Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics
Wonmi Choi, Minuk Park, Zhixiong Niu, Yongqiang Xiong, Chuck Yoo, Gyeongsik Yang
cs.AI · cs.PF
Quick verdict
Careful study: agent resource bottlenecks are task-dependent; admission/allocation demos convince but rest on one host, one LLM, and replayed LLM trajectories.
Summary
This paper characterizes how LLM-based AI agents consume local resources across three task types—retrieval-augmented QA (RAQA), web search, and coding—over seven benchmarks (HotpotQA, TriviaQA, BFCLv4, QASC, SWE-bench, BigCodeBench, APPS). ReAct-style agents call the Gemini 3 Flash API while running tools in local containers on a 24-vCPU Azure VM; the authors record and replay LLM trajectories to suppress nondeterminism. Three findings emerge: latency and local resource demand are decoupled (web search is slow but CPU-light; RAQA spends 62% of latency in local retrieval behind a ~98 GB resident index); concurrency exposes task-dependent bottlenecks (RAQA saturates CPU at 1 req/s, BigCodeBench saturates disk writes near the 207 MB/s fio ceiling, web-search memory grows ~5.8×); and faster LLM responses or extra cores help only when local execution sits on the critical path. From a sensitivity analysis over LLM response time (1–3×) and container CPU allocation (2–8 cores), the authors demonstrate two minimal policies: CPU-aware tool admission cuts HotpotQA latency 3.23× but worsens SWE-bench 2.76×, and task-aware CPU allocation reduces mixed-workload average latency 32% (6-core) and 23% (12-core). Main limitations: single host, single LLM and workflow, replay-based methodology, no task-accuracy reporting, and no code or trajectories released.
Review complete
Assessment at a glance
4
risk flags identified
● high 0
● medium 3
● low 1
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- End-to-end agent latency does not reflect tool resource demand: RAQA stresses local CPU and resident memory (62% local latency, ~98 GB index), web search stresses remote waits (49% web-API time, 14% average CPU), and coding is mixed (39% tool execution).
- Concurrency exposes task-dependent bottlenecks: CPU contention (RAQA CPU-bound at 1 req/s), disk I/O saturation (BigCodeBench ~200 MB/s vs 207 MB/s ceiling), and memory growth (web search ~5.8× from 0.5 to 6 req/s).
- Faster LLM responses and additional CPU cores do not always accelerate agents; each helps only when local tool execution is on the critical path, as shown by the joint sensitivity analysis over LLM time (1–3×) and cores (2–8).
- Analysis-guided policies work as predicted: CPU-aware tool admission improves HotpotQA 3.23× (but worsens SWE-bench 2.76×), and task-aware CPU allocation cuts mixed-workload average latency 32% at 6 cores and 23% at 12 cores.
Strengths
Gap-filling joint characterization
Table I shows no prior study (Raj, Kim, AgentCgroup) covers containerized agents across RAQA, web search, and coding while jointly varying concurrency, LLM response time, and CPU allocation; §IV delivers exactly this matrix.
Controlled methodology for a nondeterministic workload
§IV-A2 records full LLM trajectories (outputs + per-step latencies) once and replays them in all later experiments, removing LLM output nondeterminism; resource metrics via cgroups, an fio disk ceiling (207 MB/s), and CFS throttling ratio are well chosen.
Non-obvious bottleneck attribution
Analysis-to-optimization loop validated in both directions
Honest scoping
Weaknesses
Policies are minimal and hand-tuned offline
No accuracy or correctness metric anywhere
Generality threats from single-host, single-LLM, single-workflow design
Thin statistical reporting
Replay assumption asserted but data omitted
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Preliminary Review — 'Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics' (MASCOTS 2026)
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Frequently asked questions
What is the main finding of 'Not All AI Agents Are Equal'?
That LLM-agent performance bottlenecks depend on the task: retrieval-augmented QA is bound by local CPU and a ~98 GB in-memory index, web search is dominated by waiting on remote APIs, and coding mixes LLM waits, disk I/O, and CPU. Consequently, faster LLM responses or extra CPU cores only help when local tool execution is on the critical path.
How much does the paper's optimization improve latency?
Limiting concurrent tool executions to the number of CPU cores (CPU-aware admission) cuts HotpotQA latency 3.23× on average but slows SWE-bench 2.76×. Assigning cores by task sensitivity in a mixed workload reduces average latency 32% with a 6-core budget and 23% with 12 cores; the CPU-sensitive HotpotQA improves up to 5.4×.
Why can adding CPU cores fail to speed up AI agents?
Agent latency is a mix of remote LLM/API waiting and local tool execution. Extra cores only shorten the local part. In the paper's sensitivity analysis, raising web-search containers from 2 to 8 cores improved latency by at most ~1.04× because most of that task's latency is remote waiting, while RAQA gained 13× under the same change because it is CPU-bound.
Is code or data available for this paper?
The paper does not state any code or trajectory release. The benchmarks (HotpotQA, TriviaQA, BFCLv4, QASC, SWE-bench, BigCodeBench, APPS) are public and the setup (24-vCPU Azure VM, Gemini 3 Flash, cgroup instrumentation) is described, but exact reproduction requires the recorded LLM trajectories, which are not published.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures arrive as duplicated ASCII axis-label fragments; numeric readings of Figs. 2-3 rely on prose statements rather than direct plot inspection.
- Fig. 4 heatmap values appear as raw number grids; the mapping of values to (LLM scale x cores) cells was inferred from prose order and could be misaligned for individual cells.
- The extracted text appears complete through the references, but IEEE page layout and any supplementary material beyond the arXiv v1 text could not be checked.
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