Using LLM-as-a-judge scoring to measure your software factory

Organizations can assess coding-agent performance through conventional DORA metrics, such as merge rates, cycle time, defects, and repair time, alongside direct LLM-as-a-judge scoring of recorded agent sessions. Effective scoring requires comprehensive, API-accessible traces containing prompts, tool activity, outputs, and artifacts; specialized scoring agents that evaluate dimensions such as task completion, efficiency, verbosity, code quality, or organization-specific practices; and a cost-conscious sampling strategy rather than reviewing every run. Scores can reveal regressions, identify recurring failures such as unnecessary test creation, and help teams refine agent instructions, skills, models, and context. The approach can also support automated self-improvement loops, in which observer agents analyze scored runs and propose updates, as well as benchmarking of alternative model configurations for cost and performance. Warp Factories is presented as infrastructure that stores traces, runs scorers, visualizes results, and automates these feedback workflows.

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