Build Intelligent Multi-Agent Applications with AutoGen – Now on Udemy!

Are you ready to take your AI development skills to the next level? Whether you're an AI enthusiast, a machine learning engineer, or a software developer eager to build intelligent systems, my brand-new Udemy course — "Build Intelligent Multi-Agent Applications with AutoGen" — is the perfect place to start.

This course is built using AutoGen version 0.4 and works seamlessly with AutoGen 0.5, giving you access to the latest features in multi-agent system development. I’ve designed it to be as practical and hands-on as possible covering important AutoGen concepts — packed with code demos, real-world use cases, and complete project walkthroughs.

👉 Course Link "Build Intelligent Multi-Agent Applications with AutoGen" on Udemy

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🎯 What You’ll Learn

By the end of the course, you’ll be able to:

• Design, build, and deploy multi-agent systems using the AutoGen AgentChat framework.

• Integrate OpenAI or even use open-source LLMs like LLaMA 3.2 via Ollama.

• Understand how to construct intelligent agents and teams that can collaborate, communicate, and coordinate across tasks.

• Implement advanced capabilities like SelectorGroupChat, reflect_on_tool_use, termination conditions, and human-in-the-loop interaction.

• Build and optimize two real-world AI agent projects:

  • A Streamlit app with an AI assistant to explain your code and provide optimization suggestions

  • A Multi-agent AI chatbot for customer support

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📚 Course Structure & Topics Covered

The course is structured into detailed sections, starting from the basics and progressing to advanced concepts. Each section is packed with hands-on notebooks, explanatory slides, and code walkthroughs.

🔹 Section 1: Course Setup & Introduction

• What this course is about

• Who should take it and the prerequisites

• Setting up Python 3.10+, VS Code, and creating a virtual environment

• Downloading the GitHub repo and installing required libraries

🔹 Section 2: Understanding Multi-Agent Systems and AutoGen

• What are AI agents?

• Key components and real-world use cases

• Introduction to the AutoGen framework

• Deep dive into the AgentChat architecture

🔹 Section 3: Models

• What are LLMs and how to choose the right one

• Using OpenAI/Azure OpenAI with .env secrets

• Running LLaMA 3.2 using Ollama

🔹 Section 4: Core Concepts in AutoGen

• Messages & TextMessage format

• AssistantAgent & UserProxyAgent deep dive

• Understanding on_messages(), run(), reflect_on_tool_use, and function calling

• Multi-modal capabilities with text + images

• Streaming token outputs for real-time feedback

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🔹 Section 5: Project 1 AI Assistant Streamlit App

• Introduction to the project and what it solves

• Full code walkthrough and optimization tips

• How to deploy a Streamlit interface for agent interaction

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🤖 Section 6: Teams & SelectorGroupChat

• When and how to use teams of agents

• Understanding GroupChat, RoundRobin, and SelectorGroupChat

• Defining custom selectors

• Human-in-the-loop input and termination strategies

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🛠️ Section 7: Project 2: Multi-Agent Chatbot for Customer Support

• Build an intelligent customer support chatbot using multiple agents

• Handle order placement, user queries, product search, and even basic support escalation

• Code walkthrough and design explanation

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🧠 Section 8: Advanced Concepts in Multi-Agent Systems

• Managing agent and team states

• Exploring Magentic-One, a generalist multi-agent system for web and file-based tasks

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🎓 Why Take This Course?

This course is perfect if you:

• Want to build production-ready multi-agent AI systems

• Are exploring AutoGen and want to go beyond documentation

• Are working on agent orchestration, tool use, or AI workflows

• Need to develop real-world AI chatbots and assistants

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🔗 Enroll Now

👉 Enroll in "Build Intelligent Multi-Agent Applications with AutoGen" on Udemy

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If you’re curious about the future of AI, agent collaboration, and building smarter apps — this course is your launchpad. I’ve poured in my hands-on experience, so you can skip the trial and error and go straight to building intelligent systems.

Let’s build the future — one agent at a time.