Short Course

AI Agentic Design Patterns with AutoGen

In Collaboration With

Microsoft, Penn State University



1 Hour


Chi Wang Qingyun Wu

  • Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

  • Implement agentic design patterns: Reflection, Tool use, Planning, and Multi-agent collaboration using AutoGen.

  • Learn directly from the creators of AutoGen, Chi Wang and Qingyun Wu.

What you’ll learn in this course

In AI Agentic Design Patterns with AutoGen you’ll learn how to build and customize multi-agent systems, enabling agents to take on different roles and collaborate to accomplish complex tasks using AutoGen, a framework that enables development of LLM applications using multi-agents.

In this course you’ll create: 

  • A two-agent chat that shows a conversation between two standup comedians, using “ConversableAgent,” a built-in agent class of AutoGen for constructing multi-agent conversations. 
  • A sequence of chats between agents to provide a fun customer onboarding experience for a product, using the multi-agent collaboration design pattern.
  • A high-quality blog post by using the agent reflection framework. You’ll use the “nested chat” structure to develop a system where reviewer agents, nested within a critic agent, reflect on the blog post written by another agent.
  • A conversational chess game where two agent players can call a tool and make legal moves on the chessboard, by implementing the tool use design pattern.
  • A coding agent capable of generating the necessary code to plot stock gains for financial analysis. This agent can also integrate user-defined functions into the code.
  • Agents with coding capabilities to complete a financial analysis task. You’ll create two systems where agents collaborate and seek human feedback. The first system will generate code from scratch using an LLM, and the second will use user-provided code.

You can use the AutoGen framework with any model via API call or locally within your own environment.

By the end of the course, you’ll have hands-on experience with AutoGen’s core components and a solid understanding of agentic design patterns. You’ll be ready to effectively implement multi-agent systems in your workflows.

Who should join?

If you have basic Python coding experience and you’re interested in automating complex workflows using AI agents, this course will provide the practical skills and knowledge you need to leverage AutoGen effectively.


Chi Wang

Chi Wang


Principal Researcher at Microsoft Research

Qingyun Wu

Qingyun Wu


Assistant Professor at Penn State University

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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