Agentic RAG

Agentic RAG describes a retrieval-augmented generation approach in which an AI agent actively decides what to search, how to refine retrieval, which tools to use, and when enough evidence has been gathered. It matters because dependable automation requires clear triggers, tools, state, approvals, error handling, and ownership rather than model output alone. Related concepts often include governance, integration, monitoring, evaluation, and human oversight.