AI Production Control Plane Checklist (2026)

Use this to harden an AI feature from “demo” to “product.” Keep it vendor-agnostic by default.

1) Interface contract (portability)
- Define a single internal interface for “generate()” (and “tool_call()” if you expose tools).
- Store provider-specific code behind adapters.
- Require explicit model identifiers and version tags in config (no silent defaults).
- Add a documented fallback policy (what happens on timeout, rate limit, or provider outage).

2) Prompt + policy versioning (reproducibility)
- Treat prompts, system policies, and tool schemas as versioned artifacts.
- Record which versions served each production request (traceable to a commit or artifact hash).
- Ban untracked prompt edits in dashboards as the primary path to production changes.

3) Tool layer (reliability)
- Define tool arguments with a strict schema (JSON Schema / Zod / Pydantic).
- Validate inputs server-side; return structured errors the model can correct.
- Make side-effect tools idempotent using an idempotency key per user action or turn.
- Add rate limits and timeouts per tool; log tool latency and failure codes.

4) Evaluation gates (behavior regression)
- Maintain a golden set of real tasks from production (redacted) + edge cases.
- Add three eval types: task success, policy compliance, cost/latency budgets.
- Run evals in CI for any model/prompt/tool change.
- Block release if critical evals regress; document exceptions with an owner and expiry.

5) Observability (debuggable systems)
- Implement end-to-end tracing: request → retrieval → model → tools → final output.
- Redact secrets and PII in logs by default; enforce access controls.
- Create a “one-click replay” path in staging for a recorded trace.

6) Data governance (enterprise-ready)
- Document where user inputs, retrieved documents, and outputs are stored.
- Define retention windows for prompts/traces; enforce deletion.
- Ensure your vendors’ data usage settings match your customer promises.

7) Change management (the AI change log)
- Maintain an internal AI change log with: date, owner, change type, versions, eval results, rollout plan.
- Require roll-back steps for every behavior change.
- Review AI incidents like production incidents: root cause, fix, regression test added.

If you can’t pass items 1–4, do not scale traffic. Fix the control plane first.