consensus-review

Multi-agent consensus review improves code quality by dispatching a common input to multiple independent agents, combining their parallel findings into a structured report that flags disagreements and catches issues single-agent reviews miss.

The best way to run parallel UI reviews with conflict handling is using a sectioned workflow that dispatches multiple agents per section, aggregates their findings sequentially, and flags disagreements for user review.

You aggregate multi-agent code review findings by implementing an intermediate file workflow that performs per-section aggregation on parallel prompts, producing a final consensus report with scoring and recommendations.

Yes, you can use a consensus review workflow for plan assessments by applying its sectioned workflows and parallel prompts to distribute the plan across independent agents for sequential aggregation.

Single-agent code reviews miss structural issues because they lack the diverse findings generated by parallel, sectioned reviews where multiple independent agents evaluate the same input and surface disagreements through aggregation.

You should not use multi-agent consensus reviews when a rapid, linear assessment is sufficient, as the intermediate file workflow, parallel prompt dispatch, and sequential aggregation add processing overhead for comprehensive coverage.