Metrics and Eval Plan for Enterprise AI Agents

Why Evals Matter

Enterprise AI Agent work cannot be judged by demo quality alone. A useful agent must be evaluated against representative workflows, negative cases, human review outcomes, and operational constraints.

Business Impact Metrics

• Time saved per workflow.
• Monthly task volume affected.
• Cost-saving estimate.
• Throughput improvement.
• Reduction in rework or handoff delay.

Task Quality Metrics

• Task success rate.
• Correct answer rate.
• False positive and false negative rate.
• Citation coverage and evidence completeness.
• Output-format compliance.

Human Review Metrics

• Manual intervention rate.
• Human correction rate.
• Escalation rate.
• Reviewer satisfaction.
• Average review time.
• Approval-to-rejection ratio.

System Reliability and Governance Metrics

• p50/p95 task duration.
• Retry rate, tool-call failure rate, and timeout rate.
• Cost per successful task.
• Audit-log completeness and source lineage coverage.
• Policy-violation count and sensitive-data exposure incidents.
• Human approval coverage for responsibility actions.

Eval Dataset Design

1. Normal representative cases.
2. Edge cases with missing or conflicting evidence.
3. Negative cases that must escalate.
4. Regression cases from historical failures.

Release Gates

Example Eval Cases