What you'll learn:
- Understand Multi-Agent Systems – Learn the fundamentals of multi-agent architectures, their advantages, and real-world applications in AI-driven workflows.
- Hands-on with AutoGen – Gain practical experience in using AutoGen 0.4 and 0.5 to build and customize intelligent multi-agent applications for problem-solving
- Agent Collaboration & Communication – Explore how multiple AI agents interact, share knowledge, and coordinate tasks efficiently using structured protocols
- Building Scalable AI Applications – Develop AI solutions for real-world scenarios, such as customer support
Microsoft has re-architected the AutoGen framework in version 0.4, and this course is entirely based on the updated version.
The future of AI is multi-agent collaboration, where intelligent agents work together to solve complex tasks efficiently. This course is designed to help you master the AutoGen framework (v 0.4+), a powerful tool for building and orchestrating AI agents that interact, reason, and collaborate. Whether you're an AI enthusiast, a developer, or a researcher, this course will equip you with the skills to build and deploy scalable multi-agent applications.
What You Will Learn
Fundamentals of Multi-Agent Systems – Understand the core components of AI agents, their use cases, and how they enhance AI-driven workflows.
Setting Up Your Development Environment – Learn how to install Python, set up VS Code, create virtual environments, and install necessary dependencies.
Deep Dive into AutoGen – Explore AutoGen’s architecture, libraries, and capabilities, including working with OpenAI and open-source LLaMA models.
Key AutoGen Concepts – Master agent messaging, user proxy agents, assistant agents, streaming responses, and multi-modal AI integration.
Team-Based AI Agent Collaboration – Learn how to organize AI agents into teams, define termination conditions, and implement SelectorGroupChat for LLM-based agent selection.
Advanced Multi-Agent Concepts – Explore state management in AI workflows and dive into Magentic-One, a generalist multi-agent system for web and file-based tasks.
Hands-On Projects – Implement real-world AI agent applications, including:
Project 1: Develop a Streamlit-based AI Agent App, understand and optimize code with agent
Project 2: Build a Multi-Agent AI Chatbot for Customer Support that interacts with users of ecommerce portal and processes queries dynamically.
Who Should Take This Course?
AI & ML practitioners looking to build intelligent AI agents
Developers interested in AutoGen, AutoGen AgentChat and AI automation
Researchers exploring multi-agent collaboration
Anyone eager to develop AI-powered applications
By the end of this course, you will have hands-on experience building and optimizing AI-driven multi-agent systems using AutoGen, setting you up to develop next-gen AI solutions.
