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AOS-Bench: Can LLM Agents Manage Dynamic Workloads?

Summary

AOS-Bench evaluates coding agents as workload managers under shared CPU and memory limits. Isolated task success does not carry over evenly: heavy workloads cause 13-23 point drops, serial dispatch leaves 73% of tasks unclaimed, and a fixed concurrency of two comes within four points of the best agent-managed policy.

SourcearXiv AIAuthor: Erle Zhu, Genrui Zhang, Keyang Hu, Mingyu Su, Hongning Wang
AOS-Bench: Can LLM Agents Manage Dynamic Workloads?
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[Submitted on 23 Oct 2026]

Title:AOS-Bench: Can LLM Agents Manage Dynamic Workloads?

View a PDF of the paper titled AOS-Bench: Can LLM Agents Manage Dynamic Workloads?, by Erle Zhu and 4 other authors

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Abstract:Coding agents are moving from handling one request at a time to serving as persistent assistants to which users hand a stream of work: a build, a data-processing job and a bug fix may all be pending on the same machine at once. Such an agent must decide when to take on new tasks, how to delegate and overlap them, and how to stay within the machine's CPU and memory. Existing benchmarks cannot measure this, because they evaluate each task in isolation. We introduce \benchname{}, which evaluates an agent system as a workload manager: verified tasks arrive over time and must be delivered under shared CPU and memory limits, and the same tasks run one at a time serve as a per-system reference.

Across five model--harness pairs, isolated task success does not carry over evenly. On heavy workloads every system loses 13 to 23 points. On medium workloads, systems span 11 points in isolated success but 26 points in stream success, and two systems 0.6 points apart in isolation deliver 54\% and 68\% of the same tasks. Execution-policy interventions trace much of this gap to task intake: serial dispatch leaves 73\% of heavy-workload tasks unclaimed. Yet more elaborate management does not necessarily help. A fixed concurrency of two comes within four points of the best agent-managed policy on both medium and heavy workloads, and under every policy median CPU use stays below 30\% of the provisioned cores while tasks wait. These results identify workload management as a distinct capability for agents, and \benchname{} as a way to measure it.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.33178 [cs.AI]

(or arXiv:2610.33178v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.33178

arXiv-issued DOI via DataCite

Submission history

From: Erle Zhu [view email] [v1] Fri, 23 Oct 2026 18:20:48 UTC (1,064 KB)

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Key points

  • AOS-Bench treats an agent system as a workload manager with tasks arriving over time under shared CPU and memory limits.
  • On heavy workloads, every system loses 13 to 23 points compared to isolated task success.
  • Serial dispatch leaves 73% of heavy-workload tasks unclaimed; a fixed concurrency of two performs within four points of the best agent-managed policy.
  • Median CPU use stays below 30% of provisioned cores while tasks wait under all policies.

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