EnterpriseAI: A Multi-Agent Autonomous System for Industrial Supervision and Trade Compliance with RAG-Based Regulatory Grounding
ID:17
Submission ID:170 View Protection:ATTENDEE
Updated Time:2026-07-22 16:09:05 Hits:24
Online
Start Time:2026-07-30 12:10 (Asia/Kolkata)
Duration:15min
Session:[S1] 5G and beyond Wireless Networks » [S1-1] 5G and beyond Wireless Networks
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Abstract
As industrial operations and international trade regulations get more complicated the need for independent supervision systems becomes really important. The old ways of supervising things have a problem. They lose a lot of information when workers change shifts, which causes 30 to 50 percent of safety problems. Global trade compliance teams also have a time keeping up with changing sanctions, export controls and classification rules. This paper talks about EnterpriseAI a system that uses agents to work independently and address gaps, in industrial operations and global export compliance. It uses a supervisor pattern and works with eight special AI agents using LangChain and LangGraph. The system makes sure it follows the rules by using a Retrieval-Augmented Generation pipeline that looks at regulatory texts. One of the new ideas is a governance engine that enforces eleven safety and compliance rules in real time. EnterpriseAI works locally using the LLaMA-3 model via Ollama. It provides a secure system that people can work with which helps make things safer predicts equipment problems and makes sure we follow international trade regulations without sharing sensitive information.
Keywords
Multi-Agent Systems, Industrial AI, Retrieval Augmented Generation, Trade Compliance, LLM Governance, Autonomous Supervision, LangGraph, ChromaDB.
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