AI agent demos are easy. Reliable agent behavior in production is hard. The biggest mistake is measuring quality by how smart the agent sounds instead of whether it completes useful work.
Start with narrow, repeatable workflows
Good first use cases:
- Triage support tickets.
- Draft release notes from merged PRs.
- Prepare first-pass customer summaries.
Avoid strategic tasks where "correct" depends on hidden context.
Write an agent boundary contract
Define before launch:
- What the agent can do automatically.
- What always requires approval.
- What data sources are trusted.
- What triggers fallback to manual flow.
Without this contract, incidents are hard to diagnose.
Measure outcomes, not conversation quality
Use a compact scorecard:
- Task completion rate.
- User correction rate.
- Time saved vs manual baseline.
- Escalation rate and resolution time.
A fluent response with low completion is still a failed product experience.
Roll out in four stages
- Internal only with synthetic cases.
- Limited beta with explicit opt-in.
- Production for one workflow.
- Expansion only after stable metrics.
Promote stages only when completion and correction metrics hold for at least two weekly cycles.
