【文章标题】：Warp在Claude上构建自我进化智能体
【文章正文】： How Warp builds self-improving agents on Claude Warp如何在Claude平台构建自我进化智能体 Learn how Warp devised a simple development pattern that anyone can use to create self-improving agents. 了解Warp如何设计出人人都能使用的简易开发模式来创建自我进化智能体。
In our series, , we highlight how startups are transforming their industries with AI. In this article, we share how Warp turned stateless user feedback into a self-improvement loop for its agents. 在本系列文章中，我们聚焦初创企业如何用AI改造行业。本文将揭秘Warp如何将无状态用户反馈转化为智能体的自我进化循环。
Agents need to handle recurring tasks reliably and effectively. A first-pass prompt that gets 80% of the task correct can create a noisy and annoying experience for the user. Warp learned this the hard way, and used this to inform its product strategy, creating an improved experience for nearly 1M developers worldwide. 智能体需要可靠高效处理重复性任务。仅完成80%准确度的初始提示会给用户带来嘈杂糟糕的体验。Warp通过艰难教训认识到这点，并据此制定产品策略，为全球近百万开发者优化体验。
Warp, the AI-powered terminal and agentic development environment, builds on the Claude Platform. The team ran into this “noisy experience” problem with their internal代码审查智能体。工程师们抱怨该智能体总给出无用建议且输出质量低下。 基于Claude平台的AI终端与智能开发环境Warp团队在其内部代码审查智能体上遭遇了”噪音体验”问题。
The team initially tried stopgap solutions, like manually rewriting the prompt based on observed code review failures. This made output more usable but didn’t scale. Improving context files like AGENTS.md also helped, but was far from a complete fix. 团队最初尝试临时解决方案，比如根据观察到的审查失败手动重写提示。这虽提升输出可用性但难以扩展。改进AGENTS.md等上下文文件也有助益，但远非彻底修复。
Ultimately, they realized, the real issue was that feedback to an agent, no matter what its purpose, typically disappears when the session ends, removing critical context from the agentic loop. Their solution: an Agent Skills-based framework to create self-improving agents where feedback compounds over time to continually refine and enhance agent output. 最终他们发现核心问题在于：会话结束时，任何智能体的反馈都会消失，导致关键上下文脱离智能循环。解决方案是建立基于Agent Skills的框架，让反馈随时间累积不断优化智能体输出。
Read on to learn how they built it with skills on top of the Claude Platform. 下文将详解他们如何在Claude平台上用skills技术构建该系统。
The central technique is a self-improvement loop using skills, which are file based encodings of knowledge that keep instructions out of the raw prompt. Warp evolved a self-improving agent architecture consisting of two skills, with human feedback in between. 核心技术是采用skills的自我进化循环——这种基于文件的知识编码能将指令与原始提示分离。Warp开发出包含两个skills的架构，中间嵌入人类反馈环节。
The inner/base skill holds the functional domain knowledge and instructions. For example, when a PR is opened, Warp’s code agent executes using that base skill and context to produce its review. 内部/基础skill包含功能领域知识与指令。例如当PR创建时，Warp代码智能体就基于该skill和上下文生成审查意见。
Human feedback on agent output is a critical component for the self-improvement loop. For code review this could be something as simple as a thumbs up, but the more explicit the better. 人类对智能体输出的反馈是进化循环的关键要素。代码审查中可能简单如点赞，但越明确越好。
“A human could affirm, ‘this was a good, useful comment’,” Warp founder Zach Lloyd explains, “But the human could also give detailed reasons why a code review wasn’t good. Specifics like ‘you suggested renaming this variable, but our code base convention is this type of global variable uses this particular naming context’ tell the agent how to do it right next time.” Warp创始人Zach Lloyd解释：“人类既能肯定’这条建议很好’，也能详细指出问题所在。比如’你建议重命名该变量，但代码库规范要求此类全局变量需采用特定命名上下文’，这种具体反馈能指导智能体下次正确处理。”
The outer/improver skill functions as an observer agent that runs on a schedule rather than per-task. It pulls the accumulated human feedback, compares what the agent suggested against how humans responded, and proposes a small, focused edit to the base skill. 外部/改进器skill作为观察者智能体按计划运行（非逐任务）。它汇总人类反馈，对比智能体建议与人类反应，最终提出针对基础skill的精准微调方案。
Because skills are plain files, agents are extremely good at updating them. These updates, which are reviewable, approvable, and mergeable, can flow through a normal PR/code-review workflow; once merged, the next run of the inner skill inherits the improvement. 由于skills是普通文件，智能体极擅长更新它们。这些可审查、可批准、可合并的更新能走标准PR流程，合并后下次内部skill运行即继承改进。
Warp now runs this pattern across its entire open-source repo, with separate spec-writing, review, and triage agents, each carrying their own self-improvement loop. Warp现已在全开源库推行该模式，配备独立的规范撰写、审查和分类智能体，各自拥有进化循环。
“File-based skills are a way of encoding knowledge for agents without putting that knowledge directly in the prompt, as something the agent can simply look up in the course of doing its job,” says Zach. “The framework is really simple actually: there’s the base domain-specific skill and then there’s the improver skill that refines that domain-specific skill. This simplicity is the beauty of this approach.” Zach表示：“基于文件的skills是种知识编码方式，既避免知识直接写入提示，又能让智能体执行时随时查阅。框架其实非常简单：基础领域skill+改进该领域的improver skill，这种简约正是方法的美妙之处。”
Here are some of the Warp team’s tried and true tips for writing self-improving skills for agentic loops: 以下是Warp团队总结的编写智能体进化skills的实用技巧：
Warp’s issue triage agent demonstrates the self-improving agent skills framework. The pattern is triggered whenever someone files a new GitHub issue: a GitHub Action fires an agent that analyzes the issue for complexity and feasibility, assigns labels, and suggests a direction for the fix. That triage agent runs off an inner skill file holding the domain knowledge about what each label means and how to research the codebase before acting. Warp的问题分类智能体展示了该框架。当GitHub新增issue时触发流程：GitHub Action启动智能体分析问题复杂度/可行性→分配标签→建议修复方向。该分类智能体依托内部skill文件运作，其中包含标签含义领域知识及行动前的代码库研究指南。
On a sample issue, the first-stage inner skill did a solid job but missed one label, ready to spec, which signals that a contributor can start building product and technical specs against the issue. A maintainer on the Warp team caught the gap and left feedback directly on the issue, exactly where the work was happening. Critically, he explained both what he expected and why he expected it: actionable feedback easy for the agent to absorb later. 某测试案例中，首阶段内部skill表现良好但漏标”ready to spec”标签（表示贡献者可开始制定产品技术规范）。Warp维护者发现缺失后直接在issue处留下反馈，关键是他同时说明了期望内容及原因，这种可操作反馈便于智能体后续吸收。
The outer improver skill runs in Oz, Warp’s agent orchestration platform, as a scheduled “update triage” agent. The agent authenticated to GitHub, ran a Python script bundled with the skill to… 外部改进器skill在Warp智能体编排平台Oz上作为定时”更新分类”智能体运行。该智能体通过GitHub认证后，执行skill内置的Python脚本…