Skip to content

Agentic Societies Need a Social Harness

Sep 2026 · 0 citations · 47 references
Computer Science

TL;DR

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.

View source

Similar papers

Book Open access Aug 2026

Toward a Science of AI Agent Societies

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. · 1 citation
#small language model Preprint Aug 2026

Predicting the scale limits of social mechanisms in agent societies

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.

Zeng-Qing Wu, Chuan Xiao · 0 citations
#artificial intelligence Preprint Sep 2026

A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms

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
#artificial intelligence Preprint Sep 2026

Prompted Identity Degrades Cooperation in Multi-Agent LLM Systems

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

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.