ANASSA 智能体编排框架的空间智能供应链部署
Agentic AI Orchestration Framework for Spatial Intelligence: Supply Chain Deployment
AI 导读
论文提出 ANASSA 参考架构，将结构化推理、多智能体工作流编排、权威空间定位与反馈驱动执行整合，并报告其在供应链运营中的部署。系统在智能体边界设置类型化数据契约，在六步认知循环中强制两道人工审批关卡，覆盖海运与内河货运通道的多灾害风险评估及集装箱遥测冷链异常检测。三个月 POC 中，数据质量提升 25%，空间工作流运行时间缩短 50%，决策延迟降低 60%。
正文 · 原文
Abstract:Industrial spatial decisions are assembled across systems that share neither a reference frame nor an owner, and analysts spend substantial effort reconciling those systems rather than reasoning about space. This paper presents ANASSA, a reference architecture integrating structured reasoning, multi-agent workflow orchestration, authoritative spatial grounding, and feedback-driven execution, and reports its instantiation in an operating supply-chain deployment. The architecture places typed data contracts at agent boundaries, validates spatial claims against a certified authoritative foundation, and enforces two blocking human-in-the-loop approvals inside a six-step cognitive loop. The deployment covers multi-hazard risk assessment on an ocean-and-inland freight lane, including cold-chain excursion detection over container telemetry. Across a three-month proof of concept (POC) with three implementation iterations, three deployment outcomes are reported: 25% improvement in data quality, 50% reduction in spatial workflow runtime, and 60% lower decision latency relative to the preceding manual workflow. Absolute deployment-scale quantities and baseline counts are confidential; reported percentages are observed relative changes. These are uncontrolled deployment observations, not controlled experiments. We therefore specify instrumentation and ablation protocols for mechanism-level evaluation.
| Comments: | 10 pages, 6 figures. Accepted for publication in the proceedings of GeoIndustry 2026 (5th ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications). In-person presentation scheduled for November 3, 2026, Riverside, California. Proceedings forthcoming in the ACM Digital Library |
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Software Engineering (cs.SE) |
| ACM classes: | I.2.11; H.2.8; D.2.11 |
| Cite as: | arXiv:2610.34569 [cs.AI] |
| (or arXiv:2610.34569v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34569 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Constantinos Papantoniou [view email]
[v1]
Sun, 25 Oct 2026 21:12:21 UTC (3,613 KB)
来源：arXiv cs.AI · arxiv.org