Comment by Yujiao Chen

We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and task state fixed, vary only one rule, and attribute the resulting change in collective behavior to that rule. Deployment rules causally alter collective safety: changing only the consequence rule moves mean fatality by 22 to 58 percentage points within every population. We package the methodology as a safety-case workflow that certifies a provisional rule region per deployment context and population, with explicit residual risks and monitoring obligations.