Before the first user
- Define what a successful and unacceptable output looks like.
- Run a representative evaluation set, including refusal and missing-data cases.
- Keep secrets server-side and limit provider permissions.
- Set rate limits, budgets, timeouts, and retry behavior.
- Add logs for model, prompt version, tool calls, latency, cost, and outcome.
- Decide which actions require a human approval.
Roll out gradually
Start with a small audience or shadow mode. Compare AI suggestions with the existing human process, inspect failures, and make the rollback path boring and reliable. A gradual rollout gives you real evidence before the system becomes part of a critical process.
What to monitor after launch
| Signal | Why it matters |
|---|---|
| Task success | The output still solves the user’s job |
| Correction rate | Quality problems are not hidden by fluent prose |
| Latency and errors | The system remains usable |
| Cost per approved task | The feature can support its economics |
| Privacy/security events | The system is not creating a larger risk |