Deploy AI agents in production with evidence, gates, and rollback
A production deployment checklist for AI agents: ownership, evaluation, permissions, human approval, observability, staged rollout, and rollback.
A production AI agent needs a release process, not just a deploy command. Freeze the workflow version, run a representative evaluation set, verify permissions, assign an owner, stage the rollout, monitor outcomes, and keep an immediate kill switch and rollback target.
Define the production unit
Version the workflow, model policy, tools, prompts, retrieval sources, memory behavior, and approval rules together. A model change can alter tool choice even when the prompt is unchanged, so the release boundary must include more than application code.
Gate the release
Run deterministic checks for schemas and permissions, then behavioral evaluations over real and adversarial cases. Review the full action trace. Require explicit sign-off from the technical owner and the business owner for workflows that can write to external systems.
- No shared personal credentials
- No unbounded write tool
- Known behavior for timeout, refusal, and partial failure
- Alerting tied to business outcomes, not token counts alone
Stage traffic and retain control
Begin with shadow traffic or a small cohort. Keep writes behind approval until the observed failure rate is acceptable for the process. Roll back the complete workflow version when behavior changes unexpectedly instead of attempting live prompt surgery.
Melaya traces and replay make run-level investigation possible. They do not remove the need for incident ownership, data retention rules, and downstream reconciliation.
Frequently asked questions
What is the biggest production risk for an AI agent?
The largest practical risk is often an over-broad action boundary: the agent can write more data, access more systems, or repeat an action more widely than the task requires.
Should every agent action require approval?
No. Read-only and low-impact reversible actions can run automatically. Approval should concentrate on consequential, ambiguous, external, or irreversible writes.
What should an AI agent trace contain?
At minimum: workflow version, model, input, retrieved context references, tool calls and results, approvals, errors, latency, and final outcome.
