evaluation

To evaluate agent outputs against rubrics, you can use a structured scoring engine that measures factual accuracy, completeness, and tool efficiency against ground truth. It generates an overall score with detailed dimension results.

LLM-as-judge evaluation is an automated scoring mechanism that validates agent performance at scale. It integrates with human evaluation to ensure reliable production monitoring and detect regressions across multi-dimensional metrics.

To run a sample test set for agent evaluation, install Python 3.x and execute the evaluator script with the sample flag. This initiates the structured scoring engine to generate overall scores and detailed dimension results.

Yes, agent evaluation supports multi-dimensional scoring across factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. This rubric-driven approach ensures comprehensive validation of agent outputs against ground truth.

Rubric-driven evaluation is used for agent systems to enable scalable, reliable validation through multi-dimensional scoring. It combines LLM-as-judge integration with human evaluation to detect regressions and guide continuous improvement.

Yes, you can detect regressions in agent performance using production monitoring and test-set management. The evaluation process preserves a structured scoring engine to track metrics and guide continuous improvement over time.