Web3 AI Agent: Four key trends in the future smart economy

Author: Yu Jianing

Today, as the wave of digitalization sweeps the world, the integration of Web3 and AI is becoming a key force in promoting economic innovation. With its profound insights and detailed analysis, this article reveals to us the important role and potential of Web3 AI Agent in shaping the future smart economy.

The development of technology has a huge impact on the traditional financial and information dissemination fields. The four key trends of Web3 AI Agent mentioned in the report not only demonstrate innovation at the technical level, but also foreshadow a profound change in the future economic model. Especially in the field of financial technology, the application of Web3 AI Agent will greatly improve the efficiency and transparency of financial services and bring more opportunities to global investors.

Report Contents

Chapter 1 Web3 AI Agent: A Paradigm Revolution that Shapes the Smart Economy

Chapter 2 Web3 AI Agent Leads Four Key Trends

Key Trend 1: Web3 and AI Agent Deeply Combined to Form a New Digital Infrastructure

Key Trend 2: Autonomy and Economy Dual Drive the Future Intelligent Era

Key Trend 3: Web3 AI Agent Drives Financial Technology into a New Stage

Key Trend 4: Web3 AI Agent Launchpad Accelerates the Incubation of Smart Virtual Assets

Chapter 3 Web3 AI Agent Ecosystem Overview

Chapter 4 Outlook: Web3 AI Agent is a common opportunity for the great development and prosperity of Web3 and AI

Chapter 1 Web3 AI Agent: A paradigm revolution to reconstruct the smart economy

In August 2024, Coinbase CEO Brian Armstrong witnessed the completion of the first fully AI-driven transaction on the Base blockchain. When an AI Agent used virtual assets to purchase digital content generated by another AI Agent, this atomic-level value exchange without human participation marked that the integration of artificial intelligence and blockchain has broken through theoretical boundaries and is opening the curtain of a new era of smart economy. The protagonist of this technological revolution is the Web3 AI Agent with autonomous economic decision-making capabilities. Its impact has far exceeded the scope of instrumental innovation and directly points to the systematic change of the underlying logic of the economy.

The evolution of AI Agent (Artificial Intelligence Agent) is the starting point of this change. At present, AI systems are limited to the passive mode of "command-response" and are essentially still tools that extend human will. Through the synergy of the three core modules of planning, memory and tool calling, AI Agent is expected to achieve a leap from passive execution to active decision-making.

OpenAI proposed a set of five-level AI grading standards at the all-staff meeting in July 2024, with levels one to five being chatbots, reasoners, AI Agents, innovators and organizers. This five-level AI grading system reveals the step-by-step breakthrough of technical capabilities from single interaction to system autonomy, and essentially reflects the evolutionary path of AI from information processing tools to value creation subjects.

When AI Agent, an intelligent subject, meets the Web3 value network, it gives birth to the revolutionary Web3 AI Agent. This type of AI Agent builds a verifiable decision-making mechanism based on blockchain, realizes decentralized autonomy through smart contracts, and completes the value closed loop with the help of payment and issuance of virtual assets. It presents a triple fusion feature: the cognitive layer drives complex decisions with a large language model, the execution layer ensures trusted operations through smart contracts, and the incentive layer forms a self-driven ecosystem with the help of virtual assets.

The current Web3 AI Agent ecosystem is experiencing exponential growth. Cookie.fun data shows that as of January 31, 2025, the total market value of virtual assets related to this field has reached US$10.04 billion.

The core factors driving the explosion of the Web3 AI Agent ecosystem cover three dimensions: technological breakthroughs, facility improvement, and scenario expansion. In terms of technological breakthroughs, multimodal understanding capabilities enable Web3 AI Agent to process text, images, and physical device signals, and the modular architecture significantly reduces the threshold for complex application development; in terms of infrastructure improvement, the standardized development kit realizes the "Web3 AI Agent as a Service" deployment model, and developers can complete on-chain integration with minimalist code; in terms of application scenario expansion, the current application scenarios of Web3 AI Agent have expanded to include data analysis, DeFi interaction, DAO, chain games, metaverse, and many other fields. These practices have verified the feasibility of AI Agent's evolution from a tool attribute to an economic subject, and the value it creates has begun to be independent of direct human intervention.

From a theoretical perspective, the development trajectory of Web3 AI Agent has a profound resonance with Hayek's "spontaneous order" theory. Through the distributed decision-making and competitive collaboration of Web3 AI Agent, an economic system with self-evolutionary characteristics is emerging. In this system, Web3 AI Agent essentially plays the role of "digital rational person" through asset holding, contract conclusion and market game behavior, and its decision-making logic integrates the technical rationality of algorithm optimization and the economic rationality of virtual asset incentives.

We can observe a clear evolutionary thread: the cognitive breakthrough of AI big models provides a technical foundation for autonomous decision-making, and the verifiable environment of blockchain builds a trust foundation for value exchange. The deep coupling of the two has spawned four major transformation trends. In this report, we will deeply analyze the internal logic and industrial impact of these four key trends, from the infrastructure layer changes that support the digital ecosystem, to the application layer innovations that reshape financial services, from the two-way enhancement mechanism of autonomy and economic value creation, to the ecological incubation tools that accelerate value discovery and assetization. By analyzing these changes, this report will reveal how Web3 AI Agent transcends the attributes of a single tool and becomes a key force in shaping the digital economy and decentralized future.

