Original sourceCocoloop
Summary
Warp shared engineering notes on its AI agent system that self-improves skill rules via a human feedback loop without updating model weights. The architecture includes base skill files, human feedback, and an outer 'improver' skill that periodically edits base skills and commits changes to Git. Ini…
Key points
- Developers and teams can adopt this auditable, low-cost approach to continuously improve AI agent skills without expensive model fine-tuning.
- Warp demonstrates a real-world case of AI agents self-improving with transparent, traceable file changes.
- Enterprises can build dynamic skill manuals for iterative AI agent refinement, improving task accuracy while maintaining audit compliance.
Editorial note
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