BeautyGuard: Designing a Multi-Agent Roundtable System for Proactive Beauty Tech Compliance through Stakeholder Collaboration

Abstract

As generative AI enters enterprise workflows, ensuring compliance with legal, ethical, and reputational standards becomes a pressing challenge. In beauty tech, where biometric and personal data are central, traditional reviews are often manual, fragmented, and reactive. To examine these challenges, we conducted a formative study with six experts (four IT managers, two legal managers) at a multinational beauty company. The study revealed pain points in rule checking, precedent use, and the lack of proactive guidance.Motivated by these findings, we designed a multi-agent “roundtable” system powered by a large language model. The system assigns role-specialized agents for legal interpretation, checklist review, precedent search, and risk mitigation, synthesizing their perspectives into structured compliance advice.We evaluated the prototype with the same experts using System Usability Scale(SUS), The Official NASA Task Load Index(NASA-TLX), and interviews. Results show exceptional usability (SUS:77.5/100) and minimal cognitive workload, with three key findings:(1) multi-agent systems can preserve tacit knowledge into standardized workflows, (2) information augmentation achieves higher acceptance than decision automation, and (3) successful enterprise AI should mirror organizational structures. This work contributes design principles for human-AI collaboration in compliance review, with broader implications for regulated industries beyond beauty tech.

Junwei Li

Research Fellow

My research interests include Multi-Agent LLM Systems, RAG/Graph-RAG, Educational Technology and compliance-aware AI companions for cloud–edge education and assistive use.