AI Agentic Design Patterns with AutoGen

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Table Of Contents

• Introduction
• Who Can Do This Course?
• AI Agentic Design Patterns with AutoGen
• Summary
• References

Introduction

In this post, we discuss a new course AI Agentic Design Patterns with AutoGen By DeepLearning AI

In AI Agentic Design Patterns with AutoGen we will 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.

Who Can Do This Course?

• 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.

AI Agentic Design Patterns with AutoGen

DeepLearning AI launching AI Agentic Design Patterns with AutoGen, a new short course in collaboration with Microsoft Research and Penn State University.

Learn from AutoGen creators Chi Wang (Microsoft Research) and Qingyun Wu (Penn State University) how to build and customize multi-agent systems using the AutoGen framework.

In this course we can learn to 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.

Start implementing multi-agent systems in your workflows with AutoGen today!

Summary

• https://galaxyofai.com/tag/llms/

References

• https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/