assisted coding tools and development environments adds complexity for enterprises and their leaders.

“Fragmentation of AI coding tools is an evolving problem for enterprises. A year ago, the conversation was whether to even allow an AI assistant. Now a single team is running Copilot, Claude Code, Cursor, and a few homegrown agents at once,” said Muskan Bandta, cloud associate at FinOps services providing firm ZopDev.

“The pain leaders raise with us isn’t the tools themselves. It’s that nobody can answer three questions: who is using what, what is it costing us, and is it actually safe? Fragmentation became a governance and cost problem the moment agents started touching real codebases and running up real bills,” Bandta added.

These governance and cost issues are increasing demand for suites, such as JetBrains AI for Teams and Organizations, that can coordinate AI-assisted software development across teams.

For development teams specifically, such suites with a centralized context layer can reduce operational friction, according to Nitish Tyagi, senior principal analyst at Gartner.

“Rather than teams manually configuring multiple AI coding tools, maintaining prompts, or repeatedly supplying context, a centralized context layer enables agents to work with a more consistent understanding of the organization’s codebase, standards, documentation, and workflows,” Tyagi said.

“Over time, this can help accelerate parts of the software development lifecycle by reducing time spent searching for information, understanding legacy code, onboarding developers, and reworking outputs that lack the necessary business context. The primary value is not necessarily that agents write more code, but that they produce more relevant and organization-aware outputs,” Tyagi added.

More so because, a shared context layer for AI agents primarily helps development teams by reducing knowledge silos, Tyagi further added.

Governance and cost management as benefits

The benefits of the suite, according to Bandta, transcend developer productivity and should help CIOs with governance and cost management.

“The suite will enable engineering leaders to gain greater control over what information can be accessed by different agents and teams, helping reduce security and compliance risks. For example, enterprises can prevent agents from directly accessing sensitive repositories or business-critical data,” echoed Tyagi.

 “Secondly, as the platform is continuously refining and managing context, agents get optimized and more relevant context to deliver higher quality output at lower cost,” Tyagi added.

The launch of JetBrains’ new suite, analysts pointed out, also reflects a broader shift in the software development tools market, where vendors are evolving from integrated development environments.

“This launch signals the evolution of the IDE into an ‘agentic development environment’. A platform where developers increasingly orchestrate fleets of AI agents instead of writing every line of code themselves,” Bandta said.

“The segment is splitting into agents that do the work and platforms that govern them, and the platform layer is where enterprise budgets will concentrate,” Bandta noted, adding that she expects rival vendors, including Microsoft, to pursue similar strategies.

The new capabilities, according to JetBrains, will be rolled out gradually to business customers throughout July and August.

GitHub continues to be a scintillating target for attackers because it sits in the middle of the software supply chain and gives threat actors three things they crave: source code, secrets, and automated pipelines to run amok in.

Datadog Security Research has been tracking what it calls a “sustained pattern” of GitHub API abuse over the past several months that seeks to map organizations and their members. While individually these requests are “unremarkable,” they become dangerous when they move across environments for weeks at a time, and, worse, progress to full-out cloning. The biggest challenge is that they blend into normal API usage patterns.

GitHub has been a goldmine for criminals looking to breach organizations because many development lifecycles are insecure, said David Shipley of Beauceron Security. Typically, threat actors are after API keys and cloud secrets.

“Now with everyone being pushed to do more, faster, with AI agents coding, the treasure trove of secrets is likely even bigger,” he said. “In short, to steal a line from a previous gold rush of the analog era, ‘there’s gold in them thar hills.'”

Scott Miserendino, CTO at security and compliance company DataBee, agreed. “Github is the most popular source code repository for both open-source and enterprise projects,” he said. “Its sheer volume of projects, along with being home to some of the most popular and widely used software, make it a target.”

He noted that intellectual property theft such as the unauthorized cloning of private repositories can be used to gain use of proprietary software or find vulnerabilities that can be exploited.

A second popular attack involves searching for repositories containing default credentials to popular software. Using them, attackers may develop and test assaults on accounts that are present in production environments or come installed by default on certain appliances.

