reviewer-protocol

To enforce a strict code review rejection workflow with lockout semantics, this protocol prevents the original author from self-revising rejected work and mandates a different agent to perform the fix. It manages reassignment or escalation to maintain objective quality assurance.

If all eligible agents are locked out after a rejection, the protocol provides a deadlock handling mechanism that escalates the rejected work to the user. This prevents inefficient feedback loops when no independent agents are available to perform revisions.

Yes, you can use this rejection protocol to manage quality assurance across agent teams. It orchestrates reviewer actions by reassigning rejected work to different agents or escalating it, ensuring independent review processes within your AI agent workflows.

The best way to prevent agents from reviewing their own fixes is applying strict lockout semantics. When a reviewer rejects work, the protocol locks out the original author and automatically orchestrates reassignment of the revision task to a different agent.

An agent orchestration workflow needs lockout semantics to prevent inefficient feedback loops caused by self-revision. Enforcing that a different agent performs revisions after a code review rejection maintains objective quality assurance and independent oversight.

Yes, the reviewer-protocol supports escalation when no agents are available for reassignment. It includes deadlock handling that escalates the rejected work to the user when all eligible agents are locked out, ensuring the quality assurance process is not stalled.