AI Agent Failure Has Become the Deployment Assumption

The institutional lesson is that autonomy is no longer judged by whether an agent can complete a polished task once. It is judged by whether the deployment environment can absorb failure without corrupting the work around it. Kevin122-06's fix list points away from model drama and toward process ownership: validation before action, logging during action, and people close enough to stop bad automation before it scales ^[1]. That turns agent deployment into an operations discipline, and teams selling autonomy without recovery systems are selling unfinished software.