Cross-Review Step for Parallel Agents Catches Subtle Mistakes
Cross-Review Step for Parallel Agents Catches Subtle Mistakes
When you run multiple agents in parallel - one on the frontend, one on the backend, one on tests - each agent is confident in its own work. The problem is that confidence is not correctness. Agents are terrible at finding their own mistakes.
The Self-Review Blind Spot
An agent that just wrote a function will review it through the same mental model that produced it. If the model had a flawed assumption, the review shares that flaw. This is not unique to AI - humans have the same problem. That is why code review exists.
The AOG multi-agent CLI orchestrator adds a cross-review step. After parallel agents complete their tasks, each agent reviews another agent's output. The frontend agent reviews the backend changes. The backend agent reviews the test changes. Fresh eyes, even artificial ones, catch different things.
What Cross-Review Catches
The mistakes are subtle. An API endpoint that returns data in a different shape than the frontend expects. A test that passes but tests the wrong behavior. A database migration that works locally but would deadlock under concurrent access.
These are integration-level bugs that no single agent can catch because they only appear at the boundaries between components. Cross-review forces agents to look at those boundaries.
The Cost Is Worth It
Cross-review adds time. Each review is an additional LLM call with context about both the task and the code. For a three-agent pipeline, you add three review steps. But compared to debugging an integration failure after deployment, the cost is trivial.
The pattern works best when agents have overlapping domain knowledge. A backend agent that understands API contracts can meaningfully review frontend API calls. A frontend agent with no backend knowledge would just check syntax.
Fazm is an open source macOS AI agent. Open source on GitHub.