Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.
Beginner1h35m
AI Agentic Design Patterns with AutoGen
Instructors: Chi Wang, Qingyun Wu
Earn an accomplishment with PRO · Updated Oct 7, 2025
- Beginner
- 1h35m
- 8 Video Lessons
- 6 Code Examples
- 1 Graded Assignment PRO
- Earn an accomplishment with PRO
- Updated Oct 7, 2025
- Instructors: Chi Wang, Qingyun Wu
Microsoft, Penn State University- Learn more aboutMembership PRO Plan
What you'll learn
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.
Course recap
PROBuilt in partnership with Microsoft and taught by AutoGen co-creators Chi Wang and Qingyun Wu, this course introduces AutoGen as a multi-agent conversational framework for implementing complex AI applications using agentic design patterns. Students learn to create multiple agents with different roles, personas, and capabilities.
Concept map
Key concepts
- ConversableAgentAutoGen's core building block agent that supports conversation, tool use, and code execution
- Sequential chatsAgents working step by step through a list of tasks in sequence (e.g., customer onboarding)
- Nested chatsAn agent that, when given a task, delegates to other agents internally before returning results
- Agentic workflowsReflection, tool use, planning, and multi-agent collaboration patterns
- Group chatMultiple specialized agents collaborating with controlled workflow between them, including planning agents
Lesson highlights
- 1.Introduction — AutoGen overview, multi-agent framework concepts
- 2.Multi-Agent Conversation and Stand-up Comedy — ConversableAgent basics, two-agent conversations
- 3.Sequential Chats and Customer Onboarding — Step-by-step task execution across agents
- 4.Reflection and Blogpost Writing — Nested chats for iterative review and improvement
- 5.Tool Use and Conversational Chess — Providing user-defined functions to agents
- 6.Coding and Financial Analysis — Code writing and execution, sandbox environments
- 7.Planning and Stock Report Generation — Group chats with planning agents, workflow control
- 8.Conclusion
About 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.
- A custom group chat with multiple agents that collaborate to generate a detailed stock performance report, incorporating a planning agent and customizing how the conversation flows between different agents.
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.
Course Outline
8 Lessons・6 Code Examples- IntroductionVideo・4m
- Multi-Agent Conversation and Stand-up ComedyVideo with Code Example・12m
- Sequential Chats and Customer OnboardingVideo with Code Example・8m
- Reflection and Blogpost WritingVideo with Code Example・10m
- Tool Use and Conversational ChessVideo with Code Example・15m
- Coding and Financial AnalysisVideo with Code Example・17m
- Planning and Stock Report GenerationVideo with Code Example・15m
- ConclusionVideo・1m
- Quiz
Graded・Quiz
・10m
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