We tend to assume that if every part of a system behaves correctly, the system itself will behave correctly. That assumption is deeply embedded in how we design, test, and operate software. If a service returns valid responses, if dependencies are reachable, and if constraints are satisfied, then the system is considered healthy. Even in distributed systems, where failure modes are more complex, correctness is still tied to the behavior of individual components. In modern AI systems, particularly those combining retrieval, reasoning, and tool invocation, this assumption is increasingly stressed under continuous operation. This model works because most systems are built around discrete operations. A request arrives, the system processes it, and a result is returned. Each interaction is bounded, and correctness can be evaluated locally. But that assumption begins to break down in systems that operate continuously. In these systems, this behavior is not the result of a single request.…