I’ve spent years helping teams ship AI-driven features into production, and the hard truth is this: models don’t break the way the rest of your system does. They drift, decay, and behave unpredictably when the world shifts under them. In this talk, I’ll break down the failure patterns I’ve seen across real deployments — the subtle bugs, the cascading outages, the “the model was right but the system wasn’t ready” moments — and how to design architectures that survive them. We’ll explore guardrails, observation layers, resilience patterns, and how to build human-in-the-loop workflows that actually work at scale. If your team is shipping AI features — or about to — this session will show you what goes wrong first, and how to prevent it.