As enterprise leaders start deploying agentic workflows, they must establish the infrastructure to build and run them, one capable of fluidly routing a diverse set of workloads across the most efficient compute resources.This requires the ability to manage heterogeneous infrastructure, utilizing high-performance accelerators for large-scale training and inference, and utilizing CPUs for the critical orchestration layer of agentic AI. As autonomous agents become more prevalent, CPUs are ideally suited for managing agent state, semantic routing, tool selection, and spinning up secure, isolated sandboxes to safely execute untrusted generated code.The Google Axion advantageGoogle Cloud, with its workload-optimized Compute Engine portfolio, which includes general-purpose and specialized offerings, shines in addressing this need.Google Axion processors within this portfolio comprise a family of custom Arm processors engineered for performance, efficiency, and versatility, with a feature set that supports general-purpose workloads, CPU-based AI workloads, and other specialized tasks requiring Arm-native compatibility and direct hardware access.Axion is Google’s first custom Arm-based server CPU, introduced in April 2024. It is designed specifically for hyperscale cloud and AI-era data center workloads. Axion also leverages more than a decade of Google’s custom silicon innovation. This enables Google to more readily incorporate customer feedback into chip designs and address the more general, though complex, needs of CPUs. Matching workload type to the processorBhumik Patel, Director of Software Ecosystem Development at Arm, says the key to all of this is to match the workload type as closely as possible to computing capacity. CPU-powered cloud instances are a practical option for certain AI workloads, particularly those with smaller datasets or less complex models. “Agentic tasks such as orchestrating, talking to APIs, and memory management are all ones CPUs are good at, so it’s a distributed and concurrent AI workload,” Patel tells The New Stack. Intelligent workload-processing apportionment makes agentic AI more cost-effective and efficient than running all workloads on a single compute type.This efficiency is quantifiable. The Google Kubernetes Engine Agent Sandbox running on Google Axion N4A provides up to 30% better price performance than the next hyperscale cloud provider, says Google’s Mo Farhat, Axion Group Product Manager. The GKE Sandbox is an open-source Kubernetes-native primitive designed to execute untrusted AI-generated code safely. “Agentic tasks such as orchestrating, talking to APIs, and memory management are all ones CPUs are good at, so it’s a distributed and concurrent AI workload.”Intelligent workload decoupling makes agentic AI significantly more cost-effective. Google Cloud’s fluid computing foundation enables engineering teams to reserve specialized accelerators strictly for heavy reasoning and generative workloads, while leveraging Axion CPUs for high-concurrency orchestration and context management.Secure execution with the GKE Agent SandboxAs agents begin to generate and execute dynamic code autonomously, security is non-negotiable. Running AI-generated code directly in a standard cluster poses severe security risks, as untrusted code could potentially access other apps or the underlying cluster node.The Google Kubernetes Engine (GKE) Agent Sandbox resolves this by providing an isolated environment for safely executing untrusted code. Running on Axion-powered N4A instances, the sandbox provides up to 30% better price performance than comparable workloads on other hyperscalers.The vertical stack isolates sensitive tasks at the kernel level with sub-second latency.The vertical stack isolates sensitive tasks at the kernel level with sub-second latency. GKE Agent Sandbox natively supports gVisor (an open-source application kernel developed by Google that acts as a secure sandbox for containers) and default-deny Kubernetes network policy. Agent Sandbox provides pluggable interfaces for open-source sandboxes, such as Kata Containers, enabling users to customize their kernel isolation. Powered by gVisor technologies with software support from Arm’s architecture, the sandboxes intercept and validate system calls before they reach the host kernel. These isolated execution environments enable deployment of autonomous systems at scale without sacrificing performance or operational agility.To manage resources efficiently when agents sit idle, GKE Pod snapshots allow users to save and restore the exact process state of sandboxed environments. This functionality provides four major architectural benefits:Fast startup: Reduces sandbox startup time by restoring from a pre-warmed snapshot rather than initializing from scratch.Long-running agents: Pauses sandboxes that take a long time to run and resumes them later—or moves them across nodes—without losing progress.Stateful workloads: Persist an agent’s context, such as conversation history or intermediate calculations.Reproducibility: Captures a specific state to use as a baseline for spinning up multiple new sandboxes.Getting startedAs token generation, autonomous workflows, and continuous agent interactions grow exponentially, relying exclusively on accelerator-backed stacks for every task will become financially and architecturally unsustainable.The combination of CPU and accelerator execution accounts for bursts in agent activity and unpredictable demand spikes by eliminating the inference tax. Google Cloud’s full-stack advantage enables organizations to deploy the right machine for the job. By using Google Axion and GKE Agent Sandbox, builders can optimize total cost of ownership and security while maintaining the performance required for AI agents.Learn more about Google Axion.The post Arm and Google offer a smarter option to run agentic AI workloads appeared first on The New Stack.