Why You Should Split Planning and Coding Between Separate AI Agents
Split Your AI Agents: One Plans, One Codes
The instinct with AI coding tools is to throw everything at a single agent - describe what you want, let it plan and build in one shot. But a growing number of developers are discovering that splitting the planning and implementation into separate agents produces dramatically better results.
The Split-Role Approach
The idea is simple. One agent acts as the architect - it analyzes requirements, asks clarifying questions, identifies edge cases, and writes a detailed spec. A second agent takes that spec and implements it. The planning agent never writes production code. The coding agent never makes architectural decisions.
This separation works because LLMs are better at focused tasks. When an agent is both planning and coding, it tends to start implementing before fully thinking through the design. It commits to an approach in the first few lines and then works backward to justify it.
Why It Catches More Bugs
The planning agent can play adversary. Before any code is written, it can identify potential issues - race conditions, missing error handling, edge cases in input validation. When a separate agent implements the spec, those requirements are already documented and harder to skip.
Think of it like code review happening before the code exists. The spec becomes a contract that the implementation agent must fulfill, and you can verify the output against it.
Making It Practical
You don't need fancy tooling. Have your planning agent output a structured markdown spec. Feed that spec as context to your implementation agent. The overhead is minimal - maybe an extra 30 seconds per task - but the quality improvement is noticeable, especially for anything beyond trivial changes.
The key insight is that AI agents work best with constrained scope. Give them a clear, bounded job and they perform significantly better than when they're juggling multiple concerns.
Fazm is an open source macOS AI agent. Open source on GitHub.