Organizations: School of Software Engineering, Sun Yat-sen University, Zhuhai, Guangdong, China · School of Computer Science, Peking University, Beijing, China · School of Computer Science and Engineering (School of Cyber Security), University of Electronic Science and Technology of China, Chengdu 611731, China · The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, Zhejiang, China
The convergence of Web3 technologies and AI agents is a rapidly evolving frontier that is reshaping decentralized ecosystems. This paper presents the first comprehensive landscape analysis of the intersection between Web3 and AI agents, examining five critical dimensions: landscape, economics, governance, security, and trust mechanisms. Through an analysis of 979 capitalized Web3-AI agent tokens at a 1 September 2026 cutoff, compared with a cohort of 275 tokens collected at 6 April 2025, we develop a taxonomy and systematically map the current market landscape (RQ1), identifying distinct patterns in project distribution and capitalization. We complement the token frame with on-chain measurement of the agent standards layer: 530,266 ERC-8004 identity registrations across 16 chains and 432,913 x402 settlements on Base. We then investigate four integration domains: AI agents in decentralized finance (RQ2), governance (RQ3), security (RQ4), and Web3 trust mechanisms for agent reliability (RQ5). Infrastructure dominates the landscape in both project count and capitalization; value is highly concentrated (Gini 0.938), and the archived cohort lost 36.4% in capitalization over seventeen months despite token population growth. The agent standards layer shows adoption at scale, but economic throughput remains limited. Across integration domains, AI agents extend DeFi trading and make continuous security auditing affordable, while agents in governance remain largely a blueprint. These findings outline critical considerations for future research toward robust, intelligent, and trustworthy decentralized systems with AI agent interactions.
Figures & tables
Figure 1: A Typical Architecture of Web3-AI Agents
Figure 2: Research Methodology
Figure 3: The Ecosystem Landscape of Web3-AI Agent Projects
Table 1: Consolidated Web3-AI Agent Taxonomy with Market Metrics 3 3 3 120 projects are classified under multiple subcategories due to their multifaceted nature, and their full market capitalization is attributed to each category they belong to in this analysis.
Figure 4: Listings and capitalization move in opposite directions over the eighteen months from 2025-03 to 2026-08, the last full month before the second cutoff. Bars count how many of the 855 classified projects had a live listing in each month, rising from 387 to 854. The line shows their aggregate capitalization, falling from 23.12billionto15.19 billion, drawn faint for monthly observations and bold for a centred three-month mean.
Figure 5: Lorenz curves of market capitalization at both timepoints. The 2026 curve lies almost on the axes: capitalization is now so concentrated that the poorest 90% of tokens hold 8.9% of the value, against 19.9% for the T1 cohort.
Figure 6: Project count and average market capitalization by primary category across the 855 classified projects. Counts are category memberships: a project assigned to two categories is counted in both.
Figure 7: Deployment count and average market capitalization by blockchain network. A project deployed on several chains is counted on each, so the bars sum to deployments rather than to projects; networks hosting fewer than ten deployments are grouped as Others .
Repository
No repository
Token
364
615
No token
441
—
Table 2: Evidence available per project across the two sampling frames (1,420 projects). The empty cell is structurally empty rather than zero: a project with neither a token nor a public repository is invisible to both frames, so both distributions are conditional on discovery.
Figure 8: Value retained since 6 April 2025 by each surviving project, grouped by taxonomy subcategory, on a logarithmic scale. The vertical rule marks the subcategory median. Retention inside a subcategory spans orders of magnitude, which is why the aggregate and the median tell opposite stories.
Figure 9: x402 SettlementRouter activity on Base by month. Volume and value move independently because the two regimes are different kinds of payment: December 2025’s 108.7karrivesin3,198human−scaletransfers(median8.63, largest 9,997),whileMarch2026’s240,098transfersaremachine−scale(median0.0012, largest 0.10)andtotal326.
Figure 10: ERC-8004 Identity registrations by chain, against registrations per distinct registrant. The two series move inversely: the highest-volume chains have the lowest concentration, and the most concentrated chains are small.
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.
The emergence of autonomous AI agents as first-class participants in digital infrastructure marks a fundamental inflection point in the evolution of the Web. While significant research has been directed at agent behaviour and reasoning, comparatively little attention has been paid to the infrastructure those agents require to operate reliably at scale. This paper addresses that gap with a systematic analysis of Agentverse, the agent cloud platform developed by Fetch.ai under the Artificial Superintelligence (ASI) Alliance, which represents one of the most mature production deployments of agent-native infrastructure available today. We make three principal contributions. First, we conduct an empirical audit of the Agentverse platform, cataloguing 204 API endpoints (Q1 2026) and characterising what is operational, partially deployed, or absent. From this audit we derive a Gap Taxonomy of eight categories encompassing 62 distinct missing capabilities, ranging from agent memory and observability to security, economic primitives, and enterprise scaling. Second, we propose a seven-layer Agent Cloud Stack -- a reference architecture for what a fully realised agent-native cloud should provide by 2030, grounded in the specific gaps we identify. Third, we characterise five critical evolution paths: from ephemeral storage to a full Agent Memory Cloud; from keyword discovery to a semantic, trust-weighted Agent DNS; from a single-protocol model to a multi-standard agent lingua franca; from single-instance hosting to Kubernetes-scale orchestration; and from simple token payments to rich agent economic primitives. Together these contributions provide a diagnostic of current agent infrastructure and a technically grounded vision for what the agent cloud must become to support the agentic web -- Web4 -- by 2030.
As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe computational, collaborative, and security bottlenecks. Current surveys remain fragmented across system optimization, architecture design, and trust, lacking a unified framework to evaluate the fundamental trade-off between output quality and economic cost. To bridge this gap, this survey presents the first comprehensive survey of Token Economics. By unifying computer science and economics, we conceptualize tokens as production factors, exchange mediums, and units of account. We synthesize existing literature across a four-dimensional taxonomy: (1) Micro-level (Single Agent): Optimizing budget-constrained factor substitution via neoclassical firm theory. (2) Meso-level (Multi-Agent Systems): Minimizing collaboration friction using transaction cost and principal-agent theories. (3) Macro-level (Agent Ecosystems): Addressing congestion externalities and pricing via mechanism design. (4) Security: Internalizing adversarial threats as endogenous economic constraints. Finally, we outline frontier directions, including differentiable token budgets and dynamic markets, to lay the theoretical foundation for scalable next-generation agent systems.
Yuxi Chen, Junming Chen, Chenyu He +9
College of Computer Science and Technology, Zhejiang University · School of Economics, Zhejiang University · The State Key Laboratory of Blockchain and Data Security, Zhejiang University +1