evaluation

Evaluation rubrics define scoring dimensions like factual accuracy, completeness, and citation quality. Create structured rubrics that score each dimension, then apply them to agent outputs against ground truth to obtain per-dimension and overall performance scores.

Test-set design involves preparing tasks with expected ground-truth outputs and corresponding agent responses. Structure test sets to cover diverse scenarios, then run evaluations across the full set to identify performance patterns and regressions.

LLM-based judgment combined with human evaluation scales validation across large test sets. LLMs score outputs against rubrics while humans validate critical or ambiguous cases, reducing manual effort while maintaining quality assurance.

Production monitoring uses dashboards and alerting to track agent performance metrics in real time. Continuous evaluation pipelines surface quality issues automatically, enabling rapid detection and response to performance drops.

Multi-dimensional rubrics assess factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. These dimensions collectively measure whether context engineering decisions improve agent output quality and user trust.

Yes, evaluation requires expected ground-truth outputs to compare against agent responses. Ground truth provides the benchmark for scoring each dimension, enabling objective measurement of agent performance and context-engineering decisions.