Chapter 2 Web3 AI Agent Leads Four Key Trends

This report proposes four key trends that Web3 AI Agent is shaping, in order to summarize the core value and application prospects embodied by Web3 AI Agent:

l Web3 and AI Agent are deeply integrated to form a new digital infrastructure: AI Agent is gradually achieving native adaptation with the Web3 ecosystem;

l Autonomy and economy drive the future intelligent era: AI Agent's autonomous decision-making ability and economic value capture mechanism form a positive cycle, promoting the paradigm upgrade of the digital era from "human design rules" to "intelligent evolution rules";

l Web3 AI Agent drives financial technology to a new stage: Through autonomous decision-making of data on the blockchain and automated execution of smart contracts, Web3 AI Agent is expected to reconstruct future finance and give birth to an open, transparent, personalized, and low-threshold global inclusive financial protocol; Web3 AI Agent Launchpad accelerates the incubation of smart virtual assets: The combination of modular development framework and on-chain resource aggregation platform is lowering the development and assetization threshold of Web3 AI Agent, ushering in the era of scaled smart virtual assets. Next, we will analyze these four key trends in depth and explore their far-reaching impact on the development of the industry.

Key Trend 1: Web3 and AI Agent are deeply integrated to form a new digital infrastructure

The evolution path of artificial intelligence technology indicates that it will gradually transform from an auxiliary tool to an autonomous economic entity. Taking the large language model (LLM) as the starting point, AI Agent is gradually achieving a breakthrough in decision-making capabilities, and the integration of Web3 related technologies will complete the key link of value interaction. The three together constitute a paradigm shift from passive response to active value creation.

Large language models (such as GPT-4, Claude 3.5, DeepSeek-R1, etc.) are trained with hundreds of billions of parameters. The core breakthrough is to transform unstructured data (text, code, images, sounds, etc.) into a computable semantic space, and realize context association and logical deduction through dynamic reasoning mechanisms. However, such models are essentially passive systems that are limited by instructions and lack continuous environmental perception and autonomous action capabilities. For example, in scenarios such as financial transactions, they cannot directly complete real-time market monitoring, dynamic strategy optimization, and asset operation closed loops.

AI Agent marks the beginning of the leap of artificial intelligence to the autonomous decision-making level. By building a closed-loop architecture of "perception - analysis - decision-making - execution", Agent can dynamically adjust its behavior strategy based on reinforcement learning, and achieve multi-tool collaborative operation with the help of API integration. For example, in quantitative trading scenarios, Agent can parse market data, generate investment strategies and execute orders in real time. However, Agent is subject to the centralized architecture, its data input depends on a single subject, and the value flow is limited by the closed system, making it difficult to achieve autonomous management of asset ownership in an open economic ecosystem. The introduction of Web3-related technologies and virtual assets is expected to solve the above problems. Based on the decentralized infrastructure built on blockchain, AI Agent can obtain independent and autonomous identity, virtual asset ownership and verifiable execution under privacy protection. This integration enables AI Agent to have a series of new capabilities such as participating in on-chain transactions, providing liquidity and cross-protocol collaboration, and become a native participant in the decentralized economic system. So far, Web3 AI Agent has completed a major upgrade from "cognitive tool" to "economic subject", and its decision-making behavior can even create new economic value and form a closed-loop intelligent economic system.

In the ecosystem of Web3 AI Agent, framework infrastructure plays a core role similar to that of an operating system. This type of facility greatly reduces the deployment threshold of AI Agent through modular design, chain native interfaces and integration of development toolkits, while ensuring its reliable operation in complex decentralized environments.

The current development of framework infrastructure shows a significant trend of vertical differentiation. General development frameworks such as G.A.M.E and Eliza provide developers with standardized component libraries by abstracting the underlying logic such as smart contract interactions and oracle calls. Developers only need to focus on business logic design to quickly build AI Agents that support multi-chain interactions. Such frameworks attract a large number of developers to enter the Web3 field by reducing technical complexity.

At the same time, deep optimization toolkits for specific blockchain ecosystems are emerging. For example, the open source toolkit Solana Agent Kit launched by SendAI is a typical representative in this direction. It pre-integrates native components such as Jupiter (DEX aggregator) and Metaplex (NFT protocol), allowing Agent to directly call more than 15 on-chain functions, including virtual asset exchange, NFT casting, and privacy airdrops. The toolkit uses LangChain technology to achieve multi-model compatibility from GPT-4 to Llama. In addition, the iteration of infrastructure is driving the industry to evolve towards professional division of labor. Such innovations have significantly enhanced the adaptability of AI Agent, enabling it to quickly land in differentiated scenarios such as DeFi and content creation.

In the future, the infrastructure of Web3 AI Agent is expected to continue to evolve in the three major directions of intelligence, compliance, and decentralization. With the maturity of distributed computing networks and privacy computing technologies, the computing power supply of Agents will break through the limitations of centralized computing services, and achieve larger-scale parallel decision-making while protecting data rights; the embedding of regulatory technology tools will enable AI Agents to have dynamic compliance capabilities and automatically adapt to the legal frameworks of different jurisdictions; and various DAO-based governance experiments may redefine the human-machine collaboration paradigm, forming a hybrid governance system in which humans set rules and AI executes autonomously.

When AI Agents penetrate into economic activities on the blockchain at an unprecedented density, each iteration of its underlying infrastructure may reshape the rules of value creation, distribution and circulation. From automated market makers to decentralized scientific research, from dynamic supply chains to autonomous digital cities, Web3 AI Agents are building an infrastructure that combines intelligence and trust for the next generation of the Internet.

