Retrieval-Augmented Generation (RAG) for Agents

RAG gives an agent facts at the moment it acts: instead of relying only on what a model learned in training, the agent retrieves relevant documents from a store and reasons over them. For agents this is how memory, policy and current state enter a decision. The hard part is not retrieval but provenance — an agent acting on a retrieved fact should be able to say where the fact came from and whether it was still true.

Purpose

Ground a decision in current, external facts

Store

Vector, keyword, or a knowledge graph

For agents

How memory and state enter a decision

Hard part

Provenance of a retrieved fact

Retrieval is the easy half

Fetching a relevant passage is largely solved. The agentic problem is accountability: when an agent takes an action because a retrieved document said something, the record should carry which document, which version, and when — otherwise a wrong action traces to a model rather than to the stale fact that actually caused it.

Questions

Is RAG the same as agent memory?

Related but distinct. Memory is what an agent retains across tasks; RAG is the retrieval step that pulls memory, or any external store, into the current decision.

Does RAG remove hallucination?

It reduces it by grounding answers in retrieved text, but a model can still misread or over-generalise, which is why provenance and verification remain necessary.