agent-evaluation

Design agent benchmarks using Statistical Test Evaluation, Behavioral Contract Testing, and Adversarial Testing patterns. These frameworks validate agent behavior and reliability across capabilities, preventing single-run or happy-path testing gaps.

Behavioral contract testing is an evaluation pattern for LLM-powered systems that validates agent behavior against defined expectations. It ensures resilience by testing reliability metrics across scenarios rather than relying on output string matching.

Set up an AI agent evaluation plan by explicitly specifying metrics, datasets, and test scenarios. This outlines a comprehensive benchmarking strategy to assess agent capabilities and collect performance results.

Yes, evaluating LLM agent performance requires testing fundamentals and LLM fundamentals. These prerequisites ensure robust evaluation by supporting rigorous metric definitions and test dataset management.

Single-run testing fails to catch AI agent regressions because it is an anti-pattern that evaluates only happy-path outputs. Comprehensive benchmarking requires statistical test evaluation across multiple datasets to ensure reliability.

Output string matching is an anti-pattern limitation in agent evaluation because it misses behavioral nuances. Validating AI agents requires statistical test evaluation and adversarial testing to accurately measure reliability metrics.