Agentic RAG Lets AI Systems Reason, Verify and Retry Before Answering

Traditional RAG pipelines follow a fixed retrieve-then-generate approach, which can fail when retrieved documents are irrelevant or insufficient to answer a query. Agentic RAG addresses this by enabling AI systems to reason about retrieval, verify answers, refine searches, and escalate to humans when confidence is low. A developer has published a set of open-source notebooks demonstrating various Agentic RAG patterns built using LangGraph. The notebooks are designed as self-contained, practical examples that developers can study and adapt for their own AI projects. A free version is available on GitHub, with an advanced paid version offered separately.