Warp 如何在 Claude 上构建自我改进的智能体
Learn how Warp devised a simple development pattern that anyone can use to create self-improving agents.
了解 Warp 如何设计了一种简单的开发模式，任何人都可以使用它来创建自我改进的 agent。
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.
在我们的系列文章中，我们重点介绍初创公司如何利用 AI 改变其行业。在本文中，我们分享 Warp 如何将其无状态的用户反馈转化为其智能体的自我改进循环。
| The quick pitch | |
|---|---|
| Name | Warp |
| Founded | 2020 |
| Founders | Zach Lloyd (CEO) |
| Stack | Rust, Golang, GitHub Actions, internal agent orchestration platform (Oz), Claude Platform |
| Growth | $73M raised. 800K monthly developers build on Warp. 56% of the Fortune 500 uses Warp. 10M Claude Code sessions run inside Warp to date, 400K+ per week. 40M total Warp Agent conversations. |
| 快速推介 | |
|---|---|
| 名称 | Warp |
| 成立年份 | 2020 |
| 创始人 | Zach Lloyd (CEO) |
| 技术栈 | Rust, Golang, GitHub Actions, 内部 agent 编排平台 (Oz), Claude Platform |
| 增长 | 融资 7300 万美元。每月 80 万开发者在 Warp 上进行构建。56% 的 Fortune 500 使用 Warp。迄今为止，Warp 内部运行了 1000 万次 Claude Code 会话，每周超过 40 万次。Warp Agent 对话总计 4000 万次。 |
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.
智能体需要可靠且有效地处理重复性任务。一个能完成 80% 任务的初次提示词可能会给用户带来充满噪音且令人烦恼的体验。Warp 深刻吸取了这一教训，并以此指导其产品战略，为全球近 100 万开发者创造了更优的体验。
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 code review agent. Engineers complained that their agent made unhelpful comments and produced low-quality output.
Warp 是一款由 AI 驱动的终端和智能体开发环境，构建于 Claude Platform 之上。该团队在其内部代码审查智能体中遇到了这种“充满噪音的体验”问题。工程师们抱怨他们的智能体提出了无用的评论，并产生了低质量的输出。
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 als...