Cloud infrastructure powers the digital world—but at hyperscale, even minor incidents can ripple into massive disruptions. At Microsoft, I led the design and deployment of an autonomous diagnostic fabric for Azure—a system that acted as a Copilot for Azure, reimagining how cloud reliability could be managed. By embedding agentic AI directly into the Cloud OS workflow, we empowered millions of servers worldwide to self-diagnose issues and accelerate resolution cycles—cutting incident settlement times nearly in half.
Behind the scenes, this initiative required a rethinking of Azure’s operational backbone. I built a next-generation reasoning pipeline on Azure Data Explorer for high-speed data ingestion and querying. C#/.NET for scalable, enterprise-grade orchestration. Azure OpenAI for intelligent reasoning and contextual understanding. This pipeline didn’t just process logs—it enriched and classified them with over 99% accuracy, surfacing precise remediation recommendations and enabling engineers to act with clarity and speed.
The results were transformative. Incident settlement times were cut by nearly 50%, diagnostics and triage became proactive rather than reactive, and teams across the enterprise benefited from actionable, AI-driven insights. What emerged was a new paradigm in cloud reliability—an AI-native ecosystem where triage, diagnostics, and resolution operated with intelligence at scale.
As we look ahead, we can safely say that this work wasn’t just about solving today’s challenges. It showcased the potential of agentic AI to hardwire resilience into the backbone of global cloud infrastructure. By laying the foundation for autonomous platform operations, we’ve taken a step toward a future where cloud systems not only support innovation but also sustain themselves intelligently.