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Anthropic recently shared on its blog Warp’s approach to letting an Agent evolve itself.
The core idea is simple: two layers of Agent Skills plus a loop of human feedback form a closed system that lets the Agent keep correcting itself as it is used.
This mechanism now runs across Warp’s entire open-source repository. Agents that write spec docs, review code, and triage issues each carry their own improvement loop.
The overall approach is quite similar to “I Now Completely Stop Reading AI-Written Code.” That article can be used as a contrast and as a practical demo.
Why self-evolution?
It started when Warp’s internal code-review Agent went wrong. Engineers complained that its comments were useless and the quality was uneven.
The team’s first instinct was to hand-edit prompts and add more context to AGENTS.md. That can work in the short term, but it does not scale for long-running, large-context work.
Because human feedback to the Agent disappears as soon as the session ends.
The same mistake may have to be taught again every week. For the Agent to actually get better, feedback has to persist so the same errors stop repeating.