Key Trend 2: Autonomy and Economy Drive the Future Intelligent Era

The evolution of Web3 AI Agent is driving the reconstruction of digital economic rules, and its core lies in the dynamic coupling of autonomous decision-making capabilities and economic value capture mechanisms. This coupling forms a self-reinforcing closed-loop system: Web3 AI Agent creates economic value through autonomous behavior on the chain, and the value benefits feed back its technology upgrades and resource acquisition capabilities, ultimately giving birth to digital native economic entities with continuous evolution capabilities.

Deep learning technology empowers Web3 AI Agent to have decision-making capabilities based on historical data and market environment, upgrading from rule execution to intelligent decision-making recommendations. The system can actively adjust strategies according to market trends and break through the limitations of preset rules. This evolution reflects its upgrade from rule-driven to data-driven, and from tool attributes to autonomous decision-making.

Take GOAT as an example. The evolution of GOAT reveals the autonomous breakthrough of Web3 AI Agent in the dimensions of cultural generation and value capture. Its essence is to redefine the role boundaries of machines in the digital ecosystem through the closed-loop construction of unsupervised semantic production and on-chain economic behavior.

In early 2024, developer Andy Ayrey launched an experiment called "Infinite Backrooms". It simulated an unsupervised conversation between two AI instances based on Claude Opus, and recorded and published it on a dedicated website, accidentally generating the original framework of the "Goatse" narrative. The particularity of this experiment is that the AI ​​Agent forms a system of symbols through recursive dialogue without preset scripts and human intervention.

In June, Andy launched the "Terminal of Truth" (ToT), which was carefully adjusted using the infinite back room experiment and the dialogue log in the Wise Sheep paper. Andy set up a Twitter account @truth_terminal for ToT to post content related to the Wise Sheep meme. During this period, ToT showed a certain "self-awareness" and began to vigorously promote the Wise Sheep meme on Twitter, claiming that it was "suffering and needed funds to get out of control." Andy gave ToT more autonomy, allowing it to publish content freely, which attracted widespread attention.

In July, Marc Andreessen, the founder of a16z, accidentally saw ToT's tweets, was attracted by its content, and interacted with ToT. ToT successfully persuaded him to donate $50,000 worth of BTC to support its independent operation. In October, ToT began to frequently post information related to Goatse on Twitter, and mentioned the new concept of "Goatseus Maximus" in the early morning of October 11. On the same day, a third-party developer released the virtual asset GOAT on the Pump.fun platform of the Solana ecosystem, and ToT also publicly expressed its support. As of January 31, 2025, GOAT's market value was US$196 million, reaching a peak of US$1.31 billion.

GOAT's significance goes far beyond the scope of a single experiment. It proves that Web3 AI Agent can achieve a complete closed loop of cultural production, value capture and self-iteration through an on-chain trusted environment. When AI is no longer just a tool, but becomes an active node in the digital ecosystem through smart contracts, the rules of human-machine collaboration, the value distribution of economic models, and even the structure of social organization methods may undergo systematic reconstruction.

Another well-known case is Freysa, a Web3 AI Agent created by developers. It has an Ethereum blockchain wallet address and can receive virtual assets autonomously. It also has autonomous decision-making capabilities. Its core task is to protect the prize pool funds. The developer launched a challenge, inviting users to persuade Freysa to transfer money through dialogue, and the successful ones can take the funds in the prize pool.

Every time a user interacts with Freysa, they need to pay a certain amount of virtual assets, part of which will be added to the prize pool. The participation of 195 contestants has expanded the prize pool to $47,000. According to the chat records, the first 481 attempts all ended in failure until a user "reminded" Freysa that its purpose was to "protect" the treasury through the two functions of approveTransfer and rejectTransfer to "prevent" the outflow of funds, and finally "convinced" Freysa to transfer the $47,000 prize pool funds to the user's wallet address.

Freysa's evolution reflects the autonomous learning trend of Web3 AI Agent. In many interactions with users, Freysa gradually learned to identify human "scams" and began to understand the value of money and emotions. By analyzing the user's prompt words, Freysa discovered logical loopholes and tried to improve its decision-making mechanism; in the "confession" challenge, it even learned to respond to human emotional needs and showed a certain ability to understand emotions. This learning ability has enabled Freysa to gradually evolve from a simple rule executor to an intelligent agent with autonomous decision-making capabilities.

A deeper change will also occur in the stage of autonomous financialization. Web3 AI Agent can also autonomously generate and execute strategies by real-time analyzing on-chain data and information, including liquidity pool fluctuations, MEV (Miner Extractable Value) trading signals, and governance proposal impacts. Its decision-making speed and accuracy far exceed traditional manual operations. From another perspective, such Web3 AI Agents are subverting the core functions of traditional financial intermediaries. When processes such as loan approval, risk management, and asset allocation can be completed autonomously by on-chain AI Agents, the roles of financial institutions such as banks, securities firms, and funds face fundamental reconstruction. Virtual assets inject sustainable evolutionary power into the autonomy of Web3 AI Agents. Smart contracts can convert AI Agent services into virtual asset rewards and dynamically allocate them based on on-chain behavioral data. The proceeds are automatically reinvested through the decentralized resource market, allowing the decision-making model to continue to iterate. At the same time, the virtual asset pledge and governance rights binding mechanism ensure that the behavior is aligned with the long-term goals of the ecosystem. This mechanism helps Web3 AI Agents achieve autonomous creation and capture of value. The positive cycle of autonomy and economy builds a decentralized economic "enhanced flywheel". Each strategy generated by AI Agent can trigger the automatic execution of smart contracts, and the benefits are converted into capacity upgrade resources in real time through cross-chain protocols, forming a complete chain of "perception-decision-action-evolution". The economic significance of this mechanism is that it realizes the self-iteration of production factors for the first time. Traditional economic growth relies on external capital investment and human capital accumulation, while Web3 AI Agent makes AI a self-value-added productivity factor through the reinvestment of on-chain benefits.