And, Datadog senior security engineer Julie Agnes Sparks wrote in a blog post, “the activity is not a single actor. Rather, it’s a blend of custom automated scanner tools, opportunistic abuse of leaked credentials, and coordinated networks of burner (ghost) accounts.”

A simple but effective way to map GitHub users

Sparks explained that a “large share” of GitHub’s API surface can be reached without authentication; it is public by design. Requests against APIs typically produce standard HTTP 200 responses.

This means a threat actor can build detailed maps of organizations, their public repositories, their members, who they follow, their starred repos, and projects they interact with. This traffic blends into normal API usage and thus does not seem suspicious, she said.

Furthermore, GitHub only collects geolocation data when a user interacts with private repositories, recording who they are and what access token they used, not when they interact with external resources. This limits geolocation and VPN/proxy-based attribution.

Typically, threat actors have performed automated scraping with custom or legitimate-sounding user agents, taking advantage of GitHub “ghost” accounts, profiles created anywhere from two to five years ago and left dormant.

This is an attractive method because, Sparks noted, “an account with a multi-year history reads as more legitimate than one registered the same week it starts scraping.”

Typically, these accounts are used for a “burst” of just one to three weeks across many enterprises at once, then usage stops. The researchers identified more than 50 ghost accounts across multiple user agents, clustered into families with names like user432023, user412023, or kobalt*.

Some campaigns did use the legitimate accounts of GitHub users who had inadvertently posted their OAuth tokens or personal access tokens (PATs), or have had their endpoints compromised or exposed in other ways.

Attackers use a mix of data exfiltration agents with names like GitHub-Company-Scraper, GitHub-Scraper-Tool/1.0., and GitHubAnalytics/1.5,designed to blend into normal data analysis traffic. The bulk of requests target the open source query language /graphql, which is “well suited” for bulk queries across enterprises, users, and repositories, Sparks noted. Normal REST endpoints are used for org-mapping.

The focus of the campaigns was “narrow and consistent,” and the concern “lies in the aggregate,” Sparks said. In isolation, requests target public repositories without authentication and return successful responses. This rarely produces “meaningful access” into an enterprise’s repositories.

But a group of accounts moving in sync across shared GitHub accounts with versioned, custom tooling over a period of weeks represents more troubling and systematic behavior. She cited one event in which dozens of distinct, legitimate, but compromised GitHub user accounts made API requests to a single organization within a window of only a few minutes, although in that case the attack failed, because they targeted private repository commit paths.

How enterprises can protect their GitHub environments

Sparks pointed out that these behaviors can be hunted for and detected “if you are watching the right fields,” such as those identifying the user agent, token type, autonomous system number (ASN), or attempted action.

“User agents, event activity, and actor names are vital clues to unauthorized activity in your environment,” Sparks emphasized. She suggested reviewing unusual user agent behavior across GitHub audit logs, particularly for those that extend to private repositories where the platform also captures the IP address, actor name, and programmatic access type.

Enterprises should also enable GitHub audit log streaming, baseline user agents, and perform proactive threat hunting. Most importantly, she said, they should develop detections unique to their GitHub organization, noting, “It’s important to know what normal looks like in your environment.”

Simply put, added Miserendino, enterprises should be following security best practices, including enabling multi-factor authentication (MFA) on all accounts, performing periodic user access reviews, removing any unused or unneeded accounts, and scanning repositories for credentials stored in plaintext rather than in a secret store.

GitHub continues to be a scintillating target for attackers because it sits in the middle of the software supply chain and gives threat actors three things they crave: source code, secrets, and automated pipelines to run amok in.

Datadog Security Research has been tracking what it calls a “sustained pattern” of GitHub API abuse over the past several months that seeks to map organizations and their members. While individually these requests are “unremarkable,” they become dangerous when they move across environments for weeks at a time, and, worse, progress to full-out cloning. The biggest challenge is that they blend into normal API usage patterns.

GitHub has been a goldmine for criminals looking to breach organizations because many development lifecycles are insecure, said David Shipley of Beauceron Security. Typically, threat actors are after API keys and cloud secrets.

