Agentic AI workflows don't usually fail loudly, they fail silently. A tool call returns the wrong result but no error, an agent takes a plausible-but-incorrect path, or a multi-step chain quietly drifts off course without ever throwing an exception. In this lightning talk, I'll break down the common silent failure modes in agentic systems built on AWS (Bedrock Agents, multi-step tool orchestration), why traditional error handling and monitoring miss them, and practical detection strategies you can add today: output validation checkpoints, tool-call auditing, and lightweight evals that catch drift before it reaches users. The goal is a clear, actionable framework for building agentic workflows you can actually trust in production.