Production AI Agent Rollout Checklist (2026)

Use this checklist to take an AI agent from idea to reliable production deployment. The goal is outcome quality, controlled risk, and predictable unit economics.

1) Define the job and the “done” condition
- Write a single sentence: “The agent succeeds when ____.”
- Define objective success metrics (e.g., ticket resolved, lead enriched, invoice matched).
- Set error severity levels (minor annoyance vs. financial/compliance risk).

2) Choose the narrowest viable workflow
- Start with one high-volume, repeatable process.
- Avoid open-ended tasks until you have strong eval + controls.
- Identify the top 5 edge cases that break automation today.

3) Inventory tools and harden APIs
- List every API/action the agent will call.
- Add typed schemas for tool inputs/outputs.
- Require idempotency keys for every write action.
- Define timeouts and retry policy per tool.

4) Create an execution policy (guardrails)
- Max tool calls per task (e.g., 6).
- Max wall time (e.g., 60 seconds) and max cost per task (e.g., $0.25).
- Approval tiers: auto-execute (low risk), approve (medium risk), block (high risk).

5) Establish identity and least privilege
- Create a dedicated service account per agent role.
- Grant only the minimum permissions needed for the workflow.
- Log which user initiated the request and which agent identity executed actions.

6) Build an evaluation set before launch
- Collect 100–500 representative real tasks.
- Add synthetic edge cases (missing data, ambiguous requests, tool failures).
- Decide grading: deterministic checks first, then LLM-judge as secondary.

7) Implement observability
- Log: request_id, user_id, agent_role, model, tool calls, outcomes, cost, latency.
- Trace tool calls end-to-end (OpenTelemetry-style spans recommended).
- Dashboard core metrics: success rate, escalation rate, severe error rate, p95 latency, p95 cost.

8) Run shadow mode (2–4 weeks)
- Agent proposes actions; humans execute.
- Compare agent outcome vs. human outcome.
- Track where the agent fails: retrieval gaps, tool schema issues, reasoning errors.

9) Launch staged autonomy
- Start with low-risk lanes only.
- Add approvals for financial/compliance actions.
- Implement rollback/kill switch (feature flag) and on-call ownership.

10) Operate and improve continuously
- Weekly eval reruns to catch regressions when prompts/models/tools change.
- Postmortems for severe errors.
- Quarterly permission review (least privilege tends to drift).
- Revisit unit economics monthly: cost per successful outcome and cost of rework.

Success Criteria (quick rubric)
- Quality: >90% success on “easy lane” tasks; severe errors <1%.
- Safety: approvals + audit logs for sensitive actions.
- Economics: stable cost per outcome within your ROI model.
- Operability: traced requests, dashboards, and a clear owner/team.