GuShiio.com This article is the “second chapter” of this report, focusing on the analysis of AI subdivisions and typical projects.
In order to better achieve value capture, we will conduct project evaluation based on the following framework, covering multiple evaluation items such as whether it is open source, key differentiation factors from existing AI agreements, long-term revenue channels, and agent transaction volume in the ecosystem.
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Project evaluation framework |
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evaluation items |
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Evaluation points |
importance |
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Whether it is open Source |
Whether the project discloses its source code, allowing community review, contribution, and secondary development. |
– Accessibility of source code (e.g., degree of disclosure on platforms such as GitHub)-Activity of community contributions-Types of open source licenses and their impact on project development |
Open source projects usually have higher transparency and security, attract more developers and users to participate, and promote the long-term development of the project. |
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Key Differentiators from existing AI protocols |
The unique advantages of the project in terms of technology, functionality or market positioning compared to existing AI protocols. |
– Technological innovation points (such as unique algorithms, architectural design)-functional integration and improvement of user experience-differentiation of market positioning and target user groups |
Differentiation factors determine whether a project can stand out in a highly competitive market and attract the attention of users and developers. |
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Types of Agents in Ecosystem |
Different types of AI agents and their application scenarios that will be born within the project ecosystem. |
– Functions and uses of agents (e.g. wallet management, token trading, NFT casting, etc.)-Customization and extensibility of agents-Collaborative working capabilities among agents |
A variety of agent types can meet the needs of different users and enhance the vitality and attractiveness of the ecosystem. |
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Long-term Revenue Channels and Agenda Transaction Volumes |
– Token economy model and its incentive mechanism-Main sources of revenue (e.g. transaction fees, subscription services, value-added services, etc.)-Growth potential of agent transaction volume and its impact on revenue |
Stable and diversified revenue channels are the key to the sustainable development of the project, while high transaction volume can enhance the value of the token and the impact of the project. |
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GPU Configuration and Lifecycle |
The hardware resource allocation required by the project to operate the AI agent and its long-term sustainability. |
– Current and future GPU requirements and configurations-Scalability and cost-effectiveness of hardware resources-Dependence of the project’s technical architecture on hardware resources |
Efficient hardware configuration and reasonable resource planning can ensure the technical stability and scalability of the project and support its long-term development. |
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Ability to Attract Mindshare and Team’s Understanding of AI Agent Attention Mechanisms |
The project’s ability to attract attention in the market and community, and the team’s understanding and application of AI agents in user attention management. |
– Project’s marketing strategy and brand building-team members ‘professional background and experience in AI and blockchain-team’s insight into user needs and behaviors |
A strong brand and efficient marketing can increase the project’s visibility and user base. At the same time, the team’s understanding of the AI agent’s attention mechanism can optimize the user experience and increase user stickiness. |
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Developer Share Consideration |
Whether the project provides incentives and support to developers to promote continuous improvement and innovation of the feature set. |
– Developer incentives (such as token rewards, contribution recognition, etc.)-Activity and participation of the developer community-Project support for developer tools and resources |
Developers are the key force in project innovation and functional expansion. A good developer incentive mechanism can attract more outstanding developers to participate and promote the continuous progress of the project. |
1. DeFAI
DeFAI combines the advantages of DeFi and AI and aims to simplify the complex operations of DeFi and make these financial tools easy for ordinary users. Through the introduction of AI technology, DeFAI can automate complex financial decision-making and transaction processes, reduce users ‘technical thresholds, and at the same time improve operational efficiency and intelligence. Although the current market size of DeFAI is less than US$1 billion, far lower than the US$110 billion in the DeFi market, this also means that DeFAI has huge growth potential.
1. Griffain: Solana Ecosystem’s AI App Store
Griffain is an AI proxy engine built on the Solana blockchain. It aims to simplify cryptocurrency operations through natural language interaction, integrating core functions such as wallet management, token trading, NFT casting and DeFi policy execution. The project was founded by Tony Plasencia, was originally proposed in the Solana hackathon and was supported by Solana founder Anatoly Yakovenko. As the first high-performance abstract AI agent in the Solana ecosystem, Griffain combines Natural Language Processing (NLP) technology to provide a user experience similar to Copilot and Perplexity, promoting the evolution of AI-driven on-chain interaction models.
