Answers
How do you stop an AI agent from hallucinating in production?
Ground every answer in retrieved source material and require a citation, add confidence scoring that routes uncertain cases to a person, and log every action for audit. The fix is almost never a better model — it's the engineering around it.
"Hallucination" usually isn't the agent inventing something from nothing — it's the agent answering confidently from memory when it should have looked something up, or acting on a document it half-remembers instead of the one actually in front of it. The fix addresses that directly: every factual claim gets grounded in retrieved source material with an explicit instruction to answer only from what was retrieved, and to say so plainly when the answer isn't there.
Confidence scoring closes the remaining gap. Even a well-grounded agent will occasionally land on a genuinely ambiguous case, and the difference between a safe system and a risky one is what happens next — a low-confidence response should route to a person for review rather than ship on its own, every time, with no exceptions carved out for the case that "probably" doesn't need it.
We built a document-processing agent for a client where an early version occasionally misread a field on a scanned form and acted on the wrong number. Grounding every extraction in the specific region of the document it came from, with a confidence score attached, and routing anything below a defined threshold to a human reviewer, cut the error rate to effectively zero — not because the model got smarter, but because the system around it stopped letting a low-confidence guess through unchecked.