Short CourseBeginner1 Hour 26 Minutes

AI Agentic Design Patterns with AutoGen

Instructors: Chi Wang, Qingyun Wu

Microsoft, Penn State University
  • Beginner
  • 1 Hour 26 Minutes
  • 8 Video Lessons
  • 6 Code Examples

What you'll learn

  • 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.

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
  • Introduction

    Video4 mins

  • Multi-Agent Conversation and Stand-up Comedy

    Video with code examples13 mins

  • Sequential Chats and Customer Onboarding

    Video with code examples7 mins

  • Reflection and Blogpost Writing

    Video with code examples10 mins

  • Tool Use and Conversational Chess

    Video with code examples16 mins

  • Coding and Financial Analysis

    Video with code examples17 mins

  • Planning and Stock Report Generation

    Video with code examples15 mins

  • Conclusion

    Video1 min

Instructors

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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