Warp's Self-Improving AI Agents Learn From Feedback

Warp, the AI-powered terminal used by 800K monthly developers, solved a core agent reliability problem by building a self-improvement loop using file-based skills on top of Claude. Instead of losing user feedback when sessions end, their system captures it and uses an observer agent to refine the base skill over time, creating compounding improvements. The framework uses two skills: a base skill holding domain knowledge and an improver skill that periodically updates it based on human feedback. Tips from the Warp team include writing principles over rigid rules, explaining the reasoning behind instructions, and making feedback effortless to capture where developers already work.