“Now with everyone being pushed to do more, faster, with AI agents coding, the treasure trove of secrets is likely even bigger,” he said. “In short, to steal a line from a previous gold rush of the analog era, ‘there’s gold in them thar hills.'”

Scott Miserendino, CTO at security and compliance company DataBee, agreed. “Github is the most popular source code repository for both open-source and enterprise projects,” he said. “Its sheer volume of projects, along with being home to some of the most popular and widely used software, make it a target.”

He noted that intellectual property theft such as the unauthorized cloning of private repositories can be used to gain use of proprietary software or find vulnerabilities that can be exploited.

A second popular attack involves searching for repositories containing default credentials to popular software. Using them, attackers may develop and test assaults on accounts that are present in production environments or come installed by default on certain appliances.

And, Datadog senior security engineer Julie Agnes Sparks wrote in a blog post, “the activity is not a single actor. Rather, it’s a blend of custom automated scanner tools, opportunistic abuse of leaked credentials, and coordinated networks of burner (ghost) accounts.”

A simple but effective way to map GitHub users

Sparks explained that a “large share” of GitHub’s API surface can be reached without authentication; it is public by design. Requests against APIs typically produce standard HTTP 200 responses.

This means a threat actor can build detailed maps of organizations, their public repositories, their members, who they follow, their starred repos, and projects they interact with. This traffic blends into normal API usage and thus does not seem suspicious, she said.

Furthermore, GitHub only collects geolocation data when a user interacts with private repositories, recording who they are and what access token they used, not when they interact with external resources. This limits geolocation and VPN/proxy-based attribution.

Typically, threat actors have performed automated scraping with custom or legitimate-sounding user agents, taking advantage of GitHub “ghost” accounts, profiles created anywhere from two to five years ago and left dormant.

This is an attractive method because, Sparks noted, “an account with a multi-year history reads as more legitimate than one registered the same week it starts scraping.”

Typically, these accounts are used for a “burst” of just one to three weeks across many enterprises at once, then usage stops. The researchers identified more than 50 ghost accounts across multiple user agents, clustered into families with names like user432023, user412023, or kobalt*.

Some campaigns did use the legitimate accounts of GitHub users who had inadvertently posted their OAuth tokens or personal access tokens (PATs), or have had their endpoints compromised or exposed in other ways.

Attackers use a mix of data exfiltration agents with names like GitHub-Company-Scraper, GitHub-Scraper-Tool/1.0., and GitHubAnalytics/1.5,designed to blend into normal data analysis traffic. The bulk of requests target the open source query language /graphql, which is “well suited” for bulk queries across enterprises, users, and repositories, Sparks noted. Normal REST endpoints are used for org-mapping.

The focus of the campaigns was “narrow and consistent,” and the concern “lies in the aggregate,” Sparks said. In isolation, requests target public repositories without authentication and return successful responses. This rarely produces “meaningful access” into an enterprise’s repositories.

But a group of accounts moving in sync across shared GitHub accounts with versioned, custom tooling over a period of weeks represents more troubling and systematic behavior. She cited one event in which dozens of distinct, legitimate, but compromised GitHub user accounts made API requests to a single organization within a window of only a few minutes, although in that case the attack failed, because they targeted private repository commit paths.

How enterprises can protect their GitHub environments

Sparks pointed out that these behaviors can be hunted for and detected “if you are watching the right fields,” such as those identifying the user agent, token type, autonomous system number (ASN), or attempted action.

“User agents, event activity, and actor names are vital clues to unauthorized activity in your environment,” Sparks emphasized. She suggested reviewing unusual user agent behavior across GitHub audit logs, particularly for those that extend to private repositories where the platform also captures the IP address, actor name, and programmatic access type.

Enterprises should also enable GitHub audit log streaming, baseline user agents, and perform proactive threat hunting. Most importantly, she said, they should develop detections unique to their GitHub organization, noting, “It’s important to know what normal looks like in your environment.”

Simply put, added Miserendino, enterprises should be following security best practices, including enabling multi-factor authentication (MFA) on all accounts, performing periodic user access reviews, removing any unused or unneeded accounts, and scanning repositories for credentials stored in plaintext rather than in a secret store.

This article originally appeared on InfoWorld.

