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.
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.
Where this lives in the estate
FlashyOS — where memory and provenance meet on the mesh
Keep reading
By Michael Gord · published 2026-10-04 · part of the Agentic Encyclopedia. Dates are the day of publication; events are cited at their own dates.