发表机构
TU Wien（维也纳技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对智能体AI与LLM多智能体系统的价值保留需求，提出三类价值导向架构模式，结合现实用例为可信多智能体系统设计奠定基础。
AI 中文摘要
智能体AI与基于大语言模型（LLM）的多智能体系统（MAS）的兴起，为自动化复杂任务带来了前所未有的机遇，同时也引发了关于隐私、公平性、安全性等以人类为中心的基本价值保留的关键问题。尽管软件工程传统上聚焦于功能正确性，但将LLM与AI智能体融入复杂的社会技术系统，加剧了对负责任软件工程和稳健价值对齐的需求。在MAS中，架构设计决策（如协调机制、通信协议、系统拓扑）对系统行为及产生的结果起着核心作用。本文认为，架构选择不仅影响MAS的功能与性能，还可促进面向价值的系统行为。因此，我们研究不同架构设计如何支持不同的以人类为中心的价值，探讨以下价值保留架构模式：（i）具有联邦拓扑的隐私感知架构；（ii）促进多元性与多样性的分布式架构；（iii）用于检测和缓解不公平性的守卫智能体架构。最后，我们引入代表性用例，以说明所提架构在现实场景中的应用。通过将架构设计与以人类为中心的价值相联系，本研究为构建可信MAS的统一架构模式与指南奠定了基础。
英文摘要
The emergence of agentic AI and LLM-based multi-agent systems (MAS) presents unprecedented opportunities for automating complex tasks, while simultaneously raising critical concerns about the preservation of fundamental human-centered values, such as privacy, fairness, and safety. Although software engineering has traditionally focused on functional correctness, the adoption of LLMs and AI agents into complex socio-technical systems has intensified the need for responsible software engineering and robust value alignment. In MAS, architectural design decisions, such as coordination mechanisms, communication protocols, and system topologies, play a central role in shaping system behavior and the outcomes they produce. This paper argues that architectural choices influence not only the functionality and performance of MAS but can also promote value-oriented system behavior. Therefore, we investigate how different architectural designs support different human-centered values, discussing the following value-preserving architectural patterns: (i) a privacy-aware architecture with a federated topology, (ii) a distributed architecture to promote pluralism and diversity, and (iii) a guard-agent architecture to detect and mitigate unfairness. Finally, we introduce representative use cases to illustrate the proposed architectures in real-world scenarios. By linking architectural design with human-centered values, this work lays the foundation for a unified set of architectural patterns and guidelines towards the design of trustworthy MAS.
CommentsAccepted to AgenticDev Workshop at ASE 2026
