consensus-resolver

To resolve disagreements between multiple AI agents, apply deterministic rules to their outputs to achieve a consensus decision. This handles unanimous agreement, majority rule, ties, and critical blockers to ensure a definitive outcome.

During consensus decision making, tied agent votes are handled by applying predefined rules that guarantee a resolution. This deadlock prevention ensures a final outcome is reached even when agents split their votes equally.

To standardize multi-agent verification outputs, apply deterministic conflict resolution rules to conflicting code review results. If two agents pass and three reject a review, the final output becomes 'REJECT' with the rejecting reasons listed.

Yes, multi-agent coordination handles critical blockers differently than standard majority rule. The consensus process applies specific deterministic rules to critical blockers to ensure they are addressed before a final definitive outcome is achieved.

The best way to aggregate conflicting outputs from multiple AI agents is applying a deterministic consensus framework. This standardizes decision-making processes by resolving disagreements through predefined rules like majority rule or deadlock prevention.