How do you make AI agents reliable in production?

Reliability comes from the system around the model. I start by narrowing scope: one well-defined job, a handful of tools with clear descriptions, and fixed workflow steps wherever the path is predictable, leaving the open-ended loop only where it is really needed. Small per-step error rates compound over a long chain, so fewer and simpler steps matter.

Every tool call passes through validation: arguments against the schema, values against business rules, and the user's permissions. Actions are idempotent where possible, so a retry does not refund twice. The loop has a step limit, a cost budget and timeouts, and it detects repeated calls. Anything irreversible, such as sending money, deleting data or emailing a customer, needs explicit human approval, and the agent's credentials allow only what the task requires.

Then I make it observable. Every run is traced: prompts, tool calls, arguments, results, cost and latency. I keep a suite of realistic tasks and score whole trajectories, not just final answers, including whether the agent used the right tools and stopped correctly. Production failures are reviewed and added to that suite.