agent_evaluation_benchmarking

AI agent performance is evaluated using multi-dimensional metrics that assess correctness, reliability, efficiency, and safety. This Skill provides frameworks to measure these dimensions against established public benchmarks and custom evaluation tasks.

Benchmarking an AI agent against SWE-bench is achieved by running this evaluation framework, which integrates established public benchmarks for software engineering tasks. It provides continuous assessment tools to compare agent outputs against baseline datasets.

Regression testing for AI agent evaluation is implemented through continuous evaluation and baseline comparison. This framework monitors agent quality over time by running custom evaluation pipelines and golden datasets to detect performance regressions.

Custom evaluation datasets can be used to assess agent quality in specific domains by designing custom evaluation tasks. The framework supports golden datasets and tailored evaluation pipelines to ensure accurate domain-specific measurement.

Human evaluation for AI agent benchmarking incorporates rubric-based scoring and inter-rater agreement to provide qualitative assessment. This approach complements automated metrics by capturing subjective dimensions of agent quality and reliability.