evaluation

To evaluate agent performance using multi-dimensional rubrics, you compare agent outputs against ground-truth data and optional tool-call logs to generate per-dimension scores for factual accuracy, completeness, source quality, and tool efficiency.

Quality gates in an agent evaluation pipeline are automated thresholds that prevent production regressions by triggering alerts when agent pass rates or evaluation scores fall below predefined rubric levels.

Yes, you can benchmark multi-agent setups against ground truth data. The evaluation framework enforces a rubric model and uses ground-truth data to measure how well agents perform tasks across various dimensions.

You do not need tool-call logs to score agent factual accuracy and completeness. Tool-call logs are optional; the evaluation requires a rubric model and ground-truth data to produce per-dimension scores.

The best way to prevent agent regressions in production pipelines is to implement continuous quality assurance with evaluation pipelines that compare outputs against ground truth and enforce quality gates to trigger alerts when pass rates fall.

You should use a multi-dimensional rubric model for benchmarking when you need objective, repeatable improvements across context-engineering or multi-agent setups, requiring ground-truth data to produce per-dimension scores.