Multi-Agent Distributional Shift Degrades AI Cooperation in Deployment

Executive Summary

ML agents trained in isolation fail when deployed alongside other adaptive agents, as behavioural variance from peers creates distributional shifts the original training never anticipated. In mixed-motive settings this breaks cooperative assumptions, exposing organisations to unpredictable system failures that single-agent testing and governance frameworks will not detect.