The Rise of AI Observability: Why Traditional Monitoring Fails

As AI applications scale in production, traditional application monitoring proves fundamentally inadequate—unstable outputs, unpredictable latencies, and dynamic token costs demand a new observability approach. The four-pillar framework of logging, metrics, tracing, and evaluation is essential for understanding and optimizing AI systems in 2026.