agent-evaluationlisted

# Agent Evaluation Decide whether a stochastic candidate improves observable outcomes under comparable conditions. [prompt-design](../prompt-design/SKILL.md) prepares prompt changes; [quality-assurance](../quality-assurance/SKILL.md) owns deterministic software proof. Keep datasets and execution commands in the project or provider's existing evaluation workflow. ## Workflow 1. **Declare the decision**: identify the behavior, baseline, candidate, success criteria, regressions that block adoption, trial budget, and stopping rule in an [evaluation brief](references/evaluation-brief.md). Scale rigor to the decision; a small development probe supports iteration, not broad reliability claims. 1. **Freeze identity**: record code, prompt, tools, retrieval snapshot, model/version, runtime settings, retries, and grader versions. Change one factor when attributing an improvement to it; label unpinned provider behavior as a reproducibility limit. 1. **Choose representative cases**: include ordinary successes, known failures, hard negatives, tool errors, and relevant trust boundaries. Keep development cases separate from held-out decision cases; do not tune on the latter and still call them unseen. 1. **Grade outcomes first**: use executable tests, schema checks, state inspection, and attempted tool actions where possible. For semantic grading, calibrate against labeled examples, blind candidate identity and vary presentation order; use independent human judgment for consequential disa