AgentOps Production Readiness Checklist (2026)

Use this checklist to ship an AI agent into production without sacrificing security, reliability, or cost control. It’s designed for founders, engineering leads, and operators.

1) Define the workflow and success criteria
- Write a one-page spec: what the agent does, what it must not do, and what “done” means.
- Choose 3–5 measurable KPIs (examples): task success rate, escalation accuracy, time-to-resolution, cost per run, policy violations per 1,000 runs.
- Decide the autonomy level: read-only, draft-only, or write/actions enabled.

2) Identity, permissions, and tool access
- Assign a dedicated service identity per workflow (avoid shared tokens across agents).
- Enforce least privilege: allowlist endpoints, methods, and data scopes.
- Add explicit approval gates for high-stakes actions (refunds, deletes, permission changes). Document thresholds (e.g., refunds > $200 require approval).
- Confirm audit logs capture: initiating user, run ID, timestamps, tool arguments, tool responses, and model/prompt version.

3) Evaluation suite (CI for behavior)
- Build a “golden tasks” set (minimum 50; target 200) with verifiable outcomes.
- Add an adversarial set (minimum 20) covering prompt injection and social engineering.
- Automate checks: schema validation, data policy checks (PII), correct field updates, and domain constraints.
- Set a deploy gate (example): ≥95% pass rate on golden tasks and zero critical policy violations.

4) Observability and debugging
- Enable tracing by default: prompts, tool calls, intermediate steps, final outputs.
- Add dashboards for: success rate, tool error rate, p95 latency, tokens/run, $/run, escalation rate.
- Create an on-call runbook: how to identify regressions, how to disable actions, how to roll back.

5) Cost and latency budgets
- Define hard budgets: max tokens/run, max tool calls, and max $/run.
- Implement fail-closed behavior: if budget is exceeded, escalate to human or stop safely.
- Load test with realistic traffic and worst-case inputs.

6) Change management and rollback
- Version prompts, tool schemas, policies, and model selection.
- Use staged rollout (e.g., 5% → 25% → 50%) with metric checks between stages.
- Prove rollback: rehearse a revert in staging; target <10 minutes to restore known-good behavior.

7) Data handling and compliance
- Confirm retention settings for traces and logs (and who can access them).
- Document data residency needs and whether vendor data is used for training.
- For regulated environments, ensure SOC 2 controls align with your internal policies.

Exit criteria for production autonomy
- Stable metrics for 2 consecutive weeks at target traffic.
- Proven rollback path and a tested incident response procedure.
- Security sign-off on permissions and auditability.
- Finance sign-off on unit economics ($/outcome), not just $/message.