Building AI Agents That Don't Hallucinate: A

An AI agent that works in a demo and an agent that works reliably in production are two different things. The gap is usually not the model — it's the scaffolding around it. After building agents for several clients, the patterns that matter most have become clear.

Tool schema quality is underrated. If your function definitions are vague, the model will interpret them creatively. Write schemas the way you'd write an API contract for a junior engineer: explicit parameter names, type annotations, enum constraints where applicable, and clear descriptions of what an error response looks like. Half of the hallucination I see in production agents is the model improvising because the tool contract was too loose.

Add a confidence step before any irreversible action. Ask the model: 'Before executing this action, rate your confidence 1–10. If below 7, state what additional information would help.' It sounds like prompt theatre, but it catches the 'I'm not sure but I'll try anyway' failure mode surprisingly well.

Finally: build rollback before launch. Every destructive action the agent can take should have a corresponding undo path. This isn't only about bugs — it's about client trust. The moment a client can say 'something went wrong' and you can say 'I can roll that back in 30 seconds,' the relationship changes.