Until Conference Starts:: ✓ Save up to £170  ✓ 10% Team Discount ✓ Parallel Conferences for £250

Until Conference Starts:: ✓ Save up to £170  ✓ 10% Team Discount ✓ Parallel Conferences for £250

Until Event Starts: ✓ Save up to 249 € ✓ 4-Day Special ✓ 10% Team Discount

Until Event Starts: ✓ Save up to 249 € ✓ 4-Day Special ✓ 10% Team Discount

Only Until April 30: ✓ Save up to $500 ✓ 10% Team Discount ✓ Access to Parallel Conferences for $250

Only Until April 30: ✓ Save up to $500 ✓ 10% Team Discount ✓ Access to Parallel Conferences for $250

Only Until May 7: ✓ Save up to 315€ ✓ 10% Team Discount ✓ Access to devmio

Only Until May 7: ✓ Save up to 315€ ✓ 10% Team Discount ✓ Access to devmio

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Only Until June 11: ✓ Save up to $960 ✓ Free Workshop Day ✓ 10% Team Discount

Thank you, Berlin MLcon community ✓ 2026 dates soon ✓ Join the community newsletter for exclusive content

Thank you, Berlin MLcon community ✓ 2026 dates soon ✓ Join the community newsletter for exclusive content

From prompt to protocol—design agentic workflows that truly work

Transform your business with the latest advancements in Large Language Models (LLMs). This track dives deep into how LLMs can automate tasks, generate creative content, and power intelligent chatbots. Learn practical strategies and cutting-edge tools to seamlessly integrate LLMs into your software applications.

Speaker Programm Program

Generative AI & Agents

Learn from Industry Leaders about:

  • Real-world GenAI systems Built for customer support, code generation, and process automation.
  • Prompt engineering and instruction tuning Strategies to improve output consistency and control.
  • Multi-Agent Communication Protocol (MCP) Secure agent communication frameworks for enterprise-grade applications.
  • RAG pipelines Combine LLMs with live data sources to deliver real-time, dynamic answers.
  • Architectures for multi-agent collaboration Design systems that support distributed reasoning and task coordination.

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 2025

Track Program London 2026

Track Highlights San Diego 2026

Track Program Munich 2026

Track Program New York 2025

Track Program Berlin 2025

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 2025

Track Sessions MLcon Berlin 2025

View all sessions

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.

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Behind the Tracks

Generative AI & Agents
Unlock the future of AI with LLM advancements.

MLOps & LLMOps
Bridge the gap between model creation and real-world application.

ML & Deep Learning
From Feature Engineering to Explainable AI.

AI Strategy & Governance
Transform business processes with AI-driven strategies.

AI Developer Tools
Build next-generation machine learning applications.

ML Basics & Principles
Gain a solid foundation in core ML concepts.

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