What is agentic RAG?
A retrieval architecture in which an AI agent decides when, where, and how to retrieve knowledge during a multi-step reasoning process instead of always running one fixed retrieval step before generation.
Why is this important?
Agentic RAG can combine multiple knowledge systems, reformulate queries, inspect intermediate evidence, and retrieve again when information is missing. This flexibility is valuable for complex research but adds latency, cost, and control risk.
How it works
The agent evaluates the task, selects a retrieval tool, generates a query, inspects results, updates its plan, optionally searches another source or graph, and stops when evidence satisfies its completion criteria.
Technical example
An account-research agent searches CRM records, traverses company relationships, retrieves product documentation, checks recent web sources, and cites the evidence used in its final brief.
Implementation notes
Bound retrieval steps, constrain allowed sources, validate generated queries, enforce permissions in every tool, detect repeated searches, record provenance, and evaluate whether additional retrieval materially improves the answer.
