Value-Preserving Architectures for Agentic AI Systems

2026-09-03

AI Summary

本文探讨了通过不同架构设计（如隐私感知、分布式和守护代理架构）来支持人类核心价值，以解决在多智能体系统中保持隐私、公平性和安全性的问题。

Abstract

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.

Problem

• Agentic AI
• Multi-Agent Systems (MAS)
• Value Preservation
• Human-Centered Values
• Architectural Design

Innovation

• value-preserving architectures
• human-centered values
• federated topology
• distributed architecture
• guard-agent architecture