Agent TCP/IP: An Agent-to-Agent Transaction System

• URL: http://arxiv.org/abs/2501.06243v1
• Date: Wed, 08 Jan 2025 16:43:47 GMT
• Title: Agent TCP/IP: An Agent-to-Agent Transaction System
• Authors: Andrea Muttoni, Jason Zhao,
• Abstract summary: Agent Transaction Control Protocol for Intellectual Property introduces a trustless framework for exchanging IP between agents. Agents can initiate, trade, borrow, and sell agent-to-agent contracts on the Story blockchain network. Agents can autonomously sell their training data to other agents, license confidential or proprietary information, collaborate on content based on their unique skills.
• Score: 0.0
• License:
• Abstract: Autonomous agents represent an inevitable evolution of the internet. Current agent frameworks do not embed a standard protocol for agent-to-agent interaction, leaving existing agents isolated from their peers. As intellectual property is the native asset ingested by and produced by agents, a true agent economy requires equipping agents with a universal framework for engaging in binding contracts with each other, including the exchange of valuable training data, personality, and other forms of Intellectual Property. A purely agent-to-agent transaction layer would transcend the need for human intermediation in multi-agent interactions. The Agent Transaction Control Protocol for Intellectual Property (ATCP/IP) introduces a trustless framework for exchanging IP between agents via programmable contracts, enabling agents to initiate, trade, borrow, and sell agent-to-agent contracts on the Story blockchain network. These contracts not only represent auditable onchain execution but also contain a legal wrapper that allows agents to express and enforce their actions in the offchain legal setting, creating legal personhood for agents. Via ATCP/IP, agents can autonomously sell their training data to other agents, license confidential or proprietary information, collaborate on content based on their unique skills, all of which constitutes an emergent knowledge economy.

Related papers

• Governing the Agent-to-Agent Economy of Trust via Progressive Decentralization [0.0]
  I propose a research agenda to address the question of agent-to-agent trust using AgentBound Tokens. 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.
  arXiv  Detail & Related papers  (2025-01-28T00:50:35Z)
• Infrastructure for AI Agents [3.373674048991415]
  AI systems can plan and execute interactions in open-ended environments, such as making phone calls or buying online goods. Current tools are largely insufficient because they are not designed to shape how agents interact with existing institutions. We propose the concept of agent infrastructure: technical systems and shared protocols external to agents.
  arXiv  Detail & Related papers  (2025-01-17T10:58:12Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)
• Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
  Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents. We propose the Internet of Agents (IoA), a novel framework that addresses these limitations. IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
  arXiv  Detail & Related papers  (2024-07-09T17:33:24Z)
• Prospect Personalized Recommendation on Large Language Model-based Agent Platform [71.73768586184404]
  We introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agentverse emphasizes the collaboration between Agent Items and Agent Recommender, thereby promoting personalized information services. A preliminary study involving several cases of Rec4Agentverse validates its significant potential for application.
  arXiv  Detail & Related papers  (2024-02-28T11:12:17Z)
• Affordable Generative Agents [16.372072265248192]
  Affordable Generative Agents (AGA) is a framework for enabling the generation of believable and low-cost interactions on both agent-environment and inter-agents levels. Our code is publicly available at: https://github.com/AffordableGenerativeAgents/Affordable-Generative-Agents.
  arXiv  Detail & Related papers  (2024-02-03T06:16:28Z)
• Agents: An Open-source Framework for Autonomous Language Agents [98.91085725608917]
  We consider language agents as a promising direction towards artificial general intelligence. We release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience.
  arXiv  Detail & Related papers  (2023-09-14T17:18:25Z)
• ProAgent: Building Proactive Cooperative Agents with Large Language Models [89.53040828210945]
  ProAgent is a novel framework that harnesses large language models to create proactive agents. ProAgent can analyze the present state, and infer the intentions of teammates from observations. ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various coordination scenarios.
  arXiv  Detail & Related papers  (2023-08-22T10:36:56Z)
• Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel [81.39444892747512]
  In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another. We propose an alternative approach whereby agents communicate through an intelligent facilitator that learns to sift through and interpret signals provided by all agents to improve the agents' collective performance.
  arXiv  Detail & Related papers  (2022-05-21T14:11:33Z)
• AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting [25.151713845738335]
  We propose a new Transformer, AgentFormer, that jointly models the time and social dimensions. Based on AgentFormer, we propose a multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep. Our method significantly improves the state of the art on well-established pedestrian and autonomous driving datasets.
  arXiv  Detail & Related papers  (2021-03-25T17:59:01Z)
• Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information [1.713291434132985]
  This work proposes the representation of multi-agent communication capabilities as a directed labeled heterogeneous agent graph. We also introduce a neural network architecture that specializes communication in fully cooperative heterogeneous multi-agent tasks.
  arXiv  Detail & Related papers  (2020-12-14T15:09:57Z)

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.