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Yvan Guekeng Tindo
Yvan Guekeng Tindo

Posted on • Originally published at gtindo.dev

The Architect-Executioner Model - Using AI as a Precision Tool, Not a Replacement for Thinking

#ai #productivity #programming #webdev

The Problem: Confusing Execution with Thought

Developers often make the mistake of treating AI coding agents as partners in thinking rather than as precision execution tools. Giving a model a vague goal like:

"Implement the entire new user dashboard"
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asks it to handle complexity, context, and architecture—tasks it struggles with due to context drift and hallucination.

The philosophy:

  • You, the developer, are the Architect. You define the architecture, manage complexity, and write the blueprint.
  • AI is the Executioner. It performs repetitive, mechanical changes with precision.

The goal: a structured workflow that maximizes both human and AI strengths without replacing thought.

Step 1: Architect’s Choice – When to Code Manually

Before firing up an agent, decide if the task needs human reasoning or AI execution.

Scenario Who Handles It Why
High Conceptual Load Human Complex algorithms, new service interfaces, tricky concurrency bugs. Human reasoning is critical.
Simple, Isolated Change Human Typos, variable renames, tiny style adjustments—writing the prompt may take longer than coding.
Mechanical, Repetitive Change AI Generating boilerplate CRUD, migrating components, adding multiple similar unit tests.
High Context, Low Complexity AI Tedious changes affecting many files, like renaming props or updating patterns across the app.

Rule of thumb: If the task is cognitively heavy, do it yourself. If it's repetitive and mechanical, hand it to the AI.

Step 2: AI-Driven Prompt Engineering (Planning Phase)

Use an interactive AI (Copilot Chat, ChatGPT, etc.) not to write code, but to validate your blueprint.

Ask the AI questions like:

"What context or instruction is missing for this task?"
"Which conventions or dependencies are unclear?"
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This ensures the blueprint is complete. The output: a single, precise prompt ready for execution.

Step 3: Context Constraint – The 10-File Rule

Never let a single AI task touch more than 10 files. This enforces:

  • Context control: The LLM can “see” all relevant files, minimizing errors.
  • Faster review: Small, focused MRs are easier to validate and merge quickly.

Step 4: The Precision Mandate – Turning AI Into a Diff Machine

Instead of vague instructions:

"Update the user profile page to support theme toggling."
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Give line-level, file-specific instructions:

In src/context/themeContext.js, add setThemeMode.
In src/components/Navbar.jsx, import setThemeMode and call it on button click.
In src/styles/global.css, add a new CSS variable for dark mode color.
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Why this works:

The AI now executes exactly what you’ve defined, respecting naming conventions, helper methods, and architectural patterns. Accuracy skyrockets (90%+ on the first pass).

Step 5: Scaling Complexity – Breaking Down Large Features

For tasks that exceed 10 files, split them into atomic MRs, each with its own blueprint:

  1. MR 1: Data & Service Layer (Ex: 6 files) – Define schema, service methods.
  2. MR 2: State Management & Hooks (Ex: 8 files) – Implement global state, fetch data.
  3. MR 3: UI Components (Ex: 9 files) – Build UI using the new hooks and service layer.

Conclusion: The Modern Developer’s Role

AI is not replacing developers—it’s elevating them. By controlling context, providing precise instructions, and reserving complex thinking for humans:

  • We speed up execution.
  • Reduce cognitive load.
  • Maintain architectural integrity.

The Architect-Executioner model: you design, AI executes. Clean, fast, and precise.

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