arXiv cs.AI· Subhrangshu Nandi, Hitesh Arora, Yunfei Bai, Udita Patel, Chirag Shah, Hannah Kwon, Wei Zhang, Weian Chen, Chloe Ruoqi Li, Alberto Sconfienza, Ayush Goyal, Yasmine Lee, Ming Xiong, Prathap Ramachandra·· 10 小时前AI 评分58
研究对比四种智能体架构在企业 SOP 上的成本与成功率权衡
Agent Architecture Cost-Success Trade-offs: A Controlled Evaluation across Enterprise-Style SOPs
AI 导读
一项 arXiv 论文在固定模型、任务和工具、只改变架构的条件下，对比了函数调用单智能体、技能增强智能体、集中式多智能体编排和分流式 swarm 四种设计（共八个实验组），覆盖 13 个企业 SOP。
正文 · 原文
Authors:Subhrangshu Nandi, Hitesh Arora, Yunfei Bai, Udita Patel, Chirag Shah, Hannah Kwon, Wei Zhang, Weian Chen, Chloe Ruoqi Li, Alberto Sconfienza, Ayush Goyal, Yasmine Lee, Ming Xiong, Prathap Ramachandra
Abstract:Enterprises increasingly use large language model agents to execute standard operating procedures (SOPs): long, branching, tool-intensive workflows. Yet there is little controlled evidence for choosing among competing agentic architectures. Holding the model, tasks, and tools fixed and varying only the architecture, we compare four designs (a function-calling single agent, a skill-augmented agent, centralized multi-agent orchestration, and a triage swarm; eight arms) across 13 enterprise SOPs, and introduce a skill-definition quality rubric to build matched high and low-quality skill corpora. Three takeaways emerge. (1)~\textbf{There is no universal best architecture; the right choice is SOP-dependent.} On a given SOP, task success ranges from under $10\%$ on the hardest to near $100\%$ on the easiest; no architecture is statistically separable in aggregate, and added structure only plausibly pays off on complex, branching SOPs (a directional, not yet significant, trend). (2)~\textbf{Added architecture cost must be justified by a task-success gain.} Every coordinated arm (multi-agent or skill-augmented) spends $1.4$--$4.7\times$ the tokens and $2.6$--$5.8\times$ the latency of the single agent for gains that are usually small and rarely significant, making the single agent the efficient default; high-quality skills do lift success (to $73.1\%$ from $67.4\%$) while degraded skills fall below it. (3)~\textbf{Implementation quality outweighs the single- versus multi-agent choice.} A plausible multi-agent implementation loses to the single agent, yet a targeted redesign of it becomes the only arm to significantly beat the baseline ($75.6\%$ vs.\ $67.4\%$).
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.12953 [cs.AI] |
| (or arXiv:2611.12953v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12953 arXiv-issued DOI via DataCite |
Submission history
From: Hitesh Arora [view email]
[v1]
Sat, 7 Nov 2026 05:12:41 UTC (1,600 KB)
来源：arXiv cs.AI · arxiv.org