One of the most unsettling characteristics of AI systems is how often they appear perfectly healthy. Infrastructure dashboards report stable CPU utilization, normal latency levels, and acceptable throughput. No alerts are triggered. From an operational standpoint, the system is functioning exactly as designed.
Yet the outputs are wrong. In many AI deployments, engineers eventually encounter this situation: a recommendation system begins suggesting irrelevant items, a support chatbot produces inconsistent answers, or an AI assistant gradually becomes less reliable in answering domain-specific questions.