Towards self-improving software factories

Warp Factories introduces a self-improvement feature intended to help coding-agent workflows adapt to team-specific standards while reducing common problems such as inefficiency, excessive output, and failure to follow guidance. Teams configure file-based scorers that evaluate selected agents against defined outcome labels, numerical scores, pass thresholds, and sampling rates; Warp includes default scorers for code quality, efficiency, and procedural compliance. Sampled agent runs are assessed using their complete conversations and tool-call histories, with results and reasoning displayed in the Factories interface. Scheduled automations then collect scorer failures and send them to a self-improvement agent, which investigates root causes and proposes reviewable changes to factory skills and configuration as branches or pull requests. Warp reports that its internal use of the system has produced more than ten improvement PRs addressing issues including costly visual-verification loops, unsuitable code abstractions, unnecessary orchestration messages, broken skill references, status reporting, and improper task routing between agents.

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