How do you reduce hallucinations in an AI agent?

Ground every claim in something the agent actually retrieved or looked up, not the model’s own memory, and check what it wrote afterward for the claims that still don’t trace back to anything. Grounding is the part that actually works: an answer built from a document, a tool result, or a database row can be checked against that source, while an answer pulled from the model’s training data can’t be checked against anything at all.

Two fixes people reach for first don’t reliably help. A bigger model doesn’t hallucinate less in any consistent way, and setting temperature to zero doesn’t stop it either, since a confident wrong answer isn’t a random one. Retrieval-augmented generation is the standard way to give the model something real to ground in, but grounding still has to be checked after the fact, because a model can retrieve the right document and still ignore it.