How Warp builds self-improving agents on Claude

Summary

Warp addressed the limitations of stateless agent interactions by building a self-improving architecture on the Claude Platform. Initial attempts to refine prompts or context files such as AGENTS.md proved insufficient for scaling, as user feedback on outputs like code reviews typically vanished at the end of each session. The resulting architecture uses two file-based skills to turn that feedback into persistent improvements.

An inner base skill encodes domain-specific instructions and knowledge for the agent’s primary task, such as triaging GitHub issues or reviewing pull requests. An outer improver skill runs on a schedule, retrieves accumulated human feedback, compares agent actions against the responses, and generates a minimal edit to the base skill file. Because the skills reside in ordinary files, the proposed changes follow standard pull-request workflows, where engineers can review, approve, and merge them before the updated instructions take effect on the next run.

The approach is illustrated by Warp’s issue-triage agent. When a new GitHub issue arrives, a GitHub Action triggers the base skill to assign labels and assess feasibility. Maintainers can add explicit comments on the issue itself; the improver skill later extracts these signals, summarizes them, and opens a targeted pull request that refines the labeling logic. Once merged, the base skill inherits the correction without requiring manual prompt rewriting.

Warp applies the same pattern to spec-writing and code-review agents across its open-source repository. The framework keeps human oversight in the loop while allowing feedback to compound, converting one-off agent sessions into continuously improving systems.

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