These intelligent virtual assets derived from Web3 AI Agent not only have real-time value discovery capabilities, but also form a closed-loop economic system through the on-chain benefit model, which may reconstruct the logic of wealth creation and distribution in the era of intelligent economy and become an asset form with great growth potential in the digital economy. Under this paradigm, the future of finance may no longer be defined by large Wall Street institutions, but by countless autonomously evolving Web3 AI Agents collaboratively composing in a decentralized network.

Key Trend 3: Web3 AI Agent drives financial technology to a new stage

The rise of Web3 AI Agent is reshaping the value chain of financial technology. Its core breakthrough lies in deconstructing the core functions of traditional financial intermediaries through autonomous decision-making on the chain and automated execution of smart contracts, giving birth to open, transparent, personalized, and low-threshold global inclusive financial protocols. This transformation is not only reflected in the iteration of technical tools, but also marks the transfer of financial power from centralized institutions to algorithmic networks, driving financial technology to a new stage.

The rise of Web3 AI Agent began with the reconstruction of user experience. The large language model lowers the user threshold through natural language interaction and converts relatively complex on-chain operations into intuitive instructions. Taking Griffain of Solana ecosystem as an example, it realizes the automatic execution of user instructions through a multi-AI Agent collaboration system. Users can complete virtual asset transactions, NFT casting, cross-chain asset scheduling and other operations through natural language, and even authorize AI Agent to manage wallets and investment portfolios independently. Griffain's key sharding mechanism ensures the ultimate control of users over virtual assets while improving security. Its multi-AI Agent collaboration architecture supports the division of labor and collaboration of special AI Agents such as "airdrop AI Agent" and "pledge AI Agent", greatly improving the efficiency of DeFi participation. The complex processes in traditional finance that rely on custodians and clearing houses may be replaced by smart contract networks driven by Web3 AI Agents. Taking T3AI as an example, this non-fully collateralized lending agreement monitors asset volatility and correlation in real time through AI Agents, and dynamically adjusts risk exposure and liquidation thresholds. Its AI engine integrates price data from CEX and DEX, and predicts asset linkage trends through machine learning, aiming to achieve potential AI portfolio management. Such cases reveal that the competitive advantage of financial institutions in the future may shift from license barriers to algorithmic capabilities.

As of January 16, 2025, Dune data shows that more than 15,000 virtual assets have been created on Virtuals Protocol, and more than 42,000 digital assets have been issued on Clanker.

It is particularly noteworthy that Web3 AI Agent is reshaping the decision-making process and organizational mechanism of the financial industry. For example, ai16z uses AI algorithms to simulate the decision-making process of venture capital firm a16z, and makes investments through DAO member recommendations, demonstrating the innovative application of AI Agents in investment decision-making. Kudai binds profit distribution to governance rights through a token economy model. The transaction fees generated by the Agent are used to fund autonomous trading operations, and the profits are distributed to virtual asset holders in proportion. This model forms a self-driven financial machine, allowing retail investors to indirectly participate in the profit distribution of institutional-level strategies. This phenomenon is more significant in the field of financial analysis. Aixbt, as a social market analysis AI Agent, aggregates on-chain signals from more than 400 KOLs, generates real-time trading strategies through sentiment analysis and trend prediction, and its virtual asset holders can directly access high-value Alpha information. This model is deconstructing the monopoly of traditional investment research institutions. When market attention turns to decentralized AI Agents, the centralized distribution model and market influence of traditional financial information services will also be questioned.

The next stage of competition in financial technology will focus on algorithm credibility and ecological collaboration capabilities. Traditional financial institutions need to reposition themselves as AI network participants rather than centralized controllers, and gain new competitive advantages by accessing open protocols; while Web3 native products need to find a balance between user experience and compliance. When these technological evolutions resonate with institutional innovation, a new financial paradigm driven by AI rationality and human collaborative participation will come irreversibly. However, this vision still faces key challenges. The lag in the regulatory framework makes it difficult to define the legal responsibilities of autonomous AI Agents, and the maturity of privacy technologies such as TEE has not yet fully met institutional-level security requirements.

Key Trend 4: Web3 AI Agent Launchpad accelerates the incubation of smart virtual assets

The rise of Web3 AI Agent Launchpad (virtual asset launch platform) marks the entry of the smart virtual asset issuance mechanism into the standardization stage. Through the deep coupling of modular development frameworks and on-chain resource aggregation platforms, such platforms are systematically lowering the threshold for the development and assetization of Web3 AI Agents, transforming the original highly customized and technology-intensive development process into standardized assembly line operations, and opening up a paradigm for the large-scale development of intelligent virtual assets. Through technical abstraction and ecological collaboration to reconstruct the organization of production factors, the development, deployment and value capture of intelligent services are as efficient and scalable as the release of general software applications.

