What Goes Into a Self-Improvement Loop for a Software Factory?

A software factory’s self-improvement loop evaluates completed agent runs using rubric-based scorers, analyzes recurring successes and failures through observer and self-improvement agents, and converts findings into reviewable code changes to the factory’s models, skills, and context. The approach distinguishes internal factory metrics, such as run count, cost, and human intervention, from DORA metrics that measure outward software delivery performance. Effective loops require captured run traces, selective or scheduled scoring to balance cost and coverage, pattern analysis across batches of results, and version-controlled factory definitions where proposed fixes can be merged as diffs. The comparison argues that infrastructure-oriented platforms such as Warp Factories allow teams to define and inspect their own rubrics and improvement mechanisms, whereas hosted products, point tools, and individual coding agents offer varying degrees of control or persistence. Common shortcomings include using scoring only for monitoring, scoring every run unnecessarily, and keeping agent configurations outside version control, while evidence of a functioning loop includes measurable automation gains, actionable pull requests, early detection of specific failures, and deliberately expanded evaluation coverage.

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