分析了超1900个DeFi投资代理，发现多数仍为简单API集成，实际收益严重分化。
Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents
- 筛选10个代表性代理，深入分析架构与链上表现
- 11个Solana代理基金共持有92.5万持币人，纸面盈利3000万但集体亏损1.917亿
- 顶级1%钱包攫取81.4%收益，市场估值远高于实际资产，适合关注早期创新者
自2024年底以来，基于AI的DeFi投资代理累计市值已超30亿美元。我们调查了超过1900个标注为AI的加密项目，筛选出以投资为核心的目标代理，并精选10个代表性项目，涵盖策略与可观测性维度。进一步对两大主流代理框架ElizaOS与Virtuals Protocol进行深度架构分析，并对11个在Solana链上具有公开交易记录的代理基金进行量化链上性能分析，覆盖925,323名持币人。研究发现当前部署仍处早期且高度异质：（1）样本中许多项目未展示明确自主交易执行证据，开发者访谈显示多数可见部署仍为基础API集成；（2）代理基金虽保留超3000万美元纸面收益，但持币人集体亏损1.917亿美元，前1%钱包占据81.4%总收益（18.1亿美元）；（3）代币估值与基金基本面关联极弱，市值/管理资产比率达10,000倍以上，而成熟DeFi协议低于1倍；（4）整体用户收益峰值达24亿美元后转为净亏损，各平台中位回报均为负值，代币平均较历史高点下跌93%。我们将其解读为一个无许可、第一代市场的典型特征——开放基础设施促进快速实验，但也让非理性或投机型代理提前上线，缺乏自主性、绩效与利益对齐的成熟标准。为此，我们提出包含自主执行、风险调整后盈利、利益一致性三个维度的成熟度框架，用以刻画当前部署与未来投资级代理系统之间的差距。
原文摘要 · Abstract (English)
DeFi investment agents, systems that use AI for autonomous on-chain trading, have attained over USD 3 billion in combined token valuations since late 2024. We survey over 1,900 AI-tagged crypto projects, filter to investment-focused agents, and curate 10 representative projects spanning strategy and observability dimensions. We then conduct a deep-dive architectural analysis of two prominent agent frameworks, ElizaOS and Virtuals Protocol, and a quantitative on-chain performance analysis of 11 Solana-based agent treasuries with publicly attributable trading activity, covering 925,323 token holders. We find that current deployments remain early and heterogeneous: (1) in our sample, many projects do not yet provide clear evidence of autonomous trade execution, and developer interviews suggest that many visible deployments remain basic API integrations; (2) agent treasuries retain over USD 30M in paper gains while token holders collectively lost USD 191.7M, with the top 1% of wallets capturing 81.4% of all gains (USD 1.81B); (3) token valuations are weakly connected to treasury fundamentals, with market-cap-to-AUM ratios exceeding 10,000x versus below 1x for established DeFi protocols; and (4) aggregate user gains peaked at USD 2.4B before declining to net losses, with median returns negative on every platform and tokens declining 93% on average from all-time highs. We interpret these outcomes as characteristic of a permissionless, first-generation market in which open infrastructure enables rapid experimentation but also allows naive or speculative agents to launch before robust standards for autonomy, performance, and stakeholder alignment emerge. We therefore propose a maturity framework along three dimensions: autonomous execution, risk-adjusted profitability, and stakeholder alignment, to characterize the gap between current deployments and future investment-grade agent systems.
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