arXiv AI

A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics

arXiv AI
Sep 4

Adapting to Evolving Requirements: Agentic AI for Retail Supply Chain Operations

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%.

By Lei Zheng, Liping Yang, Zihao Li, Guodong Lyu, Chaik Ming Koh, Chung-Piaw Teo
arXiv AI
Aug 20

A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation

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.

By Manoj N M, Vijayakrishna S, Manjunath Srinivas, Rohit Pahan
arXiv AI
Sep 1

AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance

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...

By Dhaval Patel, Shuxin Lin, James Rayfield, Nianjun Zhou, Chathurangi Shyalika, Suryanarayana R Yarrabothula, Roman Vaculin, Natalia Martinez, Fearghal O'donncha, Jayant Kalagnanam
arXiv AI
Jul 21

Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning

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.

By Zhihao Liu, Tianyu Wang, Xi Vincent Wang, Lihui Wang
arXiv AI
Jul 14

QwenPaw-Data: Bridging Facts, Methodology, and Execution for Autonomous Enterprise Data Analytics

arXiv:2607. 11019v1 Announce Type: new Abstract: Enterprise data analysis is emerging as a distinct frontier for autonomous agents.

By Tianjing Zeng, Yuntao Hong, Zhongjun Ding, Dandan Liu, Yinan Mei, Yunxiang Su, Yiming Wang, Xiaojian Zhang, Jingyu Zhu, Junhao Zhu, Zhuowen Liang, Jiazhen Peng, Lianggui Weng, Zhihao Ding, Kerui Yi, Qifeng Wang, Rong Zhu, Bolin Ding, Liyu Mou, Jingren Zhou
arXiv AI
Jul 28

CRAFT: Learn the Schema, Execute the Plan

arXiv:2607. 22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions.

By Aakash Kolekar, Sahika Genc, Shahriar Shariat, Bunyamin Sisman, Tibor Mezi, Barbara Poblete, Shree Vandana Kachroo, Calvin Chi, Parth Parmar, Ari Singer, Prayaas Jain, Cindy Barker, Benoit Dumoulin