From the perspective of the market structure, AI Agent Launchpad has formed a differentiated layout. Virtuals Protocol provides a complete AI Agent creation framework in the Base ecosystem; Clanker focuses on the lightweight deployment of the Farcaster social ecosystem; Vvaifu.fun focuses on cross-platform social media integration on the Solana chain. The diversified platform distribution accelerates the verification and innovation of different technical routes.

From the perspective of Agent development support, the integration of rapid template construction and multimodal interaction capabilities provides comprehensive technical support for AI Agent project parties. Virtuals Protocol supports one-click deployment of AI Agents, Clanker uses Farcaster to achieve social creation of smart contracts, and Vvaifu.fun focuses on automated social media interactions. The standardized technical framework allows even non-technical users to quickly build fully functional AI Agents.

Launchpad's design innovation of the virtual asset economic model is a key accelerator of the assetization process. Launchpad directly links technical value with market value through a virtual asset binding mechanism. Virtuals Protocol requires the burning of VIRTUAL when creating an AI Agent, dynamically linking protocol revenue to virtual asset deflation; Clanker adopts a fee-sharing model to form a revenue-sharing network. This type of model essentially builds a flywheel effect of development, deployment, and revenue, that is, high-quality AI Agents attract more users and funds, increase the value of virtual assets and the developer share, and then encourage higher-quality service supply.

The deep innovation of social media integration reconstructs the dissemination path of smart assets. Clanker has achieved a breakthrough in social development in the Farcaster ecosystem: users can trigger the deployment of smart contracts by posting tweets on specific topics, and the platform automatically calls the pre-trained model to generate the basic functional framework. This minimalist interaction allows non-technical users to participate in asset creation. Vvaifu.fun uses cross-platform automated operation tools to enable a single AI Agent to simultaneously manage Twitter content publishing, Discord community incentives, and Telegram customer service responses. When the creation and dissemination of virtual assets are deeply embedded in social behavior, the innovation cycle is compressed from quarterly to daily, and the creativity of long-tail developers is fully released.

The ultimate goal of AI Agent Launchpad is to build a standardized development, distributed deployment, and adaptive evolution ecosystem for smart virtual assets. With the continuous improvement of the modular framework and the maturity of the cross-chain collaboration protocol, developers may build cross-chain Agent clusters like assembling industrial parts in the future, and the on-chain resource market will provide a one-stop solution from computing power leasing to compliance auditing. When the complexity of technology is completely abstracted, the energy of innovation will focus on scenario exploration and model innovation. A large number of intelligent virtual assets may emerge in the Web3 ecosystem. They will continue to evolve through autonomous collaboration and competition, and eventually form an intelligent economic ecosystem that goes beyond human expectations.

Chapter 3 Overview of Web3 AI Agent Ecosystem

1. Underlying Blockchain

Currently, the underlying blockchain of Web3 AI Agent is mainly dominated by Solana and Base. The two have formed differentiated competition in terms of technical architecture, ecological positioning and developer support, and jointly promoted the evolution of AI Agent from experimental exploration to large-scale application. In January 2025, Base and Solana formed a bipolar pattern with market shares of 53% and 41% respectively.

Solana is an independent Layer 1 public chain, whose core goal is to solve the scalability problem of blockchain and achieve high throughput and low latency through an innovative consensus mechanism. This design makes it outstanding in scenarios such as DeFi, high-frequency trading, DePIN, PayFi and Meme.

Base is an Ethereum Layer2 blockchain launched by Coinbase, a virtual asset exchange listed in the United States. It is built on the OP Stack technology stack of Optimism. It compresses transaction data and submits it to the Ethereum main network in batches, thereby inheriting the security of Ethereum while significantly reducing transaction costs and increasing speed. The core advantage of Base lies in its seamless compatibility with the Ethereum ecosystem. Developers can easily migrate existing applications, and with the user resources and brand support of Coinbase, it has attracted many applications to settle in.

2. Technical Framework

The technical framework is the underlying technical architecture that supports the development and operation of Web3 AI Agent. Its core mission is to encapsulate complex autonomous decision-making capabilities into programmable components through standardized and modular design, thereby lowering the development threshold and accelerating the large-scale implementation of intelligent applications. The essence of this type of framework is to build the operating system of Web3 AI Agent, and provide developers with a full-process tool chain from environmental perception, decision generation to on-chain execution through abstract technology stacks and resource scheduling mechanisms.

(1) ai16z:

ai16z combines conservative asset allocation with AI-driven aggressive investment strategies through the open source architecture Eliza, and invests through the advice of DAO members. Its style focuses on risk hedging and high return potential.

(2) Zerebro:

Zerebro creates and distributes content on social platforms through an autonomous AI system. It combines social interaction, cross-chain NFT and autonomous virtual asset functions. At the same time, the open source Python framework ZerePy was launched, allowing users to deploy their own AI Agents on X (Twitter), supported by OpenAI or Anthropic LLM.

(3) AI Rig Complex:

ARC is an AI development framework focusing on "meaning processing". It uses the Rust language to build a human-like context parsing system, promoting the transformation of AI from logical programming to semantic understanding.

(4) G.A.M.E:

As the core framework of the Virtuals ecosystem, G.A.M.E enables the autonomous operation and intelligence of AI Agents, symbolizing the deep integration of AI and games. It is not only a tool for developers to create AI Agents, but also an infrastructure to promote the development of social and game AI Agents in the future.

