1. A demo proves possibility; production proves reliability

A controlled demo can look impressive, but production exposes missing data, edge cases, latency, cost and user behaviour.

2. Monitor more than uptime

Traditional uptime is not enough. Teams need to know whether the agent is answering well, using the right tools and escalating the right cases.

3. Track tool calls, latency, cost and escalation rate

Operational dashboards should show how often tools are used, where failures happen, how long tasks take and when human help is needed.

4. Evaluate answer quality and source grounding

Review samples against expected answers, approved sources and citation quality. Track regressions when prompts, models or knowledge bases change.

5. Review failed tasks and near misses

Failures are useful signals. They show where workflow rules, data quality, permissions or human approval gates need improvement.

6. Update knowledge bases and workflows safely

Changes should be tested before release, especially when they affect high-volume or high-risk tasks.

7. Keep a feedback loop with human users

Users should be able to correct outputs, flag uncertainty and request workflow changes. Their feedback is the fastest way to improve a production agent.