Value-Preserving Architectures for Agentic AI Systems

The paper "Value-Preserving Architectures for Agentic AI Systems" discusses how architectural choices in large language model-based multi‑agent systems (MAS) can promote human‑centered values such as privacy, fairness, and safety. It introduces three value‑preserving architectural patterns: a privacy‑aware federated topology, a distributed architecture that encourages pluralism and diversity, and a guard‑agent design to detect and mitigate unfairness. Representative use cases illustrate how these patterns can be applied in real‑world scenarios, aiming to provide guidelines for building trustworthy MAS.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• agents
• safety