Architecting Scalable Clouds: Integrating AI
The hard part of AI in the cloud isn't scaling anymore—it's governing agents that act on your infrastructure on their own.
Autoscaling and workload optimization are no longer the challenge. They're commoditized—built into the cloud platforms and AIOps tools you already use. The real shift begins when AI stops recommending and starts acting: agents that provision, deploy, and change your infrastructure on their own.
This session focuses on the part most teams underbuild—trust boundaries, blast radius, and policy-as-code that keeps an agent inside its guardrails. The principle worth holding onto: agents earn autonomy, they don't start with it. You'll leave with a clear approach for building AI systems that scale without handing over control you can't take back.
About Sumir Arora
Sumir Arora is a Senior Solution Architect with 15+ years across cloud, integration, and platform engineering. His recent work sits at the intersection of platform engineering and AI—building agentic systems that bring autonomy to operational workflows like change management, troubleshooting, and API testing.
He designs for the hard part: letting automation scale without losing control. That means governance, guardrails, policy-as-code, and clear architectural boundaries—the things that make autonomous systems safe to run in production.