Multi-Agent AI: From Concept to Deployment

Step into the future of AI by learning how to design and build powerful multi-agent systems that move beyond theory into real-world production.

This session/course is designed to help you understand how modern agentic workflows are created using frameworks like AutoGen and LangGraph — and more importantly, how to think like a systems builder rather than just a model user.

What you’ll learn:

• Foundations of multi-agent architecture — when and why to use agents

• Orchestration patterns, memory, tool usage, and agent communication

• Designing reliable workflows that don’t break in production

• Practical differences between AutoGen and LangGraph

• Debugging, observability, and evaluation strategies

• Best practices for scalability and performance

• How to structure real-world AI applications using agent-based design

By the end, you won’t just understand agents — you’ll know how to approach building production-ready systems with clarity and confidence.

Best suited for engineers, developers, and AI practitioners who already understand the basics of Python and LLMs and want to level up into advanced, high-impact AI system design.