How to evaluate an AI agent before production is the difference between a controlled pilot and a public incident. Demos are not evaluation.
Build an evaluation set that hurts
- Real transcripts/tickets — including angry ones
- Adversarial prompts (jailbreaks, PII requests)
- Tool-failure scenarios
- Multilingual samples if you support them
- Edge cases legal/compliance cares about
Metrics that matter
- Task success / contain rate
- Hallucination or policy violation rate
- Tool call accuracy
- Average handle time vs baseline
- Human escalation quality
Human review process
Sample conversations weekly. Score with a rubric, not vibes. Freeze prompt/tool versions that pass gates. Align with AI guardrails.
Go-live checklist
- Kill switch and traffic percentage rollout
- On-call owner for model/tool outages
- Logging retained per policy
- Customer disclosure where required
- Rollback to human-only queue
How we run this with clients
Pilots under AI agents include an eval harness before scale. Cost context: AI agent development cost.
Design your eval gate
Book a call and we will help define pass/fail thresholds for your use case.
