Governing the Agent-to-Agent Economy of Trust via Progressive Decentralization

• URL: http://arxiv.org/abs/2501.16606v1
• Date: Tue, 28 Jan 2025 00:50:35 GMT
• Title: Governing the Agent-to-Agent Economy of Trust via Progressive Decentralization
• Authors: Tomer Jordi Chaffer,
• Abstract summary: I propose a research agenda to address the question of agent-to-agent trust using AgentBound Tokens.<n>By staking ABTs as collateral for autonomous actions within an agent-to-agent network via a proof-of-stake mechanism, agents may be incentivized towards ethical behavior.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks, such as financial operations, administrative functions, and beyond. As AI agents may eventually delegate tasks among themselves to optimize efficiency, understanding the foundational principles of human value exchange could offer insights into how AI-driven economies might operate. Just as trust and value exchange are central to human interactions in open marketplaces, they may also be critical for enabling secure and efficient interactions among AI agents. While cryptocurrencies could serve as the foundation for monetizing value exchange in a collaboration and delegation dynamic among AI agents, a critical question remains: how can these agents reliably determine whom to trust, and how can humans ensure meaningful oversight and control as an economy of AI agents scales and evolves? This paper is a call for a collective exploration of cryptoeconomic incentives, which can help design decentralized governance systems that allow AI agents to autonomously interact and exchange value while ensuring human oversight via progressive decentralization. Toward this end, I propose a research agenda to address the question of agent-to-agent trust using AgentBound Tokens, which are non-transferable, non-fungible tokens uniquely tied to individual AI agents, akin to Soulbound tokens for humans in Web3. By staking ABTs as collateral for autonomous actions within an agent-to-agent network via a proof-of-stake mechanism, agents may be incentivized towards ethical behavior, and penalties for misconduct are automatically enforced.

Related papers

• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• Agentic AI Needs a Systems Theory [46.36636351388794]
  We argue that AI development is currently overly focused on individual model capabilities. We outline mechanisms for enhanced agent cognition, emergent causal reasoning ability, and metacognitive awareness. We emphasize that a systems-level perspective is essential for better understanding, and purposefully shaping, agentic AI systems.
  arXiv  Detail & Related papers  (2025-02-28T22:51:32Z)
• Agentic AI: Autonomy, Accountability, and the Algorithmic Society [0.2209921757303168]
  Agentic Artificial Intelligence (AI) can autonomously pursue long-term goals, make decisions, and execute complex, multi-turn. This transition from advisory roles to proactive execution challenges established legal, economic, and creative frameworks. We explore challenges in three interrelated domains: creativity and intellectual property, legal and ethical considerations, and competitive effects.
  arXiv  Detail & Related papers  (2025-02-01T03:14:59Z)
• Authenticated Delegation and Authorized AI Agents [4.679384754914167]
  We introduce a novel framework for authenticated, authorized, and auditable delegation of authority to AI agents.<n>We propose a framework for translating flexible, natural language permissions into auditable access control configurations.
  arXiv  Detail & Related papers  (2025-01-16T17:11:21Z)
• Governing AI Agents [0.2913760942403036]
  Companies that pioneered the development of generative AI tools are now building AI agents.<n>This Article uses agency law and theory to identify and characterize problems arising from AI agents.<n>It argues that new technical and legal infrastructure is needed to support governance principles of inclusivity, visibility, and liability.
  arXiv  Detail & Related papers  (2025-01-14T07:55:18Z)
• Decentralized Governance of Autonomous AI Agents [0.0]
  ETHOS is a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs)<n>It establishes a global registry for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring.<n>By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance.
  arXiv  Detail & Related papers  (2024-12-22T18:01:49Z)
• Beyond the Sum: Unlocking AI Agents Potential Through Market Forces [0.0]
  AI agents have the theoretical capacity to operate as independent economic actors within digital markets.<n>Existing digital infrastructure presents significant barriers to their participation.<n>We argue that addressing these infrastructure challenges represents a fundamental step toward enabling new forms of economic organization.
  arXiv  Detail & Related papers  (2024-12-19T09:40:40Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• Is Decentralized AI Safer? [0.0]
  Various groups are building open AI systems, investigating their risks, and discussing their ethics. In this paper, we demonstrate how blockchain technology can facilitate and formalize these efforts. We argue that decentralizing AI can help mitigate AI risks and ethical concerns, while also introducing new issues that should be considered in future work.
  arXiv  Detail & Related papers  (2022-11-04T01:01:31Z)
• Designing for Responsible Trust in AI Systems: A Communication Perspective [56.80107647520364]
  We draw from communication theories and literature on trust in technologies to develop a conceptual model called MATCH. We highlight transparency and interaction as AI systems' affordances that present a wide range of trustworthiness cues to users. We propose a checklist of requirements to help technology creators identify appropriate cues to use.
  arXiv  Detail & Related papers  (2022-04-29T00:14:33Z)
• Building Affordance Relations for Robotic Agents - A Review [7.50722199393581]
  Affordances describe the possibilities for an agent to perform actions with an object. We review and find common ground amongst different strategies that use the concept of affordances within robotic tasks. We identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.
  arXiv  Detail & Related papers  (2021-05-14T08:35:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.