Autonomous Systems Don't Fail. They Drift Until They Break.

Your AI system isn't going to crash. It's going to drift. A recommendation engine making 1.4 model calls instead of 1. A retrieval pipeline fetching 5 chunks instead of 3. An agent retrying twice instead of once. Nothing broke. Until the cost doubled. The Three Categories of Autonomous Systems Drift Cost drift — token consumption creeps up invisibly. The signal is in your cloud bill, which most engineers don't see in real time. Behavior drift — outputs change in ways subtle enough to pass quality checks but meaningful enough to affect user experience. Decision drift — autonomous agents make subtly different choices than they were designed to make, compounding across every request in the queue. Why Monitoring Doesn't Catch It Standard monitoring answers: Is the system up? Is latency within SLA? Drift detection requires different instrumentation: Per-request token consumption tracked over time Model call counts per workflow Retry rate trends by agent and tool Context utilization percenta