Agentic AI applications face challenges in delivering reliable and high-performing models due to complex multi-cloud environments and non-deterministic behaviors. Effective deployment requires comprehensive telemetry and standardized metrics for monitoring and improving models, alongside addressing issues like hallucinations and degraded performance. The rapid evolution of AI models by providers such as OpenAI and Anthropic introduces risks, including deprecation of older versions and automatic upgrades that may disrupt applications. To counter these, AI Model Versioning and A/B testing offer a unified view to compare models, identify bottlenecks, and ensure improvements in latency, reliability, and cost-effectiveness. Distributed tracing helps debug errors by tracking requests from input to completion, while real-time monitoring of token usage and security risks enhances system robustness. The AI Observability app allows users to analyze model performance with minimal setup, fostering proactive incident prevention through continuous learning and adaptation. Future enhancements will focus on multi-cloud setups, advanced visualization, and intelligent forecasting to further optimize AI services.