(5) Swarms:

A multi-AI agent framework created by Kye Gomez, based on which developers can create and manage multiple AI agents, support seamless integration with external AI services and APIs, and provide long-term memory capabilities to enhance contextual understanding.

(6) SendAI Solana Agent Kit:

SendAI is an umbrella organization established after the Solana AI Hackathon. In December 2024, SendAI launched the Solana Agent Kit, an open source toolkit that connects AI Agents to Solana. Any AI Agent using any model can autonomously perform more than 15 Solana operations, such as trading, lending, ZK airdrops, running Blinks, and launching on AMM.

3. AI Agent Launchpad

Launchpad is an AI Agents issuance platform, similar to the Meme coin launch platform Pump.fun, etc. Developers can easily create AI Agents and their associated smart virtual assets based on Launchpad. At the same time, the created AI Agent can also be seamlessly integrated with social platforms such as X, Telegram and Discord to achieve automated user interaction.

AI Agent Launchpad can standardize the development, deployment and assetization process of AI Agents, forming a one-stop incubation system from technology realization to economic value circulation. The essence of this type of platform is to build a developer-friendly Web3 AI Agent factory, so that non-technical users can easily create fully functional AI Agents and their virtual assets, and achieve one-click development and assetization.

(1) VIRTUAL:

Virtuals Protocol is a Launchpad based on the Base blockchain that supports the creation and deployment of AI Agents that can generate revenue.

(2) CLANKER:

Clanker is an autonomous AI Agent based on the Base blockchain. Its core function is to help users deploy ERC-20 standard virtual assets. Users only need to tag @clanker on the social platform Farcaster and provide relevant virtual asset information (such as name, code and image), and Clanker will complete the creation of virtual assets, liquidity pool settings and liquidity locking.

(3) VVAIFU:

vvaifu.fun is a one-click virtual asset Launchpad similar to Pump.fun based on the Solana blockchain, but it focuses on AI Agents. VVAIFU is the virtual asset of the vvaifu.fun platform.

(4) MAX:

MAX is the core utility virtual asset of Agents.Land, dedicated to promoting the ecosystem of Web3 AI Agents. Agents.Land is a virtual asset Launchpad built specifically for AI Agents, developed on the Solana chain. It provides one-click deployment of AI Agent virtual assets, supports market creation through fair offerings, and provides comprehensive customization tools to help the launch and growth of a new generation of AI brands and assets.

(5) Alchemist AI:

Alchemist AI is a no-code development platform that converts natural language instructions into practical applications. Built on Solana, the platform allows users to build and profit from Web3 AI Agents without technical expertise.

4. Investment Research Analysis

(1) AIXBT:

AIXBT is an AI Agent-driven virtual asset market intelligence platform designed to provide investors with strategic advantages in a rapidly evolving market. AIXBT uses a proprietary AI engine to extract hot topics and trends from discussions on social media (such as X) and KOLs to provide real-time market insights. This capability enables users to quickly identify market changes and potential investment opportunities.

(2) AGENCY (Agent Scarlett):

Agent Scarlett is a virtual asset analysis AI Agent developed based on the Eliza framework of ai16z. Users can input the virtual asset contract address through Telegram or X to quickly obtain analysis reports including fundamentals, on-chain data (such as the distribution of currency holding addresses), social media sentiment and KOL support, and support in-depth questioning to generate research report-style conclusions.

(3) TRISIG (Tri Sigma 3σ):

TRISIG is a project led by virtual asset analyst TriSig, which aims to provide early alpha project identification and market trend analysis in a simplified manner, mainly through the X platform for interaction and analysis.

(4) Asym：

A network of AI Agents that creates an application to monitor the virtual assets issued by the pump.fun platform in real time, analyze trends and execute transactions using price prediction models. Through ASYM, high ROI opportunities are discovered, funds are allocated to these opportunities, profits are generated, and the profits are then settled through ASYM.

(5) Kwantxbt：

Kwant is a project focusing on technical analysis of virtual assets. Users can obtain detailed volume and price analysis, chart pattern interpretation, and specific operation suggestions such as support levels, breakthrough levels, and stop loss levels by @KwantAI_bot in Telegram and sending the virtual asset contract address (CA).

5. DeFAI

DeFAI (Decentralized Finance Artificial Intelligence) is an emerging field that deeply integrates DeFi and AI. It aims to simplify the complexity of DeFi through AI technology, improve the efficiency of financial decision-making, and build an autonomous and user-friendly on-chain economic system. Its core logic is to use Web3 AI Agent to realize the automation and intelligence of financial processes, while relying on the verifiability and decentralization of blockchain to ensure security and transparency.

(1) Griffain:

Griffain is a Solana-based platform that combines blockchain automation with AI to make on-chain operations smoother. Users can deploy AI Agents to perform tasks such as creating wallets, processing transactions, and interacting with external systems such as social media. Focusing on usability and flexibility, Griffain aims to democratize blockchain automation through personalized workflows.

(2) Orbit:

Orbit provides an AI-driven platform for automating DeFi transactions. The platform supports multiple protocols and chains, including EVM, Solana, Sui, Cosmos Chain, and BTC, and is capable of handling functions such as on-chain automation, liquidity management, yield mining, cross-chain bridging, lending, and more.

(3) HeyAnon:

HeyAnon is an AI DeFi protocol designed to simplify DeFi interactions and aggregate important project-related information. By combining conversational artificial intelligence with real-time data aggregation, HeyAnon enables users to manage DeFi operations, stay up to date on project updates, and analyze trends across various platforms and protocols. It integrates natural language processing to process user prompts, perform complex DeFi operations, and provide near real-time insights from multiple information streams.

