Multi-Agent Code Review Loops - The Simple Pattern That Works
Multi-Agent Code Review Loops
I have been running parallel AI coding agents for months. I tried complex orchestration patterns - hierarchical planning agents, consensus-based decision making, specialized agents for different file types. Most of it was overengineered.
The pattern that works best is surprisingly simple: one agent writes, another reviews.
The Writer-Reviewer Pattern
Agent A gets a task and generates the implementation. Agent B receives the output and reviews it - checking for bugs, missing edge cases, and deviations from the codebase style. If Agent B finds issues, the feedback goes back to Agent A for revision.
That is it. No complex orchestration. No voting systems. No hierarchical planning layers. Just write and review.
Why This Works Better Than Single-Agent
A single agent reviewing its own output is like proofreading your own writing - you see what you intended, not what is actually there. A second agent brings fresh context. It reads the code without knowing what it was "supposed" to do, which means it catches assumptions that the first agent baked in.
The quality improvement is significant. In my experience, the writer-reviewer loop catches roughly 60-70% of the bugs that would otherwise make it to manual review. That does not eliminate human review, but it makes human review much faster because the obvious issues are already fixed.
Practical Setup
Keep both agents working in the same repo but on isolated branches or worktrees. The reviewer agent needs read access to the codebase for context but should not be making its own changes. Feed the review comments back as structured feedback, not freeform text - it reduces misinterpretation.
The key constraint is keeping the review scope small. Do not ask the reviewer to evaluate an entire feature branch. Review individual files or small changesets. Focused reviews produce better feedback.
The Human Still Matters
This pattern reduces the human review burden but does not eliminate it. Think of it as a first filter that catches the mechanical issues so you can focus on architecture and design decisions.
Fazm is an open source macOS AI agent. Open source on GitHub.