agent-eval

Coding agent benchmarking uses git worktree isolation to execute identical tasks across multiple agents, collecting consistent performance data like pass rate and completion time to eliminate subjective comparisons.

When comparing LLM coding agents, you should track pass rate, API cost, wall-clock completion time, and consistency across repeated trial runs to ensure evidence-based, data-driven tooling decisions for your engineering team.

Yes, you can run regression checks on coding agent behavior after model or tooling updates by applying systematic, metrics-driven benchmarking to evaluate if the new model update improves performance on your actual codebase before rolling it out widely.

Evaluate coding agent performance on your actual codebase by defining custom coding tasks in YAML and running a head-to-head comparison of configured agents, capturing reproducible multi-metric data for analysis.

Yes, coding agent benchmarking supports both deterministic and LLM-based evaluation through flexible judge configuration, allowing systematic multi-metric data collection and consistency assessment across repeated trial runs.