Agentic AI changes how CPU performance should be assessed. A completed task now involves repeated model calls, retrieval, state management, tool execution, application interactions, policy checks, and validation loops. While accelerators remain central to model execution, CPUs coordinate and run much of the surrounding system path from user intent to finished work.
A recent article proposes a use-case-driven framework for evaluating that CPU contribution. It separates CPU-visible work into two complementary areas: common agentic framework functions and use-case-specific tool workloads. To capture distinct deployment venues and stress profiles, the framework defines five representative workload archetypes. It also pairs two measurement approaches: component benchmarking for reproducibility and attribution, and end-to-end benchmarking for realistic task-level outcomes.
The goal is not a finished benchmark suite or a single figure of merit. Instead, the article argues for industry agreement on measurement boundaries, workload categories, and methodological gaps. A notable suggestion is the use of replayable agent trajectories, which can reduce run-to-run variability from non-deterministic agent behavior and help isolate CPU work from model and network effects.