A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics
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arXiv:2609.37658v1 Announce Type: new Abstract: LLM agents are increasingly expected to support enterprise workflows, where tasks often involve missing information, uncertainty, feedback, and long-te...
The paper presents a graph‑constrained agentic framework that enables large language models to adapt retail supply‑chain decision modules to evolving requirements. It jointly selects intervention routes and admissible module changes, validating candidates against downstream KPIs. Experiments with 100 warehouse requirements and three LLMs show the framework improves end‑to‑end success from 72–76% to 79–83%.
arXiv:2608. 14246v1 Announce Type: new Abstract: In today's fast-paced environment, the ability to swiftly access, understand, and act on data is no longer optional; it is essential.
arXiv:2608. 03339v1 Announce Type: new Abstract: Enterprise forecasting increasingly relies on autonomous agents that interpret documents, search for data, generate code, and revise models.
We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chain networks. The framework enables end-to-end exec...
arXiv:2610.17206v1 Announce Type: new Abstract: We propose AutoMARS (Automatic Multi-Agent Risk Assessment), an agentic framework for automatic and scalable comparative risk assessment in supply chai...