Breaking Down Complex Projects for AI Coding Agents
Breaking Down Complex Projects for AI Coding Agents
You write a detailed PRD. Fifteen pages of requirements, user stories, edge cases, architecture decisions. You paste it into Claude Code and say "build this." Twenty minutes later you have a project that looks impressive at first glance and falls apart the moment you test it.
Handing an AI agent a full PRD never works. Here is what works instead.
The Right Unit of Work
An AI coding agent works best with tasks that take 15-45 minutes and have a single clear deliverable. Not "build the authentication system" but "create the login endpoint that accepts email and password, validates against the users table, and returns a JWT."
Each task should have explicit acceptance criteria. The agent needs to know what "done" looks like - not in abstract terms but in concrete, testable outcomes. "The endpoint returns a 200 with a valid JWT when credentials match, and a 401 with an error message when they don't."
The Decomposition Process
Start with your full spec. Break it into independent modules. Break each module into features. Break each feature into implementation tasks. Each task should be completable without requiring the others to exist first.
When dependencies are unavoidable, stub them. "Assume the database returns a user object with these fields" lets the agent build the logic without needing the actual database layer. You wire things together after each piece works independently.
Context Is More Important Than Instructions
A detailed prompt is less useful than a well-structured codebase. If your project has clear file organization, consistent naming, and existing patterns the agent can follow, it will produce better code with a one-sentence prompt than it would from a five-page spec in a messy codebase.
Put your conventions in a CLAUDE.md file at the project root. Architecture decisions, coding standards, naming patterns - these persist across sessions and keep every agent instance aligned.
Review Each Piece Before Moving On
Do not batch agent outputs and review later. Check each task as it completes. Fix issues before they compound. An error in the data layer becomes five errors in the business logic if you do not catch it early.
The goal is not to make the agent do everything. The goal is to make the agent do well-scoped work reliably.
Fazm is an open source macOS AI agent. Open source on GitHub.