evaluation

You evaluate agent performance by applying multi-dimensional rubrics with weighted scoring to test outputs across configurations and sessions. This enables reliable measurement of factual accuracy, completeness, and tool efficiency for autonomous systems.

Quality gates for autonomous agent systems are benchmarking workflows that validate context engineering decisions. They use regression tracking and automated evaluators to ensure agent performance meets factual accuracy and completeness standards before deployment.

Yes, you can combine automated evaluators with human judgments using human-in-the-loop evaluation workflows. This approach supports continuous improvement across production pipelines by validating context strategies and tracking regressions over time.

To set up weighted scoring rubrics for testing agent outputs, define evaluation dimensions like factual accuracy, citation quality, and source credibility. Run the built-in rubric on sample outputs to generate per-dimension scores for benchmarking.

Yes, the evaluation tool works without external dependencies. It operates as a standalone Skill with scripts and references, allowing you to run built-in rubrics on sample agent outputs immediately to generate dimension scores.

You should use multi-dimensional rubrics for agent testing when you need to quantify performance across complex dimensions like tool efficiency and source credibility. It is essential for validating context engineering decisions and tracking regressions over time.