Thank you, London MLcon community ✓ 2027 dates soon ✓ Join the community newsletter for exclusive content

Thank you, London MLcon community ✓ 2027 dates soon ✓ Join the community newsletter for exclusive content

Thank you, Amsterdam MLcon community ✓ See you in 2027 ✓ Join the community newsletter for exclusive content

Thank you, Amsterdam MLcon community ✓ See you in 2027✓ Join the community newsletter for exclusive content

Thank you, San Diego MLcon community ✓ Join us at MLcon New York ✓ September 28 – October 2, 2026

Thank you, San Diego MLcon community ✓ Join us at MLcon New York ✓ September 28 – October 2, 2026

Thank you, Munich MLcon community ✓ 2027 dates soon ✓ Join the community newsletter for exclusive content

Thank you, Munich MLcon community ✓ 2027 dates soon ✓ Join the community newsletter for exclusive content

Only Until July 23: ✓ Save up to $600 ✓ 10% Team Discount ✓ Access to devmio

Only Until July 23: ✓ Save up to $600 ✓ 10% Team Discount ✓ Access to devmio

MLcon Conference is part of AI Native Week

MLcon Conference is part of AI Native Week

Build reliable AI agents and autonomous workflows for enterprise production

Getting an AI agent to complete a task once is easy. Getting it to do so reliably, repeatably, with a high success rate in a real enterprise environment — that's the unsolved problem most teams hit after the demo. This track goes deep into agentic architecture, MCP-based tool integration, multi-agent orchestration, and the governance patterns that keep autonomous systems from going off-script.

Speaker Programm Program

AI Agents & Agentic Workflows

Learn from Industry Leaders about:

  • Designing agentic workflows for repeatability and high success rates in production
  • MCP (Model Context Protocol): connecting AI agents to enterprise APIs and data sources
  • Multi-agent orchestration with LangGraph, CrewAI, and custom frameworks
  • Tool calling, function execution, and state management in long-running agents
  • Agent observability: tracing, debugging, and evaluating autonomous systems
  • Governance and control: defining, enforcing, and auditing what your agents can do

Frequently Asked Questions

What will I learn in the Generative AI & Agents track?

You’ll learn how to build advanced GenAI systems using LLMs and intelligent agents. Topics include prompt engineering, retrieval-augmented generation (RAG), and MCP for scalable collaboration and orchestration.

Why is agentic design critical for modern GenAI?

Agentic workflows allow LLMs to act autonomously and coordinate with one another. This enables real-world applications like internal copilots, autonomous support agents, and orchestrated task execution at scale.

What is MCP and how does it enable agent collaboration?

MCP (Multi-Agent Communication Protocol) allows agents to securely share memory, context, and goals. It provides the structured communication layer needed for dependable, multi-agent AI systems.

Which use cases are covered?

Topics span customer support, code generation, knowledge retrieval, and document automation. Each example is grounded in production scenarios with real-world LLM infrastructure.

Track Speakers London 2026

Track Speakers San Diego 2026

San Diego's program will go live soon! Until then, please take a look at

New York's 2024 Program!

Track Speakers Munich 2026

Track Speakers New York 2026

Track Speakers Amsterdam 2026

Track Speakers Berlin 2026

Track Program London 2026

Track Highlights San Diego 2026

Track Program Munich 2026

Track Program New York 2026

Track Program Berlin 2026

Track Program Amsterdam 2026

Track Sessions London 2026

Track Sessions London 2026

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Track Sessions Amsterdam 2026

Track Sessions Amsterdam 2026

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Track Sessions San Diego 2026

Track Sessions San Diego 2026

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Track Sessions MLcon Munich 2026

Track Sessions MLcon Munich 2026

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Track Sessions MLCon New York

Track Sessions MLCon New York

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Track Sessions MLcon Berlin 2026

Track Sessions MLcon Berlin 2026

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Behind the Tracks

AI Agents & Agentic Workflows
Build reliable AI agents and autonomous workflows for enterprise production

MLOps & Open Source LLMs
Deploy, and operate AI models securely—from fine-tuning to production monitoring

Multimodal AI
Build AI systems that process images, audio, and video—beyond language models

AI Strategy, Organization & Governance
Scale AI responsibly—align regulation, human accountability, and organizational readiness

Advanced RAG
Master retrieval-augmented generation—from vector search to production-grade knowledge systems

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