Mise en Place for Agentic Coding: Deliberate Preparation as Context Engineering Methodology

• URL: http://arxiv.org/abs/2605.05400v1
• Date: Wed, 06 May 2026 19:33:08 GMT
• Title: Mise en Place for Agentic Coding: Deliberate Preparation as Context Engineering Methodology
• Abstract summary: The rapid adoption of AI coding agents has produced a dominant workflow pattern that prioritizes speed of implementation over deliberate preparation.<n>We propose a three-phase preparation methodology for agentic coding: contextual grounding, collaborative specification, and task decomposition.<n>We report on the application of MEP during a competitive hackathon, where roughly two hours of preparation enabled a rapid parallel implementation of a full-stack educational platform by concurrent AI agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid adoption of AI coding agents has produced a dominant workflow pattern -- often called "vibe coding" -- that prioritizes speed of implementation over deliberate preparation. We argue that this approach creates a systematic alignment problem: agents that lack sufficient context produce code requiring extensive debugging and refactoring, consuming substantial development time. Drawing on the culinary concept of mise en place (everything in its place; abbreviated MEP), we propose a three-phase preparation methodology for agentic coding: (1) contextual grounding, where domain expertise and tacit knowledge are externalized into structured documents; (2) collaborative specification, where human-agent dialogue produces detailed design artifacts; and (3) task decomposition, where specifications are converted into structured, dependency-aware task records. We report on the application of MEP during a competitive hackathon, where roughly two hours of preparation enabled a rapid parallel implementation of a full-stack educational platform by concurrent AI agents. We introduce the concept of context fluency as an emerging developer skill -- the ability to create rich, structured context that agents can act on -- and connect it to established frameworks in backward design and tacit knowledge externalization. We conclude with a research agenda for empirically validating preparation-phase methodologies in AI-assisted software development.

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