Research Paper Analyzes CPU Bottlenecks in Agentic AI Systems

A new research paper examines the computational demands of agentic AI systems from a CPU-centric perspective. Published on arXiv under identifier arXiv:2511.00739v3, the study focuses on how these autonomous problem-solvers, which utilize large language models for planning, tool usage, and reasoning, rely heavily on heterogeneous CPU-GPU architectures. The research characterizes agentic AI execution at compile-time and selects representative workloads to capture algorithmic diversity. Runtime analysis measures end-to-end latency and throughput across two distinct hardware configurations to identify architectural bottlenecks. Most external tools enabling agentic capabilities either operate on or are orchestrated by the CPU, making its performance critical. The paper aims to deepen understanding of system bottlenecks introduced by these workloads, an area previously overlooked.