Foundations of Agentic AI: Building Multi-Agent Systems

The shift from static language models to autonomous AI agents is transforming how we build software and automate complex workflows. To stay ahead in this rapidly evolving landscape, you need to understand how to design systems that can reason, plan, and use external tools independently. This course guides you from the fundamental principles of agentic architecture to designing collaborative multi-agent environments. You will learn how to structure agent memory, integrate external APIs, implement Retrieval-Augmented Generation (RAG) patterns, and establish reliable safety guardrails to build robust, autonomous systems. What you'll learn: - Understand the core anatomy of an AI agent, including reasoning loops, planning strategies, and memory systems - Build single-agent workflows that interact with external databases and APIs - Design multi-agent systems using frameworks like CrewAI and LangGraph to enable collaborative problem-solving - Implement Retrieval-Augmented Generation (RAG) patterns utilizing vector databases for grounded decision-making - Apply safety protocols, human-in-the-loop checkpoints, and guardrails to ensure predictable agent behavior - Configure basic monitoring and evaluation patterns to track agent performance and trace execution paths You will start with the basic terminology and conceptual building blocks of agentic design before moving on to step-by-step written walkthroughs and practical code implementations. The curriculum progresses naturally from simple single-agent tasks to complex, multi-agent coordination scenarios. This course is designed for beginners, software developers, and product creators looking to enter the world of autonomous AI, requiring only a basic understanding of programming concepts and no prior AI experience. Start reading today to master the next generation of autonomous AI systems.