evaluation

To evaluate agent performance, you score outputs against structured rubrics covering factual accuracy, completeness, citation accuracy, source quality, and tool efficiency with weighted scoring to quantify results and guide improvements.

The best way to monitor agent outputs in production is by applying structured rubrics continuously through an evaluation runner, creating a feedback loop that tracks quality across iterations and flags regressions.

You validate AI agent responses by selecting a structured test set, defining evaluation rubrics, and running an evaluation to generate a performance report that quantifies factual accuracy and completeness.

Yes, you can score citation accuracy and source quality separately by configuring multi-dimensional rubrics with weighted scoring, allowing you to isolate and quantify specific dimensions of agent performance.

Yes, the framework supports automated testing and benchmarking by providing tooling for test sets, evaluation runners, and continuous monitoring to quantify agent performance across multiple iterations.

You need weighted scoring when benchmarking AI agent outputs to prioritize critical dimensions like factual accuracy over secondary metrics like tool efficiency, ensuring the evaluation reflects actual performance priorities.