Can We Govern the Agent-to-Agent Economy?
- URL: http://arxiv.org/abs/2501.16606v2
- Date: Fri, 25 Apr 2025 17:21:28 GMT
- Title: Can We Govern the Agent-to-Agent Economy?
- Authors: Tomer Jordi Chaffer,
- Abstract summary: Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks.<n>We highlight emerging concepts in the industry to inform research and development efforts in anticipation of a future decentralized agentic economy.
- 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. 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 humans ensure meaningful oversight and control as a future economy of AI agents scales and evolves? In this philosophical exploration, we highlight emerging concepts in the industry to inform research and development efforts in anticipation of a future decentralized agentic economy.
Related papers
- Agentic Web: Weaving the Next Web with AI Agents [109.13815627467514]
The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web.<n>In this paradigm, agents interact directly with one another to plan, coordinate, and execute complex tasks on behalf of users.<n>We present a structured framework for understanding and building the Agentic Web.
arXiv Detail & Related papers (2025-07-28T17:58:12Z) - Ten Principles of AI Agent Economics [34.771189554393096]
AI agents are evolving from specialized tools into dynamic participants in social and economic ecosystems.<n>Their autonomy and decision-making capabilities are poised to impact industries, professions, and human lives profoundly.<n>This paper presents ten principles of AI agent economics, offering a framework to understand how AI agents make decisions, influence social interactions, and participate in the broader economy.
arXiv Detail & Related papers (2025-05-26T17:52:44Z) - Trustless Autonomy: Understanding Motivations, Benefits and Governance Dilemma in Self-Sovereign Decentralized AI Agents [14.287042083260204]
Recent trend of self-sovereign Decentralized AI Agents (DeAgents) combines Large Language Model (LLM)-based AI agents with decentralization technologies such as blockchain smart contracts and trusted execution environments (TEEs)<n>DeAgent eliminates centralized control and reduces human intervention, addressing key trust concerns inherent in centralized AI systems.<n>This study addresses this empirical research gap through interviews with DeAgents stakeholders-experts, founders, and developers-to examine their motivations, benefits, and governance dilemmas.
arXiv Detail & Related papers (2025-05-14T19:42:43Z) - 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.