Many IT leaders looking to capitalize on the promise of agentic AI still struggle with a basic step — building out their agentic architecture — even as they roll out dozens or hundreds of agents.
AI agent deployment is expected to skyrocket over the next year, with IDC predicting a required to get agents to scale responsibly, says . While some IT leaders think of architecture as the AI model powering agents, it goes much deeper.
Agentic architecture includes several elements, and in some cases, CIOs have underinvested in some functionality, says , the brain that controls subagents; subagents themselves, which have specialized functions; APIs and other tools; memory to maintain context of an agent session and overall behavior over a longer period; and guardrails to set boundaries for agents
Many organizations are now building standardizations, Pitkar says.
A lack of data access and integration can be a huge barrier to agent deployments despite heavy investments in data modernization, he adds.
In addition, IT leaders struggling to deploy agents have often failed to manage organizational changes to account for a faster, agent-driven delivery cycle, he says. Adoption can slow down considerably if teams aren’t ready to engage with agent-speed outputs.
“AI does not care about org structure when it comes to accessing data,” he says.
Use cases determine the details
While the elements of agentic architecture are fairly standard, CIOs may need to focus more on different functionality depending on the use case, according to Pitkar. The devil is in the details.
“Customer support may need higher investments in dynamic intent mappings, memory, effective UX as these are emotionally charged conversations with human users who need a problem solved to continue their job,” Pitkar adds. “Agentic commerce may focus on deterministic APIs with machine readable data, guardrails, and compliance to securely process transactions with real money.”
Another way to look at agentic architecture is to think about four layers:
- System of context, including unified, semantically enriched data
- System of work, or composable application services agents can act on
- System of agency, where planning, coordination, and governance live
- System of engagement, the interfaces that enable agents to interact with employees and customers
Most organizations have these four functions happening in other enterprise software packages, but they are built for human-paced orchestration instead of machine-speed agents, says , chief AI officer at workflow automation provider Tungsten Automation.
Good agentic architecture allows agents to operate reliably inside real business processes, not just in isolation, he says. For the past 30 years, enterprise systems were built for humans to navigate UIs, but agents work silently on the inside by calling APIs and requiring action-level permissions rather than login access.
The underestimated piece of agentic architecture is the governance controls that define what agents can do autonomously and when they must stop and hand off control to a human, Field says.
“Deploying an agent on a discrete task is straightforward,” he adds. “The hard part is that businesses don’t run on discrete tasks.”
Instead, enterprises operate on end-to-end, regulated processes with exceptions, dependencies, compliance requirements, and humans in the loop at specific moments, he notes.
“Designing agentic architecture that works across an entire process, not just a single step, is a fundamentally different challenge,” Field adds. “Traditional software fails obviously. Agents fail silently, confidently, at scale.”
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