The bug report was received as a customer complaint. An AI agent responsible for managing vendor onboarding had sent a rejection email to a supplier the company had been trying to close for three months. Nobody had authorized it. Nobody had configured it to reject vendors in that category. The agent autonomously made the decision after analyzing a compliance document and cross-referencing it with an internal policy database. By the time the complaint arrived, the reasoning chain that produced the decision had been discarded. The agent had no memory of why it did what it did. The logs showed the action but not the thought.That story is fictional in its specifics but accurate in its structure. This phenomenon represents a class of problems that teams deploying AI agents in production are encountering with increasing frequency: the agent performed an action, the output is visible, but the intermediate reasoning, including the sequence of context retrievals, model calls, tool invocations, and decisions that led to the output, is either absent, incomplete, or stored in a format that renders post hoc investigation nearly impossible. Traditional observability was not designed for systems that exhibit cognitive processes.