evaluation

To evaluate agent performance, you define a multi-dimensional rubric covering factual accuracy and tool efficiency, input ground-truth references, and run the pipeline to compute weighted scores and pass/fail statuses.

LLM-as-judge evaluation is an automated scaling mechanism that scores agent outputs against defined rubrics, enabling continuous production monitoring by tracking pass rates and sending alerts for quality degradation over time.

You set up the testing pipeline by selecting a rubric, defining a test set with ground-truth expectations, running the evaluation on a batch of agent outputs, and reviewing the resulting dimension averages and overall scores.

Yes, human-in-the-loop judgments can be integrated alongside automated metrics to evaluate large test sets, capturing subjective dimensions like citation quality and source reliability for reliable quality assurance.

Quantifying agent tool efficiency requires optional tool usage metrics ingested alongside ground-truth references, allowing the evaluation framework to calculate weighted scores across factual accuracy, completeness, and tool efficiency dimensions.