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
🎯 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:
📚 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
🔹 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
🤖 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
🛠️ 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
🧠 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
🎓 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
🔗 Enroll Now
👉 Enroll in "Build Intelligent Multi-Agent Applications with AutoGen" on Udemy
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.
