Agentic Societies Need a Social Harness
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions
In the modern world, our cities and societies face several technological and societal challenges, such as rapid urbanization, global warming & climate change, the digital divide, and social inequalities, increasing the n…
ManagementAgentic AI and the next intelligence explosion
The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, social, and relational. Recent advances in agentic AI reveal that frontier r…
DAR: Deontic Reasoning with Agentic Harnesses
Deontic reasoning is the task of answering questions by applying explicit rules and policies to case-specific facts, for example computing tax liability under a statute or determining the outcome of an immigration appeal…
Agentopia: Long-Term Life Simulation and Learning in Agent Societies
Humans learn from social life. Simulating this process with LLM-powered agents represents a promising research direction, raising a natural question: whether LLMs can learn from such simulated social experience to better…
BotSim: Mitigating The Formation Of Conspiratorial Societies with Useful Bots
Societies can become a conspiratorial society where there is a majority of humans that believe, and therefore spread, conspiracy theories. Artificial intelligence gave rise to social media bots that can spread conspiraci…