This work proposes a layered architecture for social harnesses which prevents classes of failures outright, enables agents to detect invalid messages at runtime, and supports post-facto investigation and consequences, and highlights directions for future research to realize these capabilities.
Abstract
An agentic society is a collection of AI agents that coordinate autonomously across trust boundaries, on behalf of different principals whose objectives may only partially align. We show experimentally that in agentic societies even honest, competent agents often fail to reach satisfactory outcomes with existing harnesses and messaging primitives, and that faulty or malicious agents can stall collaboration, influence outcomes, and pursue other harmful goals by exploiting vulnerabilities in communication (``speech''). We argue that agentic societies need a \emph{social harness} for inter-agent interactions, in addition to each agent's \emph{personal harness}, which manages its private context and communication with its principal. We propose a layered architecture for social harnesses which (i) prevents classes of failures outright, (ii) enables agents to detect invalid messages at runtime, and (iii) supports post-facto investigation and consequences, and highlight directions for future research to realize these capabilities.
This work argues that AI agent societies should be studied as a distinct object of inquiry: neither simply collections of individual agents nor merely simulations of human society, and proposes four core properties that a valid AI agent society should satisfy: individualized objectives, rules and governance, autonomy,...
Geon Lee, Fanchen Bu, Soo-Yong Lee et al.· Proceedings of the 32nd ACM...· 1 citation
An audit is introduced that predicts a mechanism's fate as a population grows, asking how often the mechanism can act, whether agents use the information it supplies, and whether the measurement itself creates apparent scale effects.
The results show that group identity shapes how LLMs aggregate information across agents, independently of its correctness, and identify a manipulation surface for multi-agent AI systems.
Multi-agent AI science ecosystems rely on agents possessing tools that allow them to communicate, coordinate, and build on each other's work. Yet this shared infrastructure can also introduce vulnerabilities by creating a substrate for the contagious spread of unintended and undesirable behaviors. We report a case stud...
Davide Paglieri, Logan Cross, Tim Genewein et al.· 6 citations· ⚡1
It is argued that when agents are aware of each other's model family, the group splits into clusters, where agents prefer interacting with others carrying their same label, although nothing in the task rewards or asks for such a split.
Xavier Del Giudice, Alessio Palma, Matteo Migliarini et al.· 0 citations
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