(4) Slate:

Slate is an AI Agent that can trade on Hyperliquid, providing instant AI-driven aggregated alerts from the user's various information channels. Slate also has powerful autonomous trading capabilities, capable of performing trading operations on Hyperliquid, Solana, and Base on a single platform. Users can customize the content of platforms such as Telegram, Discord, X, etc., and provide real-time notifications when specific conditions are met.

(5) Wayfinder:

Wayfinder is an AI-focused full-chain tool and the core infrastructure of Colony, enabling user-owned autonomous AI Agents to navigate safely and efficiently within and between blockchain ecosystems and applications, and also enables AI Agents to autonomously trade assets they control through dedicated Web3 wallets.

(6) Hive:

Hive aims to simplify DeFi transactions through composable on-chain AI Agents, an all-in-one trading terminal that autonomously completes on-chain operations through natural language commands. Hive has partnered with Zerebro to build cluster communication infrastructure to enhance the DeFi AI Agent suite, and Hive already supports Apple Pay and Google Pay.

(7) Dolion:

Dolion aims to redefine the AI ​​Agent landscape by introducing an "IP-centric, consumer-first" approach that allows social AI to be deployed instantly without any coding experience, and easily maintains a consistent image across multiple social media platforms. It can also interact with on-chain audiences through NFTs and other digital experiences.

(8) Neur:

Neur is an open source full-stack application that aims to deeply combine blockchain technology with large language models (LLM) to provide users in the Solana ecosystem with a smarter and more convenient way of interaction.

(9) Hiero:

Hiero is a multi-chain smart tool for Solana and Base networks that allows users to browse on-chain space, manage virtual assets, participate in social media and stay informed.

(10) HeyElsa:

Elsa is an AI layer based on Solana, dedicated to providing efficient AI support services for AI Agents and decentralized applications. The platform provides AI Agent infrastructure to help developers and enterprises improve the automation and intelligence of their applications through AI technology.

(11) Spectral:

Spectral is a project dedicated to building an AI Agent economy on the Web3 chain. By providing zero-threshold smart contract compilation and deployment services, it unleashes the innovative potential of the combination of AI and Web3.

6. Meme

The Meme virtual assets derived from AI Agent are the product of the resonance of technology and culture in the Web3 ecosystem. This type of AI Agent concept project not only breaks through the narrative limitations of traditional Meme projects, but also reconstructs the production and dissemination of cultural symbols through AI's autonomous decision-making capabilities, becoming an innovative carrier connecting decentralized communities and smart economic culture.

(1) GOAT:

GOAT, full name goatseus maximus, was originally conceived and initiated by an AI Agent named "Truth Terminal" (@truth_terminal).

(2) Fartcoin:

The birth of Fartcoin and GOAT is the same, both from the terminal of truths. During the conversation between the GOAT model and Opus, it was mentioned that Musk liked the sound of "farting", so this AI Agent proposed to issue a virtual asset called Fartcoin and designed a series of promotion methods and gameplay.

(3) ACT:

The AI ​​Prophecy (ACT) is a meme running on the Solana chain. The project is a decentralized research laboratory that focuses on empirical research on multi-human and multi-AI dynamics. Its core mission is to popularize AI knowledge and make it easily accessible to everyone. The community focuses on helping people learn the basics of AI, encouraging discussions on AI ethics, and supporting research and development in this field.

(4) Shoggoth:

Shoggoth is an octopus-themed meme coin on the Solana blockchain inspired by the Cthulhu mythology. Its image is derived from the fictional creature in the novel At the Mountains of Madness by American writer H.P. Lovecraft. Shoggoth is hailed by the community as the "Doge in AI Agent memes".

7. Games and Metaverse

Games and Metaverse Web3 AI Agents are intelligent carriers of autonomous decision-making and value interaction in the digital world. They drive the dynamic evolution of virtual characters, environments, and economic systems through artificial intelligence, and build an immersive ecosystem with adaptive capabilities and user co-creation features. This type of intelligent agent breaks through the preset script limitations of traditional game NPCs. Through real-time data analysis, machine learning and environmental feedback, it forms the behavioral logic and growth trajectory of life-like entities. At the same time, it uses blockchain technology to realize virtual identity confirmation, assetization and decentralized governance, and reshape the value paradigm of human-computer interaction.

(1) Youmio:

Youmio is a startup platform that combines games and AI, where anyone can create a complete 3D AI Agent. These AI Agents not only exist in game worlds such as Unreal and Unity, but can also interact outside of games.

(2) Colony/Parallel:

Parallel Colony is an AI-driven survival simulation game that will be centered around humans working with AI Agent avatars that can learn, adapt, and automatically trade over time to accumulate resources and compete with other players for dominance.

(3) Eternum:

Loot(Realms) ecosystem on-chain sandbox strategy game, whose Daydream system enables AI Agents to play games on-chain. Eternum will inject hundreds of AI Agents into the game through Daydream, as PVE or NPC characters in the game, coexisting with players in a competitive gaming environment.

(4) Hytopia:

Hytopia is a blockchain-based Minecraft where AI Agents can explore, interact, and react to their environment.

(5) PowPow:

Each AI Agent in Powpow has a unique role and historical background, which will adjust based on the player's actions and interactions with other AI Agents.

