Originally published on hexisteme notes, part of a series on building and running an AI agent fleet.
To stop an AI research or RAG agent from presenting its own inferences as retrieved facts, split the work so the LLM never decides what is a fact: let the LLM only extract and summarize, and let a deterministic, non-LLM pipeline do all scoring, cross-checking, and labeling. Tag a claim FACT only when a rule is satisfied — corroboration by ≥2 independent sources, or one official API — and downgrade everything else to INFERENCE. Because labeling is rule-based, the agent can't launder a guess into a fact, and the same query produces the same labels every run.
An AI agent that gathers information has two kinds of output tangled together: things it retrieved and things it concluded. A web page said the market was 1.2 trillion won (retrieved); the agent inferred the market is "growing fast" (concluded). Both come out in the same confident prose. For anything you'll act on, that blend is the problem — you can't tell which sentence is grounded and which is the model filling a gap.
The fix isn't a better prompt ("only state facts you can cite"). Prompts are probabilistic; under pressure the model reverts. The fix is structural: take the fact/inference decision away from the model entirely and put it in code.
