agent-eval

To benchmark coding agents objectively, you run head-to-head comparisons using reproducible, data-backed metrics like pass rate, API cost, and completion time. This replaces subjective tool selection with quantitative analysis across multiple agents.

Reproducible testing for AI coding tools is achieved through git worktree isolation and commit pinning. This ensures that benchmark runs are not impacted by external changes and maintain cross-run consistency.

Yes, you can use declarative YAML task definitions to evaluate coding agents. This allows you to define custom tasks systematically and apply them across different agents for consistent benchmarking.

Automated agent benchmarking supports multiple judge types including pytest, grep, and LLM-as-judge. These judges evaluate whether the coding agents successfully completed the defined tasks.

When evaluating coding agents for team adoption, you should track pass rate, API cost, wall-clock completion time, and consistency across repeated runs. These metrics provide objective data for tooling decisions.

You can compare Claude Code and Aider by running the same set of custom refactoring or bug fix tasks across both agents. The benchmarking tool tracks pass rate, cost, time, and consistency to identify the better performer for your workflow.