agent-evaluation

Benchmarking LLM agent reliability requires running behavioral tests and statistical evaluations across multiple iterations to analyze output distributions and identify failure modes. This skill automates that process by applying adversarial testing methodologies to ensure robust deployment.

Behavioral contract testing for LLM agents is the process of defining and verifying invariant behaviors to ensure agents act consistently under varying conditions. This skill implements it to validate that agents maintain expected responses throughout configuration changes.

To run an adversarial test suite against your LLM agent, use this skill to systematically stress-test agent capabilities and identify edge cases. It applies targeted adversarial methodologies to expose potential production-level failure modes.

High LLM benchmark scores often fail in production because single-run success does not reflect real-world consistency. This skill bridges that gap by using statistical reliability metrics across multiple iterations to analyze true output distributions.

Yes, you can use statistical reliability metrics for LLM agent regression testing by running behavioral test suites across multiple iterations. This skill analyzes output distributions rather than relying on single-run success to verify configuration changes.

No specific testing framework dependency is required to evaluate LLM agent capabilities. This skill operates independently to perform capability assessments and generate reliability metrics using statistical and adversarial testing patterns.