The Production Metric That Warns Us Before AI Failures Happen

Most AI failures do not start with outages. They start with drift. The system still responds. Requests still complete. Dashboards still look mostly healthy. But operational quality starts degrading quietly underneath. That is why traditional infrastructure monitoring is not enough for enterprise AI systems. CPU usage will not tell you the model is slowly losing reasoning consistency. API uptime will not tell you retrieval pipelines are becoming polluted. Latency alone will not tell you memory assembly is growing unstable.