CPUs are becoming essential infrastructure for AI agents, not just accelerators

The spotlight in AI infrastructure has long focused on GPUs and TPUs, but a quieter shift is underway: CPUs are becoming indispensable as the nature of AI work itself transforms. Rather than simply generating text, modern AI systems are now tasked with executing actions, calling external tools, and running generated code—and that execution layer depends heavily on traditional processors.

From conversation to autonomous action#

The fundamental difference lies in what AI systems are being asked to do. Early chatbots produced responses; today's agentic AI systems actively perform tasks. An autonomous agent might call multiple APIs, orchestrate workflows, spawn isolated environments, and manage branching logic to decide what to do next. This orchestration work—the constant decision-making and control flow that drives agents—plays directly to the strengths of CPUs.

Smaller language models, typically in the six- to eight-billion-parameter range, are handling specialized subtasks within agentic workflows. A CPU-based system can process roughly 25 tokens per second for these workloads, a throughput sufficient for many autonomous agent scenarios. Beyond inference on small models, CPUs also power data preparation, semantic search, vector database operations, and the scheduling logic that keeps agents running.

Securing autonomous execution with isolation#

One critical challenge emerges when agents generate and execute their own code. To prevent malicious or buggy LLM-generated code from compromising production systems, agents need sandboxed environments where they can operate safely.

Google's open-source gVisor project provides an isolation layer between applications and the host operating system, creating a trusted environment for code execution. The company's GKE Agent Sandbox extends this capability further, enabling rapid provisioning of isolated containers. According to Google's teams, the platform can spin up 300 sandboxes per second per cluster—a scale requirement that would be impossible without efficient CPU-based orchestration. The approach uses pod snapshots and warm pools of pre-suspended environments to minimize startup latency.

Cost and efficiency gains#

Google's Axion Arm-based processors are positioned as the infrastructure backbone for agentic workloads. The company claims Axion delivers up to 2x better price-performance than comparable current-generation systems, alongside over 60% improved energy efficiency—a significant advantage for operations running hundreds of agents simultaneously.