Agentic RAG
An advanced retrieval-augmented generation approach where AI agents autonomously decide what to search for and when.
Definition
Agentic RAG extends traditional retrieval-augmented generation by giving AI agents the autonomy to decide when, what, and how to search external knowledge sources. Instead of a fixed retrieve-then-generate pipeline, agentic RAG systems can iteratively refine their searches, consult multiple sources, evaluate result quality, and decide when they have sufficient information to generate a response.
This mimics how a human researcher works: start with a question, search for initial information, identify gaps, search again with refined queries, cross-reference sources, and synthesize findings into a coherent answer.
Why It Matters
Standard RAG often retrieves irrelevant documents or misses critical context because it relies on a single retrieval step. Agentic RAG dramatically improves answer quality for complex questions that require multi-step reasoning or information from disparate sources.
For businesses with large knowledge bases, internal documentation, or client archives, agentic RAG means AI assistants that can actually find and synthesize the right information rather than returning superficial or incomplete answers.
Examples in Practice
An account manager asks an AI system about a client's full history. The agentic RAG system first searches the CRM, identifies relevant campaigns, then pulls performance reports for each campaign, cross-references with billing records, and synthesizes a comprehensive client overview.
A legal team's AI assistant receives a contract question. It searches the contract database, finds the relevant clause, then autonomously searches for related precedents and internal policies before generating its analysis.