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

TL;DR

This paper introduces a three-phase preparation methodology inspired by culinary mise en place to improve AI coding agents' effectiveness by externalizing context, detailed specifications, and task decomposition, demonstrated through a hackathon case study.

Contribution

It proposes a novel structured preparation approach for AI coding agents, emphasizing context externalization and collaboration, to address the alignment problem in rapid AI-assisted development.

Findings

Two hours of preparation enabled rapid full-stack development by AI agents.

Structured context externalization improves agent performance and reduces debugging.

Introduces the concept of context fluency as a key developer skill.

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.