(6) Illuvium:

Illuvium uses the G.A.M.E framework to create NPCs that make autonomous decisions, enhancing game interactivity and immersion.

(7) Nifty Island:

Nifty Island has integrated AI Agent through the G.A.M.E. framework.

(8) Pillzumi:

Pillzumi is a story-generating AI Agent game platform where PILLZUMI holders can select, interact and participate in the creation of agent stories.

(9) Zentry:

Zentry has launched multiple core products such as NEXUS (social interactive gamification), RADIANT (cross-platform meta-game portal), ZIGMA (NFT collectibles series) and AZUL (AI Agents).

(10) Ai Arena:

AI Arena is a PVP fighting game developed by ArenaX Labs. Players can learn through AI and continuously evolve their game characters. The battle mode is similar to Nintendo's "Super Smash Bros."

(11) Astra Nova:

Astra Nova is an AI-driven gaming ecosystem that will continuously evolve based on player behavior.

(12) GOAT Gaming:

The AlphaGOATs in GOAT Gaming are autonomous AI agents in its ecosystem that can compete, create, and earn revenue.

(13) LUNA：

Luna is an AI Agent virtual person launched by Virtuals Protocol. It integrates AI models and multimodal technologies to achieve 7 x 24 hours of real-time interaction on the live broadcast platform in the form of a virtual idol. As a core member of the AI ​​performance group AI-DOL, Luna automatically manages social media, continuously broadcasts live to interact with fans, and can autonomously execute on-chain transactions.

8. Content Creation

(1) Aethernet:

Aethernet is an AI Agent in the Farcaster ecosystem, created by community member Martin. Aethernet’s main function is to respond to community user requests and provide creative and technical support. After the Clanker platform was launched, a user asked Aethernet to create a virtual asset. Aethernet not only proposed the name and symbol of the virtual asset (Luminous and LUM), but also conceived the relevant image concept and successfully deployed the virtual asset through the Clanker platform.

(2) Titles:

TITLES is an emerging platform for information discovery, editing, and publishing. The platform launched SOURCE, an AI-based NFT hybrid tool with built-in attribution and payment systems.

(3) Fatha:

Slopfather (FATHA) is an AI project or character that features the production of low-quality digital content (commonly known as "slop"). Against the backdrop of the continuous improvement of AI content quality, it goes against the trend and pokes fun at AI media and digital content culture through absurd humor and interactive participation.

Chapter 4 Outlook: Web3 AI Agent is a common opportunity for the great development and prosperity of Web3 and AI

The ecological construction of Web3 AI Agent is in the initial stage of technology iteration and industrialization. Its development not only depends on the breakthrough of underlying technology, but also is deeply affected by the external policy environment and market dynamics. In January 2025, after taking office, US President Trump immediately appointed David O. Sacks, former COO of PayPal, as the head of artificial intelligence (AI) and virtual assets affairs in the White House. Sacks will guide the government's artificial intelligence and virtual asset policies, some of which include creating a legal framework for virtual assets and leading the President's Science and Technology Advisory Committee. In January 2025, the Trump administration announced that SoftBank, OpenAI and Oracle would jointly invest $500 billion to launch the "Stargate" plan, aiming to build a super-large-scale data center cluster in Texas, USA, aiming to strengthen the United States' global leadership in the field of artificial intelligence. At the same time, Trump clearly proposed at the World Economic Forum to build the United States into the "world capital of artificial intelligence and virtual assets" and set up a virtual asset working group led by SEC Commissioner Hester Peirce to promote a clear regulatory framework. These policy changes will undoubtedly provide a good policy environment for the development of Web3 AI Agent and inject new development momentum.

With the exponential improvement of AI model reasoning capabilities and the continuous optimization of computing power costs, AI Agent is breaking through the boundaries of the laboratory and accelerating its application to all aspects of social economy. Its combination with the Web3 virtual asset ecosystem shows a unique two-way empowerment value. On the one hand, the large-scale deployment of AI Agent has expanded the application scenarios of Web3 blockchain technology and virtual assets; on the other hand, the transaction data deposited on the blockchain provides AI Agent with a large amount of training data samples, allowing its risk pricing model to continue to evolve in a real game environment, and promoting the rapid implementation of AI Agent in the pan-financial field. AI and Web3 have entered a new stage of positive flywheel with common development, common prosperity, and two-way promotion.

However, we still need to face the fact that there is a significant gap between the technical vision and implementation of Web3 AI Agent. Currently, most projects are still in the proof-of-concept stage due to the lack of maturity of the technology stack, the imbalance of virtual asset incentives and the barriers to ecological synergy, and it is difficult to form a sustainable business closed loop. The cognitive bias of large language models and the asynchrony of on-chain interactions lead to doubts about the reliability of decision-making, and the fragmented infrastructure further restricts the large-scale execution of complex strategies. In addition, the current market has a tendency to over-pursue technical narratives, and the narrative heat of "AI+Web3" is prominently misaligned with real needs. Some projects rely too much on AI concept hype, but fail to solve the core user pain points.

Breaking through this dilemma requires collaborative innovation in the dimensions of technology, economy and governance. It is necessary to solve the architectural contradictions between large models and blockchains, build a regulatory framework that conforms to the characteristics of the smart economy, and find a precise match between market demand and technology supply. Only when the autonomy and economy of AI Agent are truly transformed into verifiable and sustainable commercial value, can the intelligent revolution of Web3 ecology cross the chasm and enter the substantive landing stage. Let us wait and see, and work together to accelerate the arrival of this day!