evaluation

Evaluating agent performance requires a structured rubric covering factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. You provide a test set and rubric to the evaluation engine, run evaluations, and review per-dimension scores to guide iterative improvements.

Automated test-set driven evaluation works by applying defined rubrics across a test set to generate automated scoring with optional human review. This ensures scalable quality control by measuring multi-dimension scores and overall performance across all tests.

Yes, you can monitor agent quality and pass rates in production. Production monitoring and alerting track real usage pass rates in real time, enabling continuous improvement workflows across development cycles and ensuring reliability across deployments.

Validating new agent configurations and context pipelines is best achieved through test-set driven evaluation. By comparing per-dimension scores across configurations, you quantify performance differences and validate context engineering choices systematically.

An agent evaluation rubric should include dimensions for factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. These multi-dimension rubrics provide a balanced performance assessment of agent systems and context usage.

Yes, test-set evaluation supports human review alongside automated scoring. This combination ensures scalable quality control by allowing human reviewers to validate or override automated rubric scores during the evaluation cycle.