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Building AI Agents with MCP and LangChain | Intel Software

Video von Intel Software auf YouTube: Author: Intel Software - Bewertung: 6x - Views:130 Build your own AI agentic workflow with Langchain, LangGraph, and MCP.…

YouTube Security Videos youtube.com 2 Min. Lesezeit KRITIS / Energie Cloud & IT-Enterprise
0
↗ Quelle (youtube.com)
Reagiere als Erste:r — dein Feedback zählt!

Author: Intel Software - Bewertung: 6x - Views:130

Build your own AI agentic workflow with Langchain, LangGraph, and MCP. You can get started with an MCP math server and an MCP weather server and equip your agents with those tools. Leverage LLM models from the Llama and DeepSeek families. You can bring your own OpenAI-compatible API key and model endpoint to get started, or use the endpoints hosted on Intel Gaudi accelerators or Intel Xeon CPUs.



Intel AI for Enterprise Inference GitHub: https://github.com/opea-project/Enterprise-Inference

Denvr Dataworks OpenAI endpoint and API key: https://www.denvrdata.com/intel

IBM Cloud with Gaudi 3: https://www.ibm.com/products/gpu-ai-accelerator/intel-gaudi3

Langchain MCP documentation: https://langchain-ai.github.io/langgraph/agents/mcp/

MCP Documentation: https://modelcontextprotocol.io/quickstart/server

Ben Consolvo’s LinkedIn: https://www.linkedin.com/in/bconsolvo/

Intel DevHub Discord: https://discord.gg/rv2Gp55UJQ



#intel #denvrdataworks #ibmcloud #langchain #langgraph #mcp #intelgaudi #intelxeon #llms



About Intel Software:

Intel® Developer Zone is committed to empowering and assisting software developers in creating applications for Intel hardware and software products. The Intel Software YouTube channel is an excellent resource for those seeking to enhance their knowledge. Our channel provides the latest news, helpful tips, and engaging product demos from Intel and our numerous industry partners. Our videos cover various topics; you can explore them further by following the links.



Connect with Intel Software:

INTEL SOFTWARE WEBSITE: https://intel.ly/2KeP1hD

INTEL SOFTWARE on FACEBOOK: http://bit.ly/2z8MPFF

INTEL SOFTWARE on TWITTER: http://bit.ly/2zahGSn

INTEL SOFTWARE GITHUB: http://bit.ly/2zaih6z

INTEL DEVELOPER ZONE LINKEDIN: http://bit.ly/2z979qs

INTEL DEVELOPER ZONE INSTAGRAM: http://bit.ly/2z9Xsby

INTEL GAME DEV TWITCH: http://bit.ly/2BkNshu



#intelsoftware

Building AI Agents with MCP and LangChain | Intel Software

1. Sofort-Triage & Abwehrmaßnahmen

SOC Incident Playbook: Remote Code Execution (RCE) Defense
Syntax validiert (0 Fehler)
title: Detect Exploitation - Building AI Agents with MCP and LangChain | Intel Software
id: 19f18e77-c01d-4b19-af38-f72f9038d7f6
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
Syntax validiert (0 Fehler)
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = "Building AI Agents with MCP an" ascii wide
    condition:
        any of them
}
Syntax validiert (0 Fehler)
index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("Building AI Agents with MCP and LangChai")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count
Syntax validiert (0 Fehler)
message: "*Building AI Agents with MCP and LangChai*"
Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "Building AI Agents with MCP and LangChai"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

2. Cyber Threat Intelligence & Forensik

🎯
MITRE ATT&CK Matrix Navigator 14 Taktiken
Reconnaissance
-
Resource Development
-
Initial Access
Execution
Persistence
-
Privilege Escalation
Defense Evasion
Credential Access
-
Discovery
-
Lateral Movement
-
Collection
-
Command and Control
Exfiltration
-
Impact
💡 Tipp: Klicken Sie auf eine TTP, um Gegenmaßnahmen und SIEM-Erkennungsregeln anzuzeigen.Enterprise Matrix v14.1
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tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich Building AI Agents with MCP and LangChai.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

🛡️ Angriffsfläche & Exposure

Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.

⚡ Empfohlene Sofortmaßnahmen
  • 1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
  • 2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
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