Your AI feature worked perfectly for three months. Then quietly, response quality started degrading. Users began getting subtly incorrect answers. Some stopped using the feature entirely. By the time your team noticed from support tickets, user trust was already damaged. The problem? The embedding model provider had silently updated their model, shifting vector representations just enough to break your retrieval pipeline. Traditional software monitoring, uptime, latency, error rates, wouldn't have caught this. AI systems fail in unique ways: quality degrades gradually, outputs drift in tone, hallucination rates creep up. Monitoring AI requires watching dimensions that traditional observability doesn't cover.