Current approaches to AI agent orchestration typically involve building multi-agent frameworks that manage context passing, memory, error handling, and step coordination through code. These frameworks work well for complex, concurrent systems. But for sequential workflows where a human reviews output at each step, they introduce engineering overhead that the problem does not require. This paper presents Model Workspace Protocol (MWP), a method that replaces framework-level orchestration with filesystem structure. Numbered folders represent stages. Plain markdown files carry the prompts and context that tell a single AI agent what role to play at each step. Local scripts handle the mechanical work that does not need AI at all. The result is a system where one agent, reading the right files at the right moment, does the work that would otherwise require a multi-agent framework. This approach applies ideas from Unix pipeline design, modular decomposition, multi-pass compilation, and literate programming to the specific problem of structuring context for AI agents. The protocol is open source under the MIT license.

翻译：当前AI智能体编排的主流方法通常涉及构建多智能体框架，通过代码管理上下文传递、记忆、错误处理和步骤协调。这些框架适用于复杂的并发系统，但对于需要人工在每一步审核输出的顺序工作流而言，它们引入了问题本身不需要的工程开销。本文提出模型工作空间协议（MWP），一种用文件系统结构替代框架级编排的方法。编号文件夹代表阶段，纯Markdown文件承载提示和上下文，指示单个AI智能体在每一步扮演的角色。本地脚本处理完全无需AI的机械性工作。最终形成一个系统：一个智能体在正确时刻读取正确文件，完成原本需要多智能体框架的工作。该方法将Unix管道设计、模块分解、多遍编译和文学编程的思想应用于为AI智能体构建上下文的特定问题。该协议以MIT许可证开源发布。