santa-method

Dual-agent verification reduces single-review biases by spawning two independent reviewers with identical rubrics to evaluate the same AI output. A convergence gate requires both reviewers to reach a unified verdict before shipping, ensuring high-stakes publishing accuracy.

To implement a convergence loop for regulatory-compliant deployments, spawn two independent reviewers per round. Run the memory-free re-evaluation loop until the verdict converges to NICE, escalating to a human-in-the-loop option if convergence fails.

When independent dual review fails to converge, the escalation path triggers a human-in-the-loop option. This ensures safe ship for high-stakes publishing and regulatory-compliant deployments by allowing manual intervention when the automated convergence gate fails.

Yes, you can use multi-agent verification for batch generation workflows. The phased architecture applies independent dual review with fresh reviewers per round, ensuring accuracy and quality assurance across high-volume AI generation tasks.

The convergence gate uses a memory-free re-evaluation loop, meaning it does not require memory between review rounds. Fresh reviewers are spawned per round to maintain independence and reduce single-review biases in AI safety validations.