Anthropic is bringing its Claude Cowork AI agent to web and mobile platforms, a move aimed at helping enterprise users monitor and manage long-running AI-driven tasks from anywhere as organizations increasingly adopt agents for operational and knowledge work.

The rollout, according to the company, is based on an analysis of 1.2 million anonymized and aggregated Claude Cowork sessions conducted between May 11 and May 31 that showed that business process and operations accounted for the largest share of the AI agent’s usage at 33.4%, followed by content creation and copywriting at 16.4%, the company wrote in a blog post.

Software development represented only 8.7% of sessions, ahead of DevOps and infrastructure at 7%, along with research and intelligence at 6.4%, and data analysis and business intelligence (5.8%).

Cowork’s expansion, which is currently in beta, will allow users to start and manage Claude Cowork sessions directly from the Claude interface on the web and mobile, while enabling the AI agent to continue executing long-running tasks in the cloud, the company wrote in a separate blog post.

That cloud functionality would allow users to check progress, respond to approval requests, and resume conversations across devices without returning to their desktop, it added.

More employee productivity

For CIOs and their enterprises, Cowork’s expansion is likely to unlock productivity gains, according to Pareekh Jain, principal at Pareekh Consulting.

“Teams can monitor long-running tasks, respond to exceptions, and keep business processes moving even when away from their desks, reducing operational delays, while also improving AI adoption,” Jain said.  

Beyond productivity gains, the expansion could reshape how CIOs staff and manage operational support functions, according to Bhupendra Chopra, chief revenue officer at IT consulting firm Kanerika.

“As AI agents take on routine knowledge work such as preparing reports, processing documents, and reconciling data, employees would increasingly shift from executing those tasks to supervising AI-generated outputs, reviewing exceptions, and applying business judgment. That would allow CIOs to redeploy knowledge workers toward higher-value activities while reducing the manual effort required across operational support teams,” Chopra said.

For developers, Cowork’s expansion will help shorten development cycles as it makes AI-assisted software engineering more continuous instead of being tied to a desktop IDE, Jain said.  

Background agents bring governance challenges

However, analysts cautioned that the rise and enterprise adoption of Claude Cowork-like tools, which represent a growing class of AI agents that operate in the background, executing long-running tasks with periodic human oversight, would also require CIOs to strengthen governance, security, and oversight.

“Always-on or persistent agents expand the attack surface because they continuously access enterprise systems and data. CIOs will need stronger identity controls, least-privilege permissions, audit trails, approval workflows, policy enforcement, and continuous monitoring to ensure these agents remain secure, compliant, and accountable,” Jain said.

Citing research from Gravitee that surveyed 445 IT and security leaders across multiple industries, Chopra pointed out that 88% of the surveyed leaders said that deploying AI agents reported confirmed or suspected security incidents over the past year, while only about 14% said their agents were deployed with full security and IT approval.

“The deployment velocity is dramatically outpacing governance. A background agent reading email, files, and calendars while the user is offline requires very precise scoping of what it can access and what it can act on,” Chopra said.

“Anthropic has kept human approval in the loop for consequential actions — that’s sensible product design. But enterprises need to build the same discipline on their side before they extend broad access to any agent,” Chopra added. 

The web and mobile experience are currently available only for Max subscribers, Anthropic said, adding that support for Pro, Team, and Enterprise plans will follow in the coming weeks.

The number of tech layoffs continues to tick upwards as AI investments increase, with Microsoft alone cutting around 4,800 employees, or roughly 2.1% of its workforce, this week.

The latest cutbacks are mostly in the company’s commercial sales and Xbox divisions. They follow two others in 2025 that impacted around 15,000 workers, or roughly 4% of the company’s workforce. Prior to the latest cuts, Microsoft had 220,000-plus employees.

The headcount reduction also comes just days after the announcement of Microsoft Frontier Company, an initiative that will provide embedded support for customers deploying AI projects, similar to traditional offerings from systems integrators (SIs).

Taken together, these moves seem to indicate that Microsoft is betting on its engineering expertise, rather than traditional account management, as the path to AI success.

