What if an AI could interview you about your vulnerabilities, critique your responses, and generate a preventative action plan — all using tools available right now? This episode breaks down a four-agent architecture for cascading failure detection: an Ideation Agent that generates plausible disaster chains, an Interviewer Agent that forces you to articulate your response, a Critique Agent that finds your blind spots, and a Remediation Agent that documents actionable steps. We explore prompt engineering lessons from Anthropic’s Causal Chain Decomposition paper, the adversarial design patterns from IQT Labs’ Snowglobe framework, and a MIT Media Lab study on framing feedback for engagement. Plus, a practical comparison of LangGraph, CrewAI, and AutoGen for wiring it all together.