Whileone Tech Space

Every silicon vendor at every keynote this year has said some version of the same sentence: the next CPU generation will be built for agentic workloads. Intel talked about CPUs as the orchestration layer for agentic systems, Arm argued that agents are driving a fresh wave of data centre CPU demand, and NVIDIA introduced Vera as a CPU designed for agentic workflows rather than general-purpose compute. The direction is settled. The definition is not. Because here is the questio

Agentic AI has done something no previous AI workload managed: it made the host CPU interesting again. In single-shot inference the CPU tokenizes a prompt, hands off to the accelerator, and waits. In an agent loop it runs everything between model calls — orchestration and control flow, tool dispatch, sandbox spawn and teardown, file I/O, test execution, retrieval, JSON parsing, retries. Recent characterization work has put numbers on this. In tool-dominated agentic pipelines,

For decades, AI benchmarks lived in comfortable isolation. A model answered a question, we checked the answer, we assigned a score. Agentic AI broke that contract. When a model can browse the web, write and execute code, call external APIs, and chain its own decisions across hundreds of steps, a single accuracy number tells you almost nothing about whether the system is actually trustworthy. Evaluating an agent is less like grading an exam and more like auditing a junior empl

Discover how agentic AI is flipping the performance engineering paradigm. Learn how to evolve from a "loop tuner" to a "constraints governor" with our checklist for AI-proof SLOs.