Agentic systems turn model reasoning into action through multi-step workflows that combine inference, tool use, code execution, retrieval, orchestration, and result handling. As these systems scale across the AI factory, performance depends not only on GPU acceleration, but also on the CPU work that happens between model steps.
Across the creation and deployment of an agentic system, the CPU ensures GPU resources and the entire AI factory deliver optimal performance. The CPU is on the critical path for reasoning, response time, and learning because it executes the work between model steps: sandboxed evaluations, tool calls, code execution, data processing, KV-cache coordination, and result handling.
For agentic AI, one of the most important CPU metrics is sustained per-core performance under full socket load. The socket may be packed with concurrent sandboxes, tools, simulations, orchestration tasks, and data services, but each agent or RL workflow still depends on sequential steps that must be finished before the next step can begin. More cores increase parallelism across workflows; faster cores determine how quickly each workflow advances, generating tokens, updating models, or serving the next turn.
