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.
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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 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.
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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Reviews summary
Practical autogen agentic design
The course materials appear current and well-maintained.
Covers a wide range of essential AI agentic design patterns.
Offers crucial hands-on experience for building agent systems.
Activities
Review multi-agent systems
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Brush up on the concepts of multi-agent systems before starting the course.
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Multi-Agent Systems
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Read articles or watch videos on multi-agent systems
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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
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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.
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Conversational AI
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Design different chatbot conversation scenarios
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Implement the scenarios using AutoGen's ConversableAgent class
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Test and iterate on the conversations
Five other activities
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Conversational chess game
Show steps
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.
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Customer Onboarding
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Identify the key steps in the customer onboarding process
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Design the interactions between the different agents
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Implement the onboarding experience using AutoGen
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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
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Learn the basics of Python programming
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Write a script to generate code for stock analysis
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Implement the script using AutoGen
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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
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
Show steps
- Read articles or watch videos on multi-agent systems
- Review your notes from previous courses or textbooks
AutoGen resource collection
Show steps
Gather useful resources on AutoGen for future reference.
Show steps
- Search for online tutorials, documentation, and examples
- Organize the resources into a document or folder
Chatbot conversation scenarios
Show steps
Understand how to create engaging multi-agent conversations using AutoGen.
Browse courses on
Conversational AI
Show steps
- Design different chatbot conversation scenarios
- Implement the scenarios using AutoGen's ConversableAgent class
- Test and iterate on the conversations
Conversational chess game
Show steps
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
Show steps
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
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