deep-review

Multi-angle code review is performed by spawning 2–5 specialized sub-agents with focused mandates covering correctness, tests, consistency, UX, performance, and safety. Each sub-agent analyzes the code changes in parallel and returns actionable findings with specific file paths for quick remediation.

The best way to scale code reviews for large changes is distributing the workload across multiple sub-agents. This approach maintains governance-aware artifact traces by assigning focused mandates like architecture consistency and reliability to individual agents, capturing their findings into a consolidated review.

Yes, you can use parallel sub-agents to review code changes for UX and accessibility. You spawn a sub-agent with a dedicated UX and accessibility mandate alongside other agents for correctness or performance, returning actionable findings with file paths for quick remediation.

Sub-agent coordination for automated code analysis works by spawning 2–5 agents with focused mandates tailored to the change scope. Each agent independently analyzes its designated area, such as edge cases or developer experience, and returns findings that are consolidated into a single review.

No specific dependencies are required to run parallel code reviews with sub-agents. The process relies on spawning 2–5 sub-agents with focused mandates tailored to your change scope, capturing their findings into a consolidated review without external component requirements.

You should not use a multi-agent approach for code analysis when the change scope is too small to benefit from parallel mandates. The approach is designed to scale to changes of any size by distributing workload, but minimal changes may not require spawning 2–5 sub-agents.