Evidence-first authority page
AI Agent Deployment Checklist: Before Going to Production
A practical pre-launch checklist for moving an AI agent from prototype to production deployment.
Direct answer
Before deploying an AI agent to production, confirm: least-privilege tool permissions are in place, observability/tracing captures full execution traces, a human-approval gate exists for irreversible actions, the agent has been tested against a representative sample of real historical tasks, and there's a clear rollback plan if the deployment needs to be reverted.
Last reviewed 2026-08-29
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The checklist
- Permissions — every tool the agent can call is scoped to the narrowest access the task needs, not broader access granted for convenience.
- Observability — full traces (model calls, tool calls, results) are logged for every session, not just final outputs.
- Human checkpoints — irreversible or high-consequence actions require explicit approval before execution.
- Testing — the agent has been run against a representative sample of real historical tasks, not just a handful of happy-path examples.
- Rollback plan — there's a clear, tested way to disable or revert the agent's deployment if something goes wrong after launch.
- Rate limiting — the agent's request volume is bounded so a bug or loop can't run away with API costs or hit provider rate limits unexpectedly.
Start narrow, expand deliberately
Launch to a narrow scope or a small percentage of traffic first, monitor closely, and expand scope only after the initial deployment demonstrates the reliability your testing predicted — see how to evaluate AI agents for the fuller testing framework this checklist assumes has already been applied.