agent-eval

You can benchmark coding agents by defining tasks in YAML files, running each agent in isolated git worktrees, and using a judge configuration to compare outputs against a baseline. This yields pass rate, cost, time, and consistency metrics for robust comparison.

Comparing coding agents with this approach yields pass rate, cost, time, and consistency metrics. These objective, data-driven results guide tool selection and workflow decisions across projects, teams, or CI pipelines.

To evaluate coding agents without cross-task contamination, use per-run git worktrees. Git worktree isolation ensures each agent run starts from a clean baseline, preventing previous task outputs from affecting the next evaluation.

Yes, you need YAML task definitions to declare tasks, targets, and judging criteria. Along with a judge configuration, these YAML files establish the deterministic, reproducible tasks required to generate decision-ready comparative reports.

The best way to select coding agents for CI pipelines is running deterministic tasks defined in YAML and collecting consistency metrics. This reproducible evaluation process produces objective, decision-ready reports for tool adoption.