Running Agentic AI at Scale on Google Kubernetes Engine

The AI industry crossed an inflection point. We stopped asking "can the model answer my question?" and started asking "can the system complete my goal?" That shift from inference to agency changes everything about how we build, deploy, and scale AI in the cloud. Google Kubernetes Engine (GKE) has quietly become the platform of choice for teams running production AI workloads. Its elastic compute, GPU node pools, and rich ecosystem of observability tools make it uniquely suited not just for model serving but for the orchestration challenges that agentic AI introduces. This blog walks through the full landscape: what kinds of AI systems exist today, how agentic architectures differ, and what it actually looks like to run them reliably on GKE. The AI Taxonomy: From Reactive to Autonomous Before diving into infrastructure, it's worth establishing what we mean by the different modes of AI deployment. Not all AI is "agentic," and the architecture you choose should match the behavior you need