AI Agent Evaluation: Metrics That Actually Matter
Track the right metrics for agent performance including task success, escalation rate, latency, and cost per successful outcome.
Measure outcomes, not only model quality
Useful agent evaluation combines technical and business metrics. A model can be linguistically strong yet operationally weak if it fails on tool calls or causes frequent escalations.
Core metrics to track
Start with task success rate, median completion time, escalation rate, and cost per successful task. Add quality sampling by human reviewers to catch silent failures and drift over time.
Use evaluation to prioritize roadmap
Evaluation data should drive product decisions: where to improve prompts, where to add retrieval, and where human oversight must stay. Ship improvements based on bottlenecks, not assumptions.
Turn the playbook into a build plan
Share your stage, constraints, and target outcome—we reply with a practical next step (often discovery or a scoped squad proposal).
Request a scoping response