agent-evaluation

LLM agent evaluation applies behavioral testing frameworks and statistical analysis to measure stability across repeated runs, detect behavioral inconsistencies, and validate contracts. This skill designs capability assessments to identify reliability issues and production readiness gaps beyond simple benchmarks.

Testing AI agents for prompt injection requires adversarial validation to find edge-case failures, metric gaming, and behavioral risks. This skill designs adversarial test cases that expose gaps between benchmark performance and real-world usage to ensure production readiness.

Regression testing for LLM agents involves designing capability assessments and statistical evaluations to detect performance degradation. This skill creates regression tests that monitor agent stability and identify behavioral failures across updated versions.

Benchmarking LLM agents to measure capability and behavioral quality requires statistical evaluation methodologies and reliability metrics. This skill designs comprehensive capability assessments that quantify agent performance and validate behavioral contracts.

AI agents passing benchmarks but failing in production often suffer from metric gaming, unhandled edge cases, and behavioral inconsistencies. This skill identifies these gaps through adversarial validation and behavioral testing to bridge benchmark performance and real-world reliability.