AI engineering should accelerate the engineer's learning, with the human owning architecture and the agent implementing well-specified decisions.
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A planning process turns a vague idea into atomic features, detailed specifications, validated dependencies, and a phased implementation plan.
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Each feature should be implemented in one focused session, tested through visual, auditory, and tactile feedback, then committed as a checkpoint.
Summary
Corey J. Gallon argues that vibe coding creates brittle demoware because it skips planning and leaves developers unable to understand or change what the agent produced. His framework has three pillars: principles, process, and tools. The principles keep the human responsible for architecture, specifications, and tradeoffs while the agent handles implementation. The planning process moves from vision to feature inventory, atomic specifications, dependency analysis, and an implementation plan. During implementation, the agent receives a curated context package for one feature, writes the code, runs it, gathers visual, auditory, and tactile feedback, and refines until tests and observed behavior are clean. Gallon also describes the supporting environment: an AI coding agent, sandbox, editor, voice input, sensory validation tools, document templates, cross-references, slash commands, Markdown, Git, and plan-based progress tracking. The approach favors deliberate iteration and working software before later investment in correctness or speed.
AI engineering should make the engineer learn, not become dependent on the agent
Gallon says treating coding agents as pure productivity tools can leave engineers no better after six months, or dependent on AI for debugging, modifications, and architectural decisions. His framework treats every step as a learning opportunity. The goal is to ship software while also building the engineer's understanding. He summarizes this with "always be learning." The claim changes how progress is measured: generated code is useful, but the engineer's growing ability to understand and direct the system matters more than raw output speed.
The human owns architecture while the agent handles implementation
The framework keeps a clear boundary between architect and implementer. The developer owns architecture, interfaces, system intent, structure, design decisions, and tradeoffs. The agent handles typing code, following established patterns, writing boilerplate, and implementing tests that have been specified. Gallon describes this as delegating "the doing and not the thinking." Agents can execute a decision once it is clearly defined, but they should not replace the architectural work that determines what the system is supposed to be.
Deliberate iteration prevents the repeated starting-over cycle
Gallon says vibe coding often creates several abandoned attempts because developers do not deliberately validate and improve one system. A slower first week can create momentum in the second week, followed by much faster work in the third. The framework therefore asks developers to iterate on validated work instead of repeatedly restarting. This compounds both understanding and productivity. The intended rhythm is summarized as "compound progress, accelerate velocity," with each completed feature becoming a foundation for the next one.
Specifications give agents a blueprint instead of a conversational guess
The framework treats specification as more important than prompt engineering. Prompt engineering searches for wording that might produce the desired result, while a specification defines what "right" means. Gallon describes specifications as structured definitions of requirements, behavior, interfaces, and acceptance criteria. Writing one forces the developer to examine the problem, define interfaces, and anticipate edge cases. The agent then implements an explicit target rather than interpreting an informal conversation. His shorthand is "write the blueprint, not the prompt."
Planning turns a vague project into atomic, dependency-ordered work
The planning phase is the developer's architectural work, with the agent optionally acting as a thinking partner. It proceeds through vision, features, specification, dependencies, and plan. Vision capture defines the problem, users, essential workflows, scope boundaries, technical context, and workflow details. Feature identification extracts and categorizes every unit of functionality. Each feature is refined into a user story, technical contracts, validation contracts, and explicit dependencies. Dependency analysis produces a matrix and graph, then the implementation plan groups features into phases with validation gates.
A feature is ready only when its specification is atomic and its completion criteria are defined
Gallon asks developers to define tests and observable success criteria before implementation. This gives the agent a clear stopping point and immediate feedback for self-correction. A feature should be small enough to implement in one focused session. If the specification describes several capabilities or feels scattered, it should be split and specified again. The final feature specification contains the user story, a three-level technical blueprint, a three-level validation strategy, dependencies, and implementation notes. The rule is "reduce until irreducible."
Context must be assembled from persistent artifacts for each implementation session
The implementation session should receive a focused context package instead of every planning document. That package contains the complete feature specification, the specifications and implemented code for referenced dependencies, relevant sections of the implementation plan, and instructions for the sensory tools required by the acceptance criteria. Cross-references identify those dependencies. Gallon says architectural decisions should persist in specifications, plans, and design documents rather than relying on conversation history or memory. His phrase is "curate context, don't accumulate it."
Agents should implement one feature through a multi-sensory feedback loop
For each atomic feature, the agent writes code, executes it, gathers sensory feedback, runs the specified tests, correlates the results, and refines the implementation. Visual feedback checks rendering, layout, state, and code structure. Auditory feedback covers logs, errors, warnings, API responses, and stack traces. Tactile feedback exercises workflows, API interactions, performance, security, and integration behavior. The feature is complete only when all tests pass and the sensors report clean execution. The agent then creates an atomic Git commit containing only that feature's changes.
You have an AI-generated prototype that works today, but you do not understand enough of it to change or maintain it next week.
Your coding-agent sessions keep expanding into back-and-forth debugging because the agent lacks a precise specification, useful dependencies, or clear completion tests.
You want to build production software with agents while keeping architectural ownership and learning from each implementation.
