As AI adoption accelerates, organizations must ensure their generative and predictive models remain reliable and trustworthy in production. This session explores how to embed full-stack telemetry across data, model, and infrastructure layers to detect issues such as data and concept drift, latency degradation, distribution shifts, and hallucination patterns in generative systems. Participants will learn practical techniques for building monitoring and alerting pipelines, establishing feedback loops for retraining and governance, and integrating human oversight where needed. The discussion also highlights the organizational practices required to operate hybrid AI systems effectively in real-world environments, using concrete examples from ML pipelines and modern observability platforms.