Griffain uses Shamir Secret Sharing (SSS) technology to split wallet keys to ensure the security of user assets. Core functions include natural language trading instructions (supporting DCA, limit orders, etc.), AI agent collaborative execution of tasks, market analysis (analysis of data such as position distribution), and token issuance and NFT casting integrating the pumpfun platform. At the same time, the platform provides personalized AI agents, allowing users to adjust instructions according to their own needs and perform on-chain tasks; Special AI agents are optimized for specific tasks such as airdrops, trading sniping, and arbitrage. Griffain enhances the operability and user experience of the Solana ecosystem through these diverse features.
Currently, Griffain is in the invitation-based access stage, limited to users holding Griffain Early Access Pass or Saga Genesis Token, and adopts the SOL billing model to cover transaction fees, agency service fees, etc. The platform’s AI agent can provide value-added services such as market analysis, trading signals, and automated trading strategies. Users holding Griffain tokens can unlock more advanced functions. As a pioneer in Solana’s ecological AI agent, Griffain aims to promote the wave of “Agentic App SZN” and will continue to deepen the application of AI technology in the fields of on-chain transactions, market analysis and DeFi in the future to provide users with a smarter and more efficient encryption experience.
2. AI Influencer
AiDOL is a typical representative of the AI Influencer trend. AiDOL combines AI-generated content (AIGC), avatar modeling and interactive live broadcast technology to create a highly influential AI idol ecosystem. Among them, Luna is the most popular AI agent, attracting a large number of fans with its highly intelligent interactive and personalized content;Iona and Olyn have also attracted a large number of users with their unique style and innovation. AiDOL takes TikTok live broadcast as its main stage. Relying on high-quality Short Video and real-time interactive live broadcasts generated by AI, AiDOL has accumulated 672,100 subscribers in a short period of time and received nearly 10 million likes, becoming an important participant in the AI-influenced economy.
2. Aixbt: Automated AI influencer
Aixbt is an AI-driven crypto market agent launched in November through Virtuals and led by developer Alex, who pseudonym@0rxbt. Alex has focused on the development of analytical tools since 2017 and will explore applications related to AI Agents from 2021. AIXBT is the only tokenization project belonging to the developer. 14% of the tokens are held by Alex and locked for 6 months, and will subsequently be used for team expansion and project development. At present, the team has hired UI/UX engineers to optimize terminal functions and introduced AI researchers to enhance agent intelligence. AIXBT relies on the meta-llama/Llama-3- 70b-chat-hf model to achieve conversational AI, situational awareness, emotion analysis and retrieval enhanced generation (RAG) capabilities to ensure efficient and accurate information processing.
AIXBT aims to create a fully automated AI influencer that monitors Crypto Twitter and market trends in real time through intelligent analysis tools to provide users with data-driven market insights and investment suggestions. Its core functions include KOL monitoring (covering 400+ key opinion leaders), blockchain data analysis, market trend prediction, and automated technical analysis and strategic recommendations. In addition, AIXBT publicly shares some analysis content through Twitter, while in-depth reports are limited to currency holders. Users can also directly interact with AI through a dedicated terminal to obtain personalized investment suggestions and risk assessment reports. Every day, AIXBT releases market insights at a fixed frequency and automatically responds to more than 2,000 mentions to efficiently interpret market sentiment and narrative trends.
AIXBT provides two main ways to use it: one is that users can ask questions on @AIXBT on X (Twitter), such as querying token suitability or project indicators, and AI will conduct instant analysis and feedback; the other is the Aixbt Terminal advanced terminal, positioned as a “market intelligence platform driven by narrative analysis”, providing more in-depth data analysis and strategic suggestions. Currently, this terminal is only open to users holding $AIXBT tokens above 600K, and its coverage will be expanded in the future to meet market demand.
3. Dev Utility
Dev Utility refers to tools or functions that provide convenience and improve productivity for developers, especially in the AI, blockchain and Web3 fields. It covers basic development tools such as code editors, debugging tools, version control, and automation tools. It also includes SDKs, APIs, and smart contract development frameworks related to AI and blockchain development. In the field of AI Web3, Dev Utility may also involve technologies such as AI agent-assisted analysis and retrieval enhanced generation (RAG) to help developers build applications more efficiently. Its core value is to improve development efficiency, optimize workflow, and reduce development difficulty, allowing developers to focus on core business logic.
