Demos of AI agents are easy. Running them reliably is not. Once agents move from local experiments into production, familiar DevOps questions return: where do they run, how are they observed, how do they access tools and secrets, how do we control cost, and how do we recover when they fail? This talk shows how cloud-native infrastructure can support production-grade agentic systems. We will map the building blocks of an agent platform, from Kubernetes-based inference with open models to API gateways, tool access, observability, safety boundaries, and lifecycle operations. The goal is not to hype agents, but to show what it takes to operate them like real software.