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AI Agentic Design Patterns with AutoGen

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:

1. 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.

In this course you’ll create:

1. 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.

2. A sequence of chats between agents to provide a fun customer onboarding experience for a product, using the multi-agent collaboration design pattern.

3. 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.

4. 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.

5. 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.

6. 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.

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What's inside

Syllabus

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Students who are interested in a framework to craft their own LLM using multiple agents will find this useful

Learners interested in the AutoGen framework will find this beneficial

This course is suited for software developers wanting to learn about AutoGen

Those already familiar with LLM will find this course more accessible

Novice software developers or those unfamiliar with LLM may initially struggle with the material

Students must have proficiency in Python to fully benefit from this course

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Reviews summary

Practical autogen agentic design

According to learners, this course provides invaluable hands-on experience with the AutoGen framework, enabling them to build complex multi-agent systems. Students particularly praise the practical examples covering various agentic design patterns like reflection and tool use, finding the content highly relevant and immediately applicable. While the course offers clear explanations, some learners noted a strong prerequisite in Python and LLM basics is beneficial, as the pacing can be quick for absolute beginners. Older concerns about outdated content seem to have been addressed, highlighting the course's ongoing relevance.

The course materials appear current and well-maintained.

"Fantastic deep dive into AutoGen. The content is very current and provides a strong foundation for building sophisticated agent systems."

"I found the materials current and relevant, and any older concerns about outdated code examples seem to have been addressed."

"The course continues to deliver on its promise of hands-on experience with the latest AutoGen features, which is great."

Covers a wide range of essential AI agentic design patterns.

"I especially loved the agent reflection and nested chat examples, which opened my eyes to advanced agentic design."

"This course covers a good range of design patterns, from multi-agent collaboration to tool use, providing a solid foundation."

"I found it provided a decent overview of various agentic patterns, and the instructor explained the concepts well."

Offers crucial hands-on experience for building agent systems.

"This course is a game-changer! The hands-on labs with AutoGen are incredibly practical..."

"The practical examples are what make it shine. From building a chess game to financial analysis, the applications were diverse..."

"I felt well-prepared to start my own AutoGen projects after completing this very practical and immediately applicable course."

Some learners desired more troubleshooting and debugging guidance.

"My only critique is that some parts felt a bit rushed, and I wished for more in-depth debugging tips."

"I agree with others that some debugging insights would have been a plus, especially for local setup issues."

"I think it could use more guidance on troubleshooting common errors that arise when working with agentic systems."

Requires a strong foundation in Python and Large Language Models.

"I struggled a bit with setting up the environment locally, as the course assumed a higher level of prior knowledge than I had."

"I believe a strong background in Python and understanding of LLMs is a prerequisite for getting the most out of it."

"For me, the course moved too fast. I think it needs to be more accessible for those not already experts."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Agentic Design Patterns with AutoGen with these activities:

Review multi-agent systems

Show steps

Brush up on the concepts of multi-agent systems before starting the course.

Browse courses on Multi-Agent Systems

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Read articles or watch videos on multi-agent systems
Review your notes from previous courses or textbooks

AutoGen resource collection

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Gather useful resources on AutoGen for future reference.

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Search for online tutorials, documentation, and examples
Organize the resources into a document or folder

Chatbot conversation scenarios

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Understand how to create engaging multi-agent conversations using AutoGen.

Browse courses on Conversational AI

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Design different chatbot conversation scenarios
Implement the scenarios using AutoGen's ConversableAgent class
Test and iterate on the conversations

Five other activities

Expand to see all activities and additional details

Show all eight activities

Conversational chess game

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Develop a conversational chess game where agents can call a tool and make legal moves.

Browse courses on Conversational AI

Show steps

Familiarize yourself with AutoGen's tool use design pattern
Implement a simple chess game using AutoGen
Add the ability for agents to call a tool to make moves

Multi-agent onboarding experience

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Develop a multi-agent system to provide a unique and engaging onboarding experience for customers.

Browse courses on Customer Onboarding

Show steps

Identify the key steps in the customer onboarding process
Design the interactions between the different agents
Implement the onboarding experience using AutoGen
Evaluate and refine the experience based on user feedback

Agent-generated code for stock analysis

Show steps

Create an agent capable of generating code to plot stock gains for financial analysis.

Browse courses on Stock Analysis

Show steps

Learn the basics of Python programming
Write a script to generate code for stock analysis
Implement the script using AutoGen
Test and refine the generated code

Agent-generated blog post with nested reflection

Show steps

Create a high-quality blog post using the agent reflection framework and nested chat structure.

Show steps

Write a script for the blog post using the agent reflection framework
Implement the script using AutoGen's nested chat structure
Review and refine the generated blog post

Multi-agent financial analysis system

Show steps

Create a multi-agent system where agents collaborate and seek human feedback to complete financial analysis tasks.

Browse courses on Financial Analysis

Show steps

Design the architecture of the multi-agent system
Implement the agents using AutoGen
Develop a mechanism for agents to collaborate and seek human feedback
Evaluate and refine the system based on user feedback

Career center

Learners who complete AI Agentic Design Patterns with AutoGen will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI Agentic Design Patterns with AutoGen.

The Intelligent Investor, Rev. Ed

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This classic book by Benjamin Graham, considered the father of value investing, provides a comprehensive overview of the principles and strategies involved in long-term investing. It emphasizes the importance of fundamental analysis, margin of safety, and emotional discipline, making it highly relevant for stock market analysis.

AI Agentic Design Patterns with AutoGen

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What's inside

Syllabus

Traffic lights

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Reviews summary

Practical autogen agentic design

Activities

Career center

Reading list

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