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...
The paper introduces a multi‑agent platform built on CrewAI for conversational business intelligence. Five specialized agents process natural language queries, retrieve and analyze data, generate visualizations via the Model Context Protocol, and deliver actionable insights. The system includes a defense‑in‑depth security architecture, a query parameterization mechanism, and achieves 95.3% functional accuracy with a 24‑second mean latency, outperforming a single‑agent baseline by 22.6 percentage points in accuracy and 20.2% in quality.
arXiv:2609.01045v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities as powerful components in agentic systems, enabling sophisticated reasoning and...
arXiv:2506.03828v4 Announce Type: replace Abstract: AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows, such as condition monitoring and maintenance schedulin...
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.
arXiv:2508. 05002v2 Announce Type: replace-cross Abstract: Existing unstructured data analytics systems rely on experts to write code and manage complex analysis workflows, making them both expensive and time-consuming.
arXiv:2604. 17220v2 Announce Type: replace-cross Abstract: Modeling coordination among generative agents in complex multi-round decision-making presents a core challenge for AI and operations management.