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arxiv: 2511.00739 · v3 · submitted 2025-11-01 · 💻 cs.AI · cs.LG· cs.MA

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

Ritik Raj , Souvik Kundu , Ishita Vohra , Hong Wang , Tushar Krishna This is my paper

Pith reviewed 2026-05-18 00:52 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.MA
keywords agentic AICPU-centric analysisscheduling optimizationmicro-batchinglatency reductionheterogeneous workloadsLLM servingsystem bottlenecks
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The pith

Agentic AI execution benefits from CPU-centric bottleneck analysis that leads to overlapped micro-batching and mixed scheduling, cutting latencies by factors of 1.7x to 3.9x on hybrid hardware.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to shift focus from GPU-heavy LLM inference to the CPU's central role in agentic AI, where planning, tool calls, reasoning, and adaptation often execute or get orchestrated on the CPU in heterogeneous CPU-GPU setups. It starts with compile-time characterization to pick representative workloads that reflect algorithmic variety, then measures end-to-end latency and throughput at runtime on two distinct hardware systems to pinpoint specific architectural limits. From those measurements the authors derive two scheduling methods: CPU-Aware Overlapped Micro-Batching for uniform workloads and Mixed Agentic Scheduling for mixed request types, both designed to raise concurrent CPU-GPU use and lessen uneven resource splits. A sympathetic reader would care because agentic systems are expanding into autonomous problem-solving that depends on efficient CPU orchestration; fixing overlooked CPU bottlenecks could make real deployments faster and more scalable without new hardware.

Core claim

We first present a compile-time characterization of agentic AI execution and choose representative workloads to capture the algorithmic diversity. We then perform runtime characterization of the representative workloads analyzing the end-to-end latency and throughput on two different hardware systems to isolate respective architectural bottlenecks. Based on the insights on the bottlenecks, we finally present two scheduling optimizations, namely, 1. CPU-Aware Overlapped Micro-Batching (COMB) and 2. Mixed Agentic Scheduling (MAS) on homogeneous and heterogeneous agentic workloads, respectively. These methods optimize for improved CPU-GPU concurrent utilization while reducing skewed resource a

What carries the argument

CPU-Aware Overlapped Micro-Batching (COMB) for homogeneous cases and Mixed Agentic Scheduling (MAS) for heterogeneous cases, which increase concurrent CPU-GPU utilization and balance allocation across request types.

If this is right

  • COMB delivers up to 1.7x lower P50 latency for standalone homogeneous workloads.
  • Under homogeneous open-loop load, COMB yields up to 3.9x lower service latency and 1.8x lower total latency.
  • MAS reduces total latency for minority request types by up to 2.37x at P50 and 2.49x at P90 in heterogeneous open-loop settings.
  • Both techniques improve performance by raising CPU-GPU overlap and correcting skewed resource allocation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending COMB and MAS to multi-agent workflows with dozens of tools could reveal whether the same overlap and mixing principles scale without additional coordination overhead.
  • The CPU-centric view might apply to other hybrid systems such as real-time robotic planning, where similar tool-orchestration loads occur.
  • If new agentic models shift more work to the CPU, the bottleneck patterns identified here could become the dominant constraint rather than GPU compute.
  • Combining elements of COMB and MAS into a single adaptive scheduler could handle workloads that transition between homogeneous and heterogeneous phases.

Load-bearing premise

The chosen representative workloads adequately represent the full diversity of agentic AI tasks and their CPU demands.

What would settle it

Measure the same latency and throughput metrics on a fresh collection of agentic workloads that differ substantially from the original representative set; if the reported speedups shrink or disappear, the optimizations do not generalize.

Figures

Figures reproduced from arXiv: 2511.00739 by Hong Wang, Ishita Vohra, Ritik Raj, Souvik Kundu, Tushar Krishna.

