AgentReputation: A Decentralized Agentic AI Reputation Framework
Mohd Sameen Chishti, Damilare Peter Oyinloye, Jingyue Li

TL;DR
AgentReputation introduces a decentralized reputation framework for agentic AI systems, addressing key challenges like strategic manipulation, context transfer, and verification variability in AI marketplaces.
Contribution
It proposes a novel three-layer reputation framework with explicit verification, context-conditioned reputation cards, and a policy engine for resource and access management.
Findings
Framework separates execution, reputation, and persistence layers.
Introduces verification regimes linked to reputation metadata.
Supports adaptive verification and resource allocation.
Abstract
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. 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. 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. We therefore propose \textbf{AgentReputation}, a decentralized, three-layer reputation framework for agentic AI systems. The framework separates task execution,…
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