Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective

Researchers present a CPU-centric analysis of agentic AI systems, identifying bottlenecks in heterogeneous CPU-GPU architectures where most orchestration occurs on CPU. Two optimization methods—CPU-Aware Overlapped Micro-Batching and Mixed Agentic Scheduling—demonstrate significant latency reductions, addressing a critical infrastructure gap as agentic AI moves toward production deployment.

Agentic AI represents a fundamental shift from static LLM inference to dynamic, autonomous problem-solving systems that plan, reason, and call external tools iteratively. This architectural change creates unique performance challenges that existing GPU-optimized inference frameworks poorly address, since the CPU orchestrates most agentic capabilities and tool execution—a role traditionally considered secondary in AI infrastructure design.

The research fills a critical analytical gap by characterizing agentic AI workloads from a CPU-centric perspective. Previous optimization efforts focused almost exclusively on GPU compute utilization, leaving the CPU as an underexamined bottleneck. The authors identify that heterogeneous task execution creates skewed resource allocation patterns where CPU and GPU operate inefficiently in parallel. By profiling representative workloads across different hardware configurations, they isolate specific architectural constraints that degrade end-to-end performance.

The proposed optimizations—COMB for homogeneous workloads and MAS for heterogeneous scenarios—target CPU-GPU synchronization efficiency and concurrent utilization. Demonstrating 1.7x to 3.9x latency improvements directly impacts the viability of agentic AI in production environments where latency directly affects user experience and operational costs. For enterprises deploying multi-agent systems, these optimizations could substantially reduce infrastructure costs by maximizing hardware efficiency.

This research matters because agentic AI adoption is accelerating rapidly across enterprises, but infrastructure optimization has lagged architectural innovation. As systems scale from research prototypes to production workloads, CPU-bottlenecked performance becomes increasingly costly. The work provides both theoretical understanding and practical optimization techniques that infrastructure providers and AI deployment platforms must implement to enable efficient agentic AI scaling.

• →CPU orchestration emerges as primary bottleneck in agentic AI systems, previously overlooked in GPU-focused optimization research
• →CPU-Aware Overlapped Micro-Batching achieves up to 3.9x latency reduction under homogeneous open-loop load through improved concurrent utilization
• →Mixed Agentic Scheduling reduces minority request-type latency by up to 2.37x-2.49x in heterogeneous workloads, addressing resource allocation skew
• →Heterogeneous CPU-GPU architectures require fundamentally different optimization approaches than traditional monolithic LLM inference
• →Infrastructure efficiency directly impacts production viability of agentic AI systems as enterprise deployment accelerates