[2605.00073] AgentReputation: A Decentralized Agentic AI Reputation Framework
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Computer Science > Artificial Intelligence
arXiv:2605.00073 (cs)
[Submitted on 30 Apr 2026]
Title:AgentReputation: A Decentralized Agentic AI Reputation Framework
Authors:Mohd Sameen Chishti, Damilare Peter Oyinloye, Jingyue Li
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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.
Comments: 5 pages, 1 figure, accepted to FSE 2026, Ideas, Visions and Reflections track
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.00073 [cs.AI]
(or arXiv:2605.00073v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.00073
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arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3803437.3805579
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DOI(s) linking to related resources
Submission history
From: Jingyue Li Prof. [view email] [v1] Thu, 30 Apr 2026 12:33:39 UTC (275 KB)
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