To resolve the scaling bottlenecks and runtime errors caused by monolithic system prompts, engineering teams should treat prompts as build artifacts by modularizing instructions into reusable templates. By running these modular "skill files" through a transpiler, developers can enforce static validation, catch missing dependencies at build time, and integrate prompt generation directly into their CI/CD pipelines. This deterministic approach prevents code drift and ultimately establishes a safe framework where agents can propose updates to their own logic via standard pull requests.
Google 提出模块化提示词转译方案,构建可扩展 AI 智能体
阅读原文· developers.googleblog.comGoogle 提出将系统提示词模块化为可复用的“技能文件”,通过转译器进行静态验证并在构建时捕获缺失依赖,从而解决单体提示词导致的扩展瓶颈与运行时错误。该方法将提示词生成集成到 CI/CD 流水线中,防止代码漂移,并允许智能体通过标准 pull request 自主更新逻辑。
To resolve the scaling bottlenecks and runtime errors caused by monolithic system prompts, engineering teams should treat prompts as build artifacts by modularizing instructions into reusable templates. By running these modular "skill files" through a transpiler, developers can enforce static validation, catch missing dependencies at build time, and integrate prompt generation directly into their CI/CD pipelines. This deterministic approach prevents code drift and ultimately establishes a safe framework where agents can propose updates to their own logic via standard pull requests.