🤖 AI Summary
This work proposes a lightweight, file system–based alternative to conventional multi-agent frameworks, which often incur redundant engineering overhead in sequential human-in-the-loop workflows. The approach represents workflow stages as numbered directories, stores prompts and contextual information in Markdown files, delegates non-AI tasks to local scripts, and employs a single agent to perform complex reasoning sequentially. Drawing inspiration from Unix pipelines, modular decomposition, multi-pass compilation, and literate programming, the architecture achieves high interpretability, readability, and maintainability. Released under the MIT license, this open-source solution substantially reduces the implementation complexity of sequential workflows while effectively replicating the core functionalities of multi-agent systems.
📝 Abstract
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.