Safety and Risk Pathways in Cooperative Generative Multi-Agent Systems: A Telecom Perspective

TL;DR

This paper explores safety challenges in telecom-focused Generative Multi-Agent Systems, proposing a modular evaluation framework and analyzing how persona design impacts system stability and safety.

Contribution

It introduces a novel safety evaluation framework for telecom GMAS and provides empirical insights into how persona diversity affects safety and risk.

Findings

Progressive improvements in safety metrics through iterative testing

Persistent vulnerabilities like policy drift under certain personas

Persona design significantly influences system safety and stability

Abstract

Generative multiagent systems are rapidly emerging as transformative tools for scalable automation and adaptive decisionmaking in telecommunications. Despite their promise, these systems introduce novel risks that remain underexplored, particularly when agents operate asynchronously across layered architectures. This paper investigates key safety pathways in telecomfocused Generative MultiAgent Systems (GMAS), emphasizing risks of miscoordination and semantic drift shaped by persona diversity. We propose a modular safety evaluation framework that integrates agentlevel checks on code quality and compliance with systemlevel safety metrics. Using controlled simulations across 32 persona sets, five questions, and multiple iterative runs, we demonstrate progressive improvements in analyzer penalties and AllocatorCoder consistency, alongside persistent vulnerabilities such as policy drift and variability under specific persona combinations. Our findings provide the first domaingrounded evidence that persona design, coding style, and planning orientation directly influence the stability and safety of telecom GMAS, highlighting both promising mitigation strategies and open risks for future deployment.