Agentic RAG

RAG systems have changed how AI accesses external knowledge, but agentic RAG transforms retrieval into a reasoning-driven process. This course unpacks the fundamentals of agentic intelligence and combines reasoning with retrieval to achieve higher factual accuracy and autonomy. You’ll explore the anatomy of an agent, from its memory and tools to the orchestration logic that drives self-directed behavior. Through hands-on lessons, you’ll build an AI research assistant using LlamaIndex by assembling its tools, defining retrieval strategies, and designing reasoning loops that enable self-correction. You’ll learn how to debug, evaluate, and refine your agentic workflows using metrics like faithfulness, context recall, and answer quality, bridging theory and practice. Finally, you’ll architect scalable, dependable systems with dependency graphs and deployment guardrails, equipping you to take your agentic RAG project from prototype to production-ready reliability.