How are you preventing hallucinations from turning into actions in production AI agents?

Hi guys, I've been thinking about how hallucinations become a pretty different problem once you move from chatbots to agents. With a normal chatbot, the model might confidently give someone the wrong refund policy. Bad, obviously. But if that same model has tools and permission to actually issue refunds, now the hallucination can turn into a real action. For example: Actual policy: refunds within 30 days. Customer is at day 45. Agent hallucinates that the policy is 60 days and calls the refund API. At that point, improving the prompt doesn't feel like enough of a safety mechanism. The approach that makes the most sense to me is separating what the model is allowed to reason about from what it is allowed to decide or execute. For example, instead of asking: “Is this customer eligible for a refund?” and trusting whatever the model says, the agent could retrieve: actual order date refund policy account status previous refunds Then eligibility could potentially be validated separately before the refund tool is available. Basically, don't let the model guess something that your systems already know. But I'm curious how people are actually implementing this in production. Do you rely mostly on RAG + prompting? Do you validate tool calls separately? Do you use deterministic rules around sensitive actions? Do you have another model verify the first model? Or do you require human approval above certain thresholds? It feels like hallucination rates by themselves aren't even the most useful metric for agents. “How often can a hallucination turn into a real-world action?” might be the more important question. Would be interested to hear how people are designing around this. Thanks!