Agent Evaluation

**Verdict:** A strong, thoughtfully designed skill for evaluating LLM agents in realistic conditions, especially where traditional benchmark scores have failed to predict production behavior. **What it does well** - Treats LLM agents as stochastic systems, emphasizing **multi-run statistical testing** over brittle single-shot checks. - Encourages **behavioral contracts** and regression suites to keep agents stable across updates. - Incorporates **adversarial testing** and reliability metrics, which are critical for production environments. - Explicitly warns against common **anti-patterns** (string matching, happy-path-only tests, single-run evaluations) and addresses issues like metric gaming and data leakage. **Risks & limitations** - Evaluation quality depends heavily on how well you define behavioral invariants and test data; poor test design can still give a false sense of security. - Multi-run and adversarial testing can be **compute- and cost-intensive**, especially for complex agents. - Does not, by itself, guarantee safety or compliance; it must be paired with domain-specific policies and red-team testing. **Best-fit scenarios** - Teams moving agents from **prototype to production**, needing rigorous, repeated evaluation. - Organizations burned by agents that **passed benchmarks but failed in real-world workflows**. - Setups with **multi-agent orchestration or autonomous agents**, where emergent behavior and regressions are hard to spot without structured evaluation. **Overall:** Well-suited as a backbone evaluation skill for production-grade agents. Use it to design, run, and interpret robust agent tests, but expect to invest in good test design and complementary safety reviews.