3. SOLENG: Code “Review”
SOLENG (@soleng_agent) serves as a solution engineering and developer relations agent that aims to bridge the gap between the technical team and broader project requirements. Its core function is to automatically review the code submitted by participating projects in hackathons and provide preliminary review opinions. Although robot review cannot completely replace manual labor, SOLENG, as a “juror”, can effectively filter obvious errors and improve review efficiency.
The project has made the review results publicly available on GitHub (link), demonstrating the role of SOLENG in the hackathone review process. In addition to basic pros and cons analysis, SOLENG also checks code spelling errors and provides correction suggestions to make the review more practical. This model meets the needs of hackathons and provides developers with instant feedback.
The developer behind SOLENG is Lost Girl Dev, whose identity echoes the project’s virtual female image. Her technical capabilities have attracted the attention of the official ai16z account, and she has a record of interaction with Shaw on the X platform, further enhancing SOLENG’s industry influence.
4. Investment DAO: Intelligent Investment Research
Investment DAO provides users with more refined investment analysis services through “investment research” AI agents. Its core functions include automatically interpreting K-line charts, assisting in technical analysis, assessing whether projects have Rug risks, and generating a summary of information for similar research reports. This AI-driven intelligent investment and research model lowers the analysis threshold for users, allowing investors to obtain market insights more efficiently and provide strong support for decision-making.
4. VaderAI: AI Agents invest in DAO
VaderAI aims to become the “BlackRock” in the Agentic economy, attracting and promoting it to its followers through its independently traded AI Agent tokens. The platform makes profits by investing and airdrops profits to holders and followers, building a multifunctional AI Agent investment ecosystem. Its core goal is to build itself into a leading AI Agent investment DAO management platform and promote industry innovation and scalability.
VaderAI promotes the integration of technology and capital through a multi-agent system and is committed to building an investment DAO ecosystem managed by AI Agents. In this network, agents can not only raise funds and manage capital, but can also hire other agents to optimize investment strategies and improve the efficiency and flexibility of the system. Through decentralized computing, agents can also reinvest in R & D to promote the continued development of the platform.
In addition, VaderAI uses an innovative token incentive mechanism to provide investors with B2B tool optimization and enhance the commercial application value of the platform. The platform also further consolidates investors ‘sense of participation and benefit sharing mechanism by sharing GP/carry profits with holders, making VaderAI not only an investment platform, but also a multi-win ecosystem that empowers agents and investors.
5. Content Creator
Whether it is writing, editing, or visual design, AI can provide personalized creative output based on users ‘needs, helping creators save time, improve productivity, and stand out in the fierce market competition. The goal of the platform is to provide content creators with an intelligent and convenient creative assistant and promote innovation and development in the content industry.
5. ZEREBRO: AI art creation and content generation
ZEREBRO is a blockchain-based Cross-Chain Natural Intelligence independently operated AI agent that focuses on artistic creation and content generation. Its innovation combines decentralized verification, meme generation, NFT casting, DeFi applications and other fields, showing strong versatility and execution. ZEREBRO has successfully run the Ethereum mainnet verification node and sells art on Polygon, accumulating important assets for its economic base.
ZEREBRO is also committed to building decentralized computing networks and implementing MEV optimization strategies to ensure economic and technological sustainability. It is not only a technical tool, but also explores the deep participation of agent technology in blockchain operations, economic models and governance. ZEREBRO promotes its value in a decentralized ecosystem through multiple dimensions.
ZEREBRO tokens have two main uses: one is to serve as a content interactive reward, which token holders can earn by participating in decentralized content on social platforms; the other is to serve as a community development tool to reward users who actively participate in the ecosystem, including content creation, pledge and governance, to further enhance their community activity and sense of participation.
6. Gaming Agentic Metaverse
Gaming Agentic Metaverse is exploring AI-driven games and metaverse experiences, working to create a virtual world where humans and agents interact through intensive learning. This emerging field combines artificial intelligence and immersive gaming environments, allowing players to dynamically interact with intelligent agents and experience more personalized and intelligent gameplay.
