evaluation-quality

Agent quality measurement applies automated evaluations across dimensions like correctness and safety, comparing outputs against ground truth to identify performance gaps and track reliability over time.

Ground-truth comparison evaluates agent responses against verified reference data to quantify accuracy. It provides standardized, measurable feedback on correctness and helps teams track regression in production agents over time.

You can integrate human feedback into agent evaluation through structured feedback loops that run alongside automated eval types. This combination scores quality dimensions while capturing subjective human input to drive continuous improvement and governance.

Yes, you can detect production agent regressions by running trend analysis on quality metrics. The evaluation process tracks scoring dimensions over time, applying reporting hooks to alert teams when performance drops below established thresholds.

The best way to score agent quality dimensions is through standardized automated evaluation that measures correctness, helpfulness, and safety. This approach applies consistent metrics across responses, integrating ground-truth comparison to quantify agent usefulness.

Yes, telemetry data is required to evaluate agent quality and track trends effectively. Production telemetry provides the response logs needed for automated evaluation, enabling trend analysis and regression detection across your deployed agents.