Building Multi-Agent Systems using LangGraph and Autogen

This course introduces the essentials of multi-agent AI systems using LangGraph and Autogen, combining architectural understanding with hands-on development of intelligent, collaborative agents. Designed to give you both conceptual foundations and practical experience, it explores how agent-based systems are redefining automation, decision-making, and AI-powered problem-solving. Through guided lessons and coding demonstrations, you’ll learn how to construct multiple AI agents that communicate, plan, and execute tasks autonomously. You will work with LangGraph to structure agent workflows and use Autogen to enable dynamic interaction between agents. The course covers key topics such as agent communication, reasoning loops, task decomposition, and coordination for real-world applications like research, analysis, and workflow management. By the end of this course, you will be able to: • Understand the architecture, behavior, and lifecycle of multi-agent systems. • Build intelligent agents using LangGraph and Autogen for collaborative problem-solving. • Implement reasoning and communication strategies for effective task orchestration. • Evaluate and optimize multi-agent performance for scalability and reliability. This course is ideal for developers, data scientists, and AI practitioners who want to learn how to design and deploy intelligent multi-agent systems that can perform complex workflows autonomously. A basic understanding of Python programming and familiarity with machine learning or AI concepts will be helpful, but no prior experience with LangGraph or Autogen is required. Join us to explore the future of autonomous AI systems and learn how to build, coordinate, and optimize agents that think, collaborate, and act intelligently

• Design and build multi-agent systems that reason, plan, and collaborate on shared goals
• Implement communication and coordination strategies using LangGraph and Autogen
• Evaluate system performance through structured tasks and adaptive reasoning loops
• Optimize multi-agent workflows for reliability, scalability, and autonomous execution

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work