Interpretable Context Methodology: Folder Structure as Agentic Architecture

• URL: http://arxiv.org/abs/2603.16021v2
• Date: Wed, 18 Mar 2026 11:30:46 GMT
• Title: Interpretable Context Methodology: Folder Structure as Agentic Architecture
• Abstract summary: Current approaches to AI agent orchestration typically involve building multi-agent frameworks that manage context passing, memory, error handling, and step coordination through code.<n>This paper presents Model Workspace Protocol (MWP), a method that replaces framework-level orchestration with structure.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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