How To Define CPU And Software Stack Performance For Agentic AI
How To Define CPU And Software Stack Performance For Agentic AI
Arm proposes a use-case-driven framework for measuring the CPU's performance contribution in Agentic AI. The framework splits CPU-visible work into generic agent framework functions and use-case-specific tool workloads, uses five representative workload archetypes, and combines component-level with end-to-end benchmarking methods. It also proposes a replayable agent trajectory scheme to isolate CPU work from model and network effects, reducing variance from non-determinism.
Measuring the performance demands of agentic AI; assessing CPU bottlenecks, SW stack efficiency, and system-level tradeoffs.
October 14th, 2026 - By: Arm
Agentic AI changes the CPU performance problem. A completed task can involve repeated model calls, retrieval, state management, tool execution, application interactions, policy checks and validation loops. Accelerators remain central to model execution, while CPUs coordinate and execute much of the surrounding system path from user intent to completed work.
This paper 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. Five representative workload archetypes are used to capture distinct deployment venues, tool classes and CPU stress profiles. The paper then describes two complementary measurement approaches: component benchmarking, which emphasizes reproducibility and attribution and end-to-end benchmarking, which emphasizes realistic task-level outcomes.
The objective is not to prescribe a finished benchmark suite or a single figure of merit. It is to define the measurement boundaries, workload categories and methodological gaps that need industry agreement. In particular, replayable agent trajectories offer a path to separating CPU work from model and network effects while reducing the run-to-run variability introduced by non-deterministic agent behavior.
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Fig.1: Agentic AI infrastructure components and services. Source: Arm
Source: Semiconductor Engineering Chips and Packaging · semiengineering.com