Iterative deployment of long-running models revealed new failure modes in OpenAI's safety frameworks. These systems struggle with stability over extended durations, necessitating tighter safeguards against drift. The research provides a blueprint for managing alignment in autonomous workflows. Practitioners must now prioritize monitoring long-term state consistency to prevent catastrophic failures in agentic systems.