Figure 1
Figure 1. Figure 1: Characterization of agentic AI workloads on the basis of (a) Orchestrator (LLM and Host) (b) Agentic Path (Static and Dynamic) and (c) Repetitiveness (Single-step and Multi-step) of cores, coherence and synchronization or GPU factors - main memory capacity and bandwidth. 3 CPU dynamic energy consumes up to 44% of the total dynamic energy at large batch sizes. To the best of our knowledge, this is the first… view at source ↗
Figure 2
Figure 2. Figure 2: (a) Haystack with ENNS retrieval on QA benchmarks (b) Toolformer with WolframAlpha API on Math benchmarks (c) Chem￾crow with literature (Arxiv/Pubmed) search tool on Chemistry benchmarks (d) Langchain with web search and LexRank summarization tools on QA benchmarks (e) Mini-SWE-Agent with bash/Python execution tools on coding benchmarks We choose a custom agentic pipeline (web search − > summarization − > … view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of multi-processing and multi-threading with sequential baseline (single core) for Langchain workload 4.3 Throughput We begin by analyzing CPU parallelism in Section 4.3.1 and deriving effective strategies. Thereafter, we pair the learnt strategy on CPU side along with well-studied GPU paral￾lelization strategy to parallelize multiple agentic requests. However, we identify two throughput bottlen… view at source ↗
Figure 4
Figure 4. Figure 4: (a) vLLM throughput saturation for GPT-OSS-20B model (b) Throughput saturation for various agentic workloads (c) Average time taken by different components in Langchain benchmark showing a critical CPU context switching bottleneck at batch size 128 and remote memory references incur higher latency that stall pipelines and saturate on-socket fabrics.(Mattson et al., 2008) argues that the overhead of cache c… view at source ↗
Figure 5
Figure 5. Figure 5: CPU (AMD Threadripper) and GPU (Nvidia B200) dy￾namic energy consumption for Langchain workload CPU 0-31 CPU 64-95 CPU 96-128 CPU 0-31 CPU 32-64 CPU 32-63 (a) Multi-processing (MP) (b) CGAM Time CPU 0-31 CPU 64-95 (c) CGAMoverlap Methods P50 P90 MP 2x 2x CGAM x 2x CGAM_overlap 1.2x 1.8x x 2x CPU 31-63 CPU 96-128 Batch Tool (CPU) Batch Inference (GPU) x 2x [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Timeline of batched agentic AI inference for (a) Multi￾processing, (b) CGAM, and (c) CGAMoverlap Key Takeaway 3: CPU dynamic energy share becomes significant (44%) at large batch size (128), as CPU parallelism is less energy efficient compared to GPU. 5 OPTIMIZATIONS Based on throughput saturation insights (Section 4.3), we present two scheduling optimizations- 1 CPU and GPU Aware Micro-batching (CGAM- Sec… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of CGAM and CGAMoverlap using Bcap = 64 against baseline (multi-processing or multi-threading) on (a) Langchain workload on FreshQA benchmark, (b) Haystack workload on NQ benchmark and (c) SWE-Agent on APPS benchmark 5.1.3 CGAMoverlap We can also utilize the remaining idle CPUs for more speed-up at the cost of energy. For mixed agentic work￾loads with comparable CPU and GPU latencies, we present… view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of MAWS against multiprocessing baseline on 128 mixed Langchain tasks (half LLM heavy, half CPU heavy) [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of MAWS+CGAM against multiprocessing baseline on 256 mixed Langchain tasks Therefore, we need to limit the CPU usage of LLM heavy tasks. Since, they are LLM-heavy, we can use the lighter multi-threading for parallel vLLM API I/O. This frees up a lot of CPU resources making the CPU heavy tasks more ef￾fective. Therefore, we can optimize mixed agentic AI infer￾ence through adaptive multi-processin… view at source ↗
read the original abstract

Agentic AI serving converts monolithic LLM-based inference to autonomous problem-solvers that can plan, call tools, perform reasoning, and adapt on the fly. Due to diverse task execution need, such serving heavily rely on heterogeneous CPU-GPU systems with majority of the external tools responsible for agentic capability, either run on or are orchestrated by the CPU. Towards having a deeper understanding of its role, this paper aims to characterize and analyze the system bottlenecks introduced by agentic AI workloads from a largely overlooked CPU-centric perspective. We first present a compile-time characterization of agentic AI execution and choose representative workloads to capture the algorithmic diversity. We then perform runtime characterization of the representative workloads analyzing the end-to-end latency and throughput on two different hardware systems to isolate respective architectural bottlenecks. Based on the insights on the bottlenecks, we finally present two scheduling optimizations, namely, 1. CPU-Aware Overlapped Micro-Batching (COMB) and 2. Mixed Agentic Scheduling (MAS) on homogeneous and heterogeneous agentic workloads, respectively. In specific, these methods optimize for improved CPU-GPU concurrent utilization while reducing skewed resource allocation for heterogeneous execution. Experimental evaluations on the two hardware systems demonstrate the efficacy of COMB in yielding up to 1.7x lower P50 latency in standalone homogeneous workload execution and up to 3.9x/1.8x lower service/total latency under homogeneous open-loop load. Additionally, for heterogeneous open-loop load, MAS can reduce the total latency for minority request-type by up to 2.37x/2.49x at P50/P90 percentile.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper characterizes agentic AI execution from a CPU-centric perspective on heterogeneous CPU-GPU systems. It performs compile-time analysis to select representative workloads capturing algorithmic diversity, conducts runtime measurements on two hardware systems to isolate architectural bottlenecks, and proposes CPU-Aware Overlapped Micro-Batching (COMB) for homogeneous workloads and Mixed Agentic Scheduling (MAS) for heterogeneous workloads to improve concurrent utilization and reduce skewed allocation. Experiments report up to 1.7x lower P50 latency with COMB and up to 3.9x/1.8x and 2.37x/2.49x latency reductions with MAS under open-loop loads.

