Trustless Autonomy: Understanding Motivations, Benefits and Governance Dilemma in Self-Sovereign Decentralized AI Agents

• URL: http://arxiv.org/abs/2505.09757v1
• Date: Wed, 14 May 2025 19:42:43 GMT
• Title: Trustless Autonomy: Understanding Motivations, Benefits and Governance Dilemma in Self-Sovereign Decentralized AI Agents
• Authors: Botao Amber Hu, Yuhan Liu, Helena Rong,
• Abstract summary: Recent trend of self-sovereign Decentralized AI Agents (DeAgents) combines Large Language Model (LLM)-based AI agents with decentralization technologies such as blockchain smart contracts and trusted execution environments (TEEs)<n>DeAgent eliminates centralized control and reduces human intervention, addressing key trust concerns inherent in centralized AI systems.<n>This study addresses this empirical research gap through interviews with DeAgents stakeholders-experts, founders, and developers-to examine their motivations, benefits, and governance dilemmas.
• Score: 14.287042083260204
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The recent trend of self-sovereign Decentralized AI Agents (DeAgents) combines Large Language Model (LLM)-based AI agents with decentralization technologies such as blockchain smart contracts and trusted execution environments (TEEs). These tamper-resistant trustless substrates allow agents to achieve self-sovereignty through ownership of cryptowallet private keys and control of digital assets and social media accounts. DeAgent eliminates centralized control and reduces human intervention, addressing key trust concerns inherent in centralized AI systems. However, given ongoing challenges in LLM reliability such as hallucinations, this creates paradoxical tension between trustlessness and unreliable autonomy. This study addresses this empirical research gap through interviews with DeAgents stakeholders-experts, founders, and developers-to examine their motivations, benefits, and governance dilemmas. The findings will guide future DeAgents system and protocol design and inform discussions about governance in sociotechnical AI systems in the future agentic web.

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