“Microsoft had already reorganized its commercial business around AI,” said Thomas Randall, a research director at Info-Tech Research Group. “Recent layoffs are part of that ongoing context.”

Microsoft’s memo to employees

In a memo obtained by Business Insider, Microsoft EVP and chief people officer Amy Coleman said the cuts effectively reflect the tectonic shift being brought about by AI.

“The ‘why’ is this: Our business is changing because the world around it is changing,” she said. “Companies don’t get to choose whether their industry changes; they only get to choose whether they change with it.”

Customer needs, and the business models that serve them, are shifting, meaning vendors must “adjust resources and roles” so they can operate in a way that best serves their customers. However, Coleman emphasized: “Whenever possible, our priority is to place people into new roles aligned to the company’s highest priorities and greatest areas of opportunity.”

Which, today, is AI.

Seemingly contradictorily, Coleman said the cuts “build on” the Frontier Company announcement, which is “reshaping how we work and embedding our engineering experts alongside customers so we can help them accelerate their technology deployments.”

While she emphasized that the roles eliminated this week are not being replaced by AI, the technology is fundamentally changing work. Many everyday tasks are being automated, meaning “we all need to keep learning, keep building new skills, and keep adapting as the work evolves.”

Customers are undergoing the same shift and are looking to Microsoft for guidance, she noted. “We can’t do that well unless we’re doing it ourselves.”

Finally, she said the tech giant will evolve structure and priorities across the company “thoughtfully.”

“We are working on alternative solutions to job eliminations and … we will continue to invest in equipping employees with new skills, including in AI.”

What customers might expect

Redmond isn’t the only big tech company taking scalpels to staff as the industry adjusts to, and seeks to capitalize on, AI. Companies are spending billions and inking strategic partnerships with top AI labs, and these investments in some cases need to be offset with cuts because some have yet to provide tangible ROI.

For instance, Amazon has laid off 30,000 workers since last fall, while Google is rumored to be cutting employees in its cloud division. Meta, for its part, eliminated 8,000 employees, or about 10% of its total headcount, in May alone, while Oracle recently slashed 21,000.

For customers, there is a price to pay, however. In the case of Microsoft, Info-Tech’s Randall said that, with the cuts, some customers can now expect slower response times on “non-strategic asks,” particularly as accounts are consolidated under fewer reps.

That said, given its Frontier Company investments, top accounts with large AI, data, security, and cloud commitments may get “deeper technical engagement,” while ordinary licensing/support workflows may become leaner.

Further, customers can expect more hand-offs to partner-led engagements, given that Microsoft is already pushing customers toward its partners for FY27 (which began July 1, 2026) when it comes to AI, security, cloud modernization, Copilot, agents, and managed services, Randall said. The company is also offering partners higher margins for growth in certain AI workloads.

“To prepare for these shifts, customers should reflect and document their Microsoft account and support teams,” Randall advised.

By that he meant enumerating things like the support contacts, the partner contacts, and the escalation paths for issues. This information should be well-documented and shared internally, he said. The same goes for Microsoft-involved conversations having to do with any kind of commitment, such as those involve pricing assumptions, roadmap dependencies, or deployment milestones.

“This can ensure a smoother transition with a new account rep on what has already been set out for the organization,” Randall said.

Closing the gap between AI investment and ROI

Last week, Microsoft launched the $2.5 billion Frontier Company, which it said “goes beyond” SIs and Forward Deployed Engineers (FDE). The initiative will integrate thousands of the company’s own engineers directly into customer environments to help them build AI tools, and to also help customers learn essential skills so they can eventually handle projects on their own.

But customers shouldn’t think of this as what he described as “consulting-heavy, McKinsey-style engagements,” noted Info-Tech’s Randall. Pre-sales will likely become more focused on qualifying an organization’s specific processes for ongoing AI implementations, rather than on system-wide change management. As with other hyperscalers such as AWS, Microsoft is leaning into this more white-glove model to help “prevent the gap between AI investments and AI ROI from widening.”

“As such, Microsoft will likely reserve its best technical talent for accounts with strong production intent, credible budget, usable data, and clear executive sponsorship,” Randall predicted.

This article originally appeared on CIO.com.