evaluation

To build an evaluation framework for non-deterministic agent systems, use rubric-based scoring and test set management to measure output quality across multiple dimensions without assuming a single correct path. This approach catches regressions and compares versions effectively.

The best way to validate context engineering choices is to run benchmark test sets before and after configuration changes. This measures agent performance repeatable, verifying whether quality improved or regressed.

Monitor production agent behavior by sampling live interactions and tracking pass rates or average scores over time. This production monitoring logic raises alerts when agent quality drops below acceptable thresholds.

Agent testing does not require a single correct output path. Repeateable assessment uses sampling logic and multi-dimensional rubrics to evaluate non-deterministic results where simple pass-fail checks are insufficient.

You can compare agent configurations by executing test suites across simple, medium, complex, and very complex scenarios. Weighted rubrics measure output quality consistently, allowing structured comparison of different versions.