Significance. If the results hold with stronger validation, the work provides timely empirical insights into overlooked CPU bottlenecks in agentic AI serving and practical scheduling techniques for better CPU-GPU utilization. The focus on compile-time/runtime characterization and concrete latency gains on real hardware systems adds practical value for optimizing autonomous agent deployments.

major comments (2)
  1. [§3] §3 (compile-time characterization and representative workloads): The selection of workloads is described as capturing 'algorithmic diversity' but no verification, diversity metrics, or justification of representativeness (e.g., coverage of tool complexity or interaction patterns) is provided. This assumption is load-bearing for the central claim, as non-representative workloads would undermine the identified CPU bottlenecks and the reported efficacy of COMB and MAS.
  2. [§5] §5 (runtime characterization and experimental evaluation): The reported improvements (1.7x P50 latency for COMB; 3.9x/1.8x service/total and 2.37x/2.49x minority latency for MAS) lack details on statistical variance across runs, exact baseline scheduler implementations, workload selection criteria, and hardware-specific configurations. These omissions make it difficult to assess robustness and reproducibility of the bottleneck isolation and optimization claims.
minor comments (2)
  1. [Abstract] The abstract introduces COMB and MAS without expanding the acronyms on first use, which reduces immediate readability.
  2. [§5] Figure captions and axis labels in the runtime characterization plots could more explicitly state the exact metrics (P50 vs. P90) and load conditions for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript characterizing CPU bottlenecks in agentic AI serving. We address each major comment point by point below and have revised the paper to improve justification and reproducibility where the comments identify gaps.

read point-by-point responses

  1. Referee: [§3] §3 (compile-time characterization and representative workloads): The selection of workloads is described as capturing 'algorithmic diversity' but no verification, diversity metrics, or justification of representativeness (e.g., coverage of tool complexity or interaction patterns) is provided. This assumption is load-bearing for the central claim, as non-representative workloads would undermine the identified CPU bottlenecks and the reported efficacy of COMB and MAS.

    Authors: We agree that the original manuscript would benefit from explicit justification and metrics for workload representativeness. In the revised version, we have expanded Section 3 with a new subsection that details our selection criteria. Workloads were drawn from widely used agentic frameworks (LangChain, AutoGPT, and ReAct-style agents) to span variations in tool-call density, reasoning depth, and external service interaction patterns. We now report simple compile-time diversity metrics, including variance in CPU instruction counts, memory access patterns, and tool complexity scores across the chosen set. While these workloads do not exhaustively cover every conceivable agentic behavior, they target the primary sources of CPU heterogeneity that drive the bottlenecks analyzed in the paper. This addition directly supports the central claims without altering the experimental results. revision: yes

  2. Referee: [§5] §5 (runtime characterization and experimental evaluation): The reported improvements (1.7x P50 latency for COMB; 3.9x/1.8x service/total and 2.37x/2.49x minority latency for MAS) lack details on statistical variance across runs, exact baseline scheduler implementations, workload selection criteria, and hardware-specific configurations. These omissions make it difficult to assess robustness and reproducibility of the bottleneck isolation and optimization claims.

    Authors: We concur that additional experimental details are required for robustness and reproducibility. The revised Section 5 now includes: (1) statistical variance reported as mean ± standard deviation over five independent runs for all latency and throughput figures; (2) the baseline scheduler described as the default FIFO scheduler in our serving stack with no explicit CPU affinity or priority settings; (3) workload selection criteria explicitly linked to the compile-time analysis (high vs. low tool-call density and reasoning chain length); and (4) precise hardware configurations, including CPU model (Intel Xeon Gold 6248R), GPU (NVIDIA A100), memory bandwidth, and OS/kernel settings for each of the two systems. A new appendix provides configuration files and raw measurement data. These changes allow readers to replicate the bottleneck isolation and the reported gains from COMB and MAS. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical characterization and scheduling optimizations rest on direct measurements

full rationale

The paper performs compile-time and runtime characterization of selected agentic AI workloads on heterogeneous CPU-GPU hardware, identifies bottlenecks through latency and throughput measurements, and evaluates two proposed schedulers (COMB for homogeneous and MAS for heterogeneous cases) via explicit experiments on two systems. All reported gains (1.7x P50 latency, 3.9x/1.8x service/total latency, 2.37x/2.49x minority latency) are obtained from these hardware runs rather than from any equations, fitted parameters renamed as predictions, or self-citations that close a definitional loop. Workload selection is presented as a methodological choice whose representativeness is an external assumption, not a self-referential derivation; no load-bearing step reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper rests on standard domain assumptions about heterogeneous hardware and workload diversity without introducing new free parameters or invented entities.

axioms (1)
  • domain assumption Agentic AI serving heavily relies on heterogeneous CPU-GPU systems with majority of external tools run on or orchestrated by the CPU.
    This premise is stated directly in the abstract and underpins the entire CPU-centric perspective.

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