动态调度多智能体，让工业自动化更稳更快
DynAMO:Dynamic Asset Management Orchestration via Topological Multi-Agent Scheduling
- 分步执行+动态并行，自动识别可并行任务
- 并行调度使端到端延迟降低1.6倍，最高达1.8倍
- 适合需要高可靠、低延迟的工业智能体部署
尽管大模型驱动的智能体可实现工业资产全生命周期的端到端自动化，但实际部署仍面临延迟高、并发不稳定和安全风险。我们提出 DynAMO（动态资产调度引擎），采用计划-执行架构生成可验证的工作流图，支持顺序执行（拓扑执行）与依赖感知的并行执行。通过动态识别独立任务，DynAMO在保持结构正确性和安全性的同时，显著提升效率。在 AssetOpsBench 工业基准上的六组实验表明：并行执行使端到端延迟中位数降低1.6倍，高度并行工作流下达1.8倍。引入真实外部工具调用延迟后，延迟分解显示大模型推理与编排仍占执行时间90%以上，揭示模型推理是主要瓶颈。结构化上下文剪枝使推理延迟降低约30%，同时维持任务完成率、智能体顺序和输出质量不变，并在受控故障注入下表现出优雅退化。可复现性分析确认重复运行下执行稳定，且并行调度降低了延迟方差。这些结果确立了 DynAMO 在工业4.0自动化流水线中可扩展、安全、低延迟部署的实用蓝图。
原文摘要 · Abstract (English)
While LLM-powered agents offer end-to-end automation for industrial asset lifecycles, real-world Industry 4.0 deployment is hindered by latency, concurrency instability, and safety risks. We present DynAMO (Dynamic Asset Management Orchestration), a deployment-ready engine using a Plan-then-Execute architecture to generate verifiable workflow graphs. DynAMO supports both SequentialWorkflow (topological execution) and ParallelWorkflow (dependency-aware concurrency). By dynamically identifying independent tasks, DynAMO preserves structural correctness and safety while significantly improving efficiency through controlled reasoning overlap. Across six controlled experiments on the AssetOpsBench industrial benchmark, DynAMO demonstrates substantial performance and robustness gains. Parallel execution reduces end-to-end latency by a median of 1.6x over sequential orchestration, rising to 1.8x on highly parallelizable workflows. After instrumenting external tool calls with realistic latencies, a latency decomposition shows that LLM reasoning and orchestration still account for more than 90% of execution time, identifying model inference as the primary system bottleneck. Structured context pruning reduces inference latency by approximately 30%, and DynAMO maintains correct functional behaviour (task completion, agent sequencing, and output quality) while exhibiting graceful degradation under controlled fault injection. Reproducibility analysis further confirms stable execution under repeated runs, with parallel scheduling reducing latency variance. These findings establish DynAMO as a practical blueprint for scalable, safe, and latency-aware agent deployment in Industry 4.0 automation pipelines. Code is available at: https://github.com/kushwaha001/DynAMO
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。