AgentOps Production Readiness Checklist (2026 Edition)

Use this checklist to put an AI agent into production without trading away security, reliability, or cost control. It’s built for founders, engineering leads, and operators.

1) Define the workflow and what “success” means
- Write a one-page spec: what the agent does, what it must never do, and where it hands off to a human.
- Pick measurable KPIs that match the workflow (examples): task success rate, correct escalation rate, time-to-resolution, cost per run, policy violations per run.
- Set an autonomy level: read-only, draft-only, or write/actions enabled.

2) Identity, permissions, and tool access
- Create a dedicated service identity per workflow (avoid shared tokens across unrelated agents).
- Enforce least privilege: allowlist endpoints, methods, and data scopes.
- Add approval gates for high-stakes actions (refunds, deletes, permission changes). Document what requires approval and who can approve.
- Confirm audit logs capture: initiating user, run ID, timestamps, tool arguments, tool responses, and the model/prompt/policy version.

3) Evaluation suite (behavior gets a CI gate)
- Build a golden task set that reflects production work and has verifiable outcomes.
- Maintain an adversarial set for prompt injection and social engineering attempts.
- Automate checks: schema validation, policy checks (PII/secrets), correct field updates, and domain constraints.
- Block releases on regressions and critical policy violations.

4) Observability and debugging
- Enable tracing by default: prompts, tool calls, intermediate steps, and final outputs.
- Add dashboards for: success rate, tool error rate, tail latency, tokens per run, cost per run, and escalation rate.
- Write an on-call runbook: how to spot regressions, how to disable actions, and how to roll back.

5) Cost and latency budgets
- Define hard caps: max tokens per run, max tool calls, and a max spend per run.
- Implement fail-closed behavior: if a budget is exceeded, stop safely or escalate to a human.
- Load test with realistic traffic and worst-case inputs (timeouts, tool failures, long threads).

6) Change management and rollback
- Version prompts, tool schemas, policies, and model selection.
- Use staged rollout with metric checks between stages.
- Rehearse rollback in staging until it’s routine.

7) Data handling and compliance
- Set retention rules for traces and logs, and restrict who can access them.
- Document data residency needs and what vendor data is used for training (if any).
- For regulated environments, confirm controls align with your internal security and compliance requirements.

Exit criteria for production autonomy
- Metrics are stable over time at real traffic.
- Rollback is proven and the incident response path is tested.
- Security signs off on permissions and auditability.
- Finance signs off on unit economics tied to outcomes, not messages.