evaluation

Evaluating agent performance across non-deterministic runs requires a structured rubric-based scoring model with per-dimension weights. This framework simulates agent outputs and tracks improvements over time to deliver actionable quality insights.

Rubric-based scoring for agent quality assurance is a multi-dimensional evaluation method using per-dimension weights. It provides a structured, repeatable framework to measure agent performance and distinguish quality from luck.

You manage test sets for evaluating AI agent systems by using test-set management features to organize diverse evaluation scenarios and metrics. This enables an evaluation runner to execute outputs and produce actionable per-test results.

Yes, you can monitor production agent quality and alert on degradation. The framework includes production monitoring to surface quality trends and alert you when live use cases experience performance degradation.

Yes, the agent evaluation framework supports validating context engineering choices. It applies a scalable evaluation pipeline with test-set management and rubric-based scoring to measure how context configurations perform.

The best way to track agent improvements over time is by running a full evaluation cycle on a representative test set. This generates actionable quality metrics using rubric-based scoring and production monitoring to surface trends.