Building Multi-Agent Systems with CrewAI and AutoGen

As artificial intelligence evolves, single-prompt interactions are being replaced by collaborative, multi-agent systems that can handle complex workflows autonomously. If you want to understand how multiple AI agents can work together to solve intricate problems, this text-based course is your starting point. You will transition from understanding basic AI concepts to designing functional, coordinated AI teams. By exploring the core mechanics of modern agentic frameworks, you will learn how to assign roles, manage memory, and orchestrate tasks using CrewAI and AutoGen. What you will learn: Understand the foundational concepts and terminology of multi-agent systems and autonomous AI; Design custom AI agents with specific roles, goals, and context using CrewAI; Configure conversational patterns and collaborative workflows using AutoGen; Apply fundamental prompt engineering techniques to improve agent reliability and reasoning; Integrate basic tool calling and retrieval-augmented generation (RAG) concepts to expand agent capabilities; Coordinate complex tasks by managing agent interactions, memory, and task delegation. The course begins with essential terminology and the basic architecture of agentic systems before moving into practical implementation. You will read through clear explanations, architectural patterns, and written code snippets that demonstrate how to build and scale your own AI agent workflows. This course is designed entirely for beginners. No prior machine learning expertise or advanced programming experience is required just a curiosity about the future of AI. Start reading today and discover how to build your first autonomous AI team.