For decades, infrastructure has behaved predictably. Control planes scheduled workloads, routed traffic, enforced policies, and executed deterministic logic engineers could reason about, debug, and trust. That assumption is beginning to change. As AI systems become embedded inside operational tooling, automation platforms, deployment workflows, and decision-making pipelines, infrastructure is slowly shifting from deterministic behavior toward probabilistic behavior. Modern systems can now generate actions instead of simply executing predefined logic. They can interpret intent, make recommendations, trigger workflows, and increasingly operate with partial autonomy. This introduces an entirely new class of reliability challenges. In this talk, we explore how AI-driven systems break many traditional assumptions of Site Reliability Engineering. Unlike conventional distributed systems, AI systems can fail silently while infrastructure metrics remain healthy. Outputs may vary between identical requests. Hallucinations, semantic drift, retrieval failures, and probabilistic decision-making create operational risks that existing observability practices were never designed to detect. This beginner-friendly session explores: * Why AI systems fail differently from traditional systems * The operational risks of probabilistic infrastructure * Hallucinations, semantic degradation, and silent failures * Why traditional monitoring is insufficient for AI-native systems * Emerging patterns for AI observability and reliability * What the future of SRE may look like in increasingly autonomous environments This talk is designed for SREs, platform engineers, DevOps practitioners, and cloud engineers interested in the future intersection of reliability engineering, AI systems, and autonomous infrastructure.
Kaustubha V is a Solution Architect at Microsoft in the Silicon Cloud organization, where she works on scalable cloud platforms, AI-driven systems, automation frameworks, and developer productivity solutions. Her work focuses on building reliable infrastructure and intelligent systems for large-scale engineering workloads across Azure and multi-cloud environments. At Microsoft, she designs solutions that use machine learning to optimize compute resources such as memory, runtime, and CPU requirements. She has contributed to production-grade ML pipelines, telemetry platforms, benchmarking frameworks, and cloud-native automation services that improve operational efficiency and simplify engineering workflows. Kaustubha has strong experience in Azure, AWS, Google Cloud, Kubernetes, distributed systems, and secure cloud deployments. She holds multiple cloud and AI certifications and has earned the Kubestronaut distinction from the Cloud Native Computing Foundation. Beyond her engineering role, Kaustubha is active in AI research and technology communities. Her research interests include generative AI, machine unlearning, responsible AI, and bias mitigation in machine learning systems. She has authored research papers accepted at international workshops and conferences, including NeurIPS,ICCV, GHC ICLR, GHCI, IEEE and leading data science venues. She is also passionate about mentoring and community impact. Through initiatives such as Women Techmakers and Microsoft’s Code Without Barriers, she supports students and early-career professionals in learning cloud computing, AI, and software engineering.
