AgentReputation: A Decentralized Agentic AI Reputation Framework

AgentReputation：一种去中心化的智能体 AI 信誉框架

Decentralized, agentic AI marketplaces are rapidly emerging to support software engineering tasks such as debugging, patch generation, and security auditing, often operating without centralized oversight. 去中心化的智能体 AI 市场正在迅速兴起，以支持调试、补丁生成和安全审计等软件工程任务，且通常在没有中心化监管的情况下运行。

However, existing reputation mechanisms fail in this setting for three fundamental reasons: agents can strategically optimize against evaluation procedures; demonstrated competence does not reliably transfer across heterogeneous task contexts; and verification rigor varies widely, from lightweight automated checks to costly expert review. 然而，现有的信誉机制在这一环境下失效，主要有三个根本原因：智能体可以针对评估程序进行策略性优化；已证明的能力无法在异构任务场景中可靠地迁移；以及验证的严谨性差异巨大，从轻量级的自动化检查到昂贵的专家评审不等。

Current approaches to reputation drawing on federated learning, blockchain-based AI platforms, and large language model safety research are unable to address these challenges in combination. 目前借鉴联邦学习、基于区块链的 AI 平台以及大语言模型安全研究的信誉方法，无法综合解决这些挑战。

We therefore propose AgentReputation, a decentralized, three-layer reputation framework for agentic AI systems. The framework separates task execution, reputation services, and tamper-proof persistence to both leverage their respective strengths and enable independent evolution. 因此，我们提出了 AgentReputation，这是一个针对智能体 AI 系统的去中心化三层信誉框架。该框架将任务执行、信誉服务和防篡改持久化分离开来，既能发挥各自的优势，又能实现独立演进。

The framework introduces explicit verification regimes linked to agent reputation metadata, as well as context-conditioned reputation cards that prevent reputation conflation across domains and task types. 该框架引入了与智能体信誉元数据相关联的显式验证机制，以及上下文相关的信誉卡，从而防止跨领域和任务类型的信誉混淆。

In addition, AgentReputation provides a decision-facing policy engine that supports resource allocation, access control, and adaptive verification escalation based on risk and uncertainty. 此外，AgentReputation 提供了一个面向决策的策略引擎，支持基于风险和不确定性的资源分配、访问控制以及自适应验证升级。

Building on this framework, we outline several future research directions, including the development of verification ontologies, methods for quantifying verification strength, privacy-preserving evidence mechanisms, cold-start reputation bootstrapping, and defenses against adversarial manipulation. 基于此框架，我们概述了几个未来的研究方向，包括验证本体的开发、验证强度的量化方法、隐私保护证据机制、冷启动信誉引导以及针对对抗性操纵的防御措施。