Agentic RAG

What is Agentic RAG?

Agentic RAG replaces the fixed embed-then-generate pipeline with a workflow: a planner decomposes the question, retrievers route per sub-question, a validator checks evidence, and a synthesizer cites. It is the dominant pattern for cross-system enterprise questions that traditional RAG cannot answer reliably. See Agentic RAG vs Traditional RAG.

What is an example of Agentic RAG?

For a supplier-risk question, an agent searches contracts, resolves the supplier’s parent company, queries incident records, follows dependencies in a graph, checks evidence completeness, and produces a cited synthesis.

How is Agentic RAG different from related concepts?

Traditional RAG normally follows one predefined retrieve-then-generate path. Agentic RAG dynamically decides what to retrieve and whether additional steps are needed.

Why it matters for on-premise & regulated AI

Agentic RAG lets the agent decide what to retrieve, when, and whether to search again — which means retrieval decisions themselves become agent behavior that needs governing. Running agentic RAG privately keeps the query rewrites, intermediate retrievals, and reasoning steps (which often contain more sensitive context than the final answer) inside your boundary and inside a single auditable trace.

What should enterprises evaluate for Agentic RAG?

• Use agentic retrieval only when simpler search, hybrid retrieval, or query decomposition cannot meet the task reliably.
• Measure evidence completeness, source quality, citation support, permissions, tool choice, step count, latency, and cost.
• Set termination, retry, and abstention rules so the agent cannot search indefinitely or synthesize without sufficient evidence.

Related terms

• RAG (Retrieval-Augmented Generation)
• GraphRAG