Enterprise AI Evaluation
AI Agent Evaluation Launch Checklist
A practical checklist for Copilot Studio, RAG, document AI, and enterprise AI agents before production.
Most AI agents look impressive in demos. The real question is whether they can survive production: messy users, outdated knowledge, permission boundaries, tool errors, cost and latency surprises, and regressions after updates.
Checklist Contents
What's inside
- 10 launch-readiness questions
- A 30-7-3-4-1 implementation plan
- Core scenario and test-set templates
- RAG and groundedness checks
- Grader selection guide: rules, LLM judges, and human review
- Failure taxonomy for debugging agents
- Capability suite vs. regression suite separation
- Release gates for write actions, cost, latency, and anomaly spikes
- Online monitoring loop for continuous improvement
Audience
Who this is for
- Copilot Studio builders
- Analytics, BI, and automation teams
- Healthcare, HR, finance, and public-sector AI teams
- Teams moving from AI demo to production
- Leaders who need evidence before approving an internal agent launch
Production Readiness
Why it matters
Agent evaluation turns vague feedback like ‘the agent is not good enough’ into specific, reviewable evidence: which scenario failed, what trace shows the failure, which component caused it, and what release gate should stop it from reaching production.
Operating Model
The operating model
Real failures + isolated harness + repeated trials + calibrated graders + trace review + capability/regression separation + release gates + online monitoring.
Next Step
Download the AI Agent Evaluation Checklist