agent-eval

To benchmark coding agents, you run cross-agent evaluations using YAML task definitions and deterministic judges like tests or builds, generating comparison data for pass rate, cost, time, and consistency across runs.

Comparing coding agent performance consistently requires per-run git worktrees for environment isolation and deterministic judges such as tests or builds to validate task completion and produce robust comparison metrics.

Head-to-head coding agent comparison computes pass rate, cost, time, and consistency across runs. These metrics help identify the best-performing tool for a team by evaluating deterministic task outcomes.

Yes, you can use git worktrees to isolate coding agent evaluation tasks. Per-run worktrees ensure that each agent operates in a clean, independent environment to generate reproducible comparison data.

Validating model updates requires running cross-agent evaluations on custom tasks. By applying deterministic judges and tracking pass rate or cost metrics, you can benchmark new agents before adoption.

Yes, YAML task definitions are required to run agent-eval benchmarks. These definitions specify the reproducible tasks that coding agents must complete, allowing deterministic judges to evaluate performance.