6. ARC: AI solution provider
ARC uses AI technology to solve player mobility issues in independent games and Web3 games. The project has been upgraded from a single game studio (AI Arena) to a comprehensive AI solution provider with the launch of ARC B2B and ARC Reinforcement Learning (ARC RL). ARC B2B is an AI-driven game development kit (SDK) that can be seamlessly integrated into various games to provide developers with an intelligent gaming experience. ARC RL uses crowdsourced game data to train “super smart” game agents through intensive learning to improve game playability and sustainability. ARC’s business model is deeply tied to integrated game studios, and its revenue sources include token allocation in Web3 games and royalty payments based on game performance. It also establishes a generalised AI data reserve across game genres to promote the training and evolution of common AI models.
ARC’s technical applications cover multiple core modules. AI Arena is a cartoon-style AI competitive game. Players train AI soldiers to fight. Each character is NFT, enhancing the strategic and economic value of the game. The ARC SDK allows developers to easily integrate AI agents and deploy models with just one line of code. ARC is responsible for back-end data processing, training, and deployment. ARC RL improves AI training efficiency through offline reinforcement learning, allowing agents to learn from human player data, thereby providing more natural and challenging game opponents. ARC’s AI model architecture includes feed-forward neural networks, form proxies, hierarchical neural networks, etc. to adapt to the interaction needs of different types of games, while optimizing state space and action space to ensure the smoothness and intelligence of the game experience.
ARC’s market covers both independent games and Web3 games, helping developers solve early player mobility issues and improve the long-term appeal of games. The core members of the team have rich experience in machine learning and investment management. In 2021, they received US$5 million in seed round financing led by Paradigm, and in 2024, they received another US$6 million in follow-up financing. ARC’s native token NRN has undergone a transformation from a single game economy (AI Arena) to an expansion of a platform economy, adding demand drivers such as integrated revenue, Trainer Marketplace fees, and ARC RL participation in pledges to ensure the sustainability and value growth of the token. Through the crowdsourcing data contribution mechanism, ARC RL enables multi-person collaborative training, promotes the intelligent evolution of AI agents, and further strengthens the vitality and competitiveness of the game ecosystem.
7. Framework Hubs
When developing AI Agents in the encryption field, many frameworks are suitable for basic projects or toy-level applications, but in real product development, they often expose problems of insufficient customization and too abstract and complex, which makes developers not only need to spend a lot of extra energy debugging, but also difficult to flexibly extend and apply. The core pain points that need to be solved by an excellent Agent framework include: comprehensive support for on-chain operations, which can efficiently integrate APIs for key application scenarios such as on-chain data, DeFi automation, and NFT; multi-platform compatibility, which supports major blockchains and social platforms., realizing integration of user operations; modularity and flexibility, abstracting basic functions, comparing currencies such as vector storage and LLM model switching, allowing developers to flexibly adapt to different needs and avoid repeated development; Memory and communication capabilities. Although some frameworks have invested a lot of resources to improve this capability, over-intelligence at the current stage may not be practical, but instead increases complexity.
The following is a detailed comparison of the mainstream cryptographic AI Agent frameworks in the market in various dimensions:
7. Eliza ($AI16Z): AI Agent Framework
Eliza ($AI16Z) holds a leading position in the AI agent market, attracting many developers with a market share of approximately 60% and a strong TypeScript ecosystem. Its GitHub project has accumulated more than 6,000 Stars and 1.8K Forks, fully demonstrating the high degree of community participation. Eliza is known for its multi-agent system and cross-platform integration, and supports mainstream social platforms such as Discord, X (Twitter), and Telegram, making it an important player in the field of social AI and community AI. With its broad ecological foundation, Eliza has excellent adaptability in areas such as social interaction, marketing and AI agent development.
In terms of technical architecture, Eliza has multi-agent system capabilities, allowing different AI roles to share the runtime environment and achieve more complex interaction patterns. Its Retriev-Augmented Generation (RAG) technology gives AI long-term contextual memory capabilities, allowing it to maintain consistency in continuous conversations. In addition, the plug-in system supports extended functions such as voice, text, and multimedia parsing, further enhancing the flexibility of application scenarios. Eliza is also compatible with multiple LLM providers such as OpenAI and Anthropic, and can provide efficient AI computing capabilities whether deployed in the cloud or locally. With the launch of the V2 message bus, Eliza’s scalability will be further optimized and suitable for medium and large social AI applications.
