用文件夹层级替代复杂框架，让单个AI按步骤完成任务。
Interpretable Context Methodology: Folder Structure as Agentic Architecture
- 用编号文件夹表示流程阶段，Markdown文件定义每步提示与上下文。
- 单个AI agent通过读取文件实现全流程执行，无需多智能体框架。
- 适合需要人工审查每步输出的顺序任务，降低工程复杂度。
当前AI智能体编排方法通常依赖多智能体框架来管理上下文传递、记忆、错误处理和步骤协调，适用于复杂的并发系统。但对于需要人工逐步审查输出的顺序工作流，这类框架引入了不必要的工程开销。本文提出模型工作区协议（Model Workspace Protocol, MWP），将框架级编排替换为文件系统结构：编号文件夹代表流程阶段，普通Markdown文件携带提示与上下文，告知单一AI智能体在每一步扮演的角色；本地脚本负责无需AI参与的机械操作。最终实现一个由单一智能体在恰当时机读取对应文件即可完成全链路任务的系统。该方法融合了Unix管道设计、模块化分解、多遍编译与文献编程思想，专为结构化AI代理上下文而设计。协议开源，采用MIT许可。
原文摘要 · Abstract (English)
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.
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