As AI systems move into real products, observability becomes a prerequisite for trust - not a nice-to-have. This session shows how to design a unified telemetry layer for AI workloads that correlates model behavior, system performance, cost signals, and business outcomes. We’ll cover practical patterns for tracing multi-step AI workflows, surfacing failure modes early, linking hallucinations and regressions to upstream causes, and building alerting that drives action instead of noise. The focus is on operational clarity: enabling teams to diagnose issues quickly, optimize intelligently, and run AI systems with confidence in production.