AgentReputation: A Decentralized Agentic AI Reputation Framework

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, reputation services, and tamper-proof persistence to both leverage their respective strengths and enable independent evolution. 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. In addition, AgentReputation provides a decision-facing policy engine that supports resource allocation, access control, and adaptive verification escalation based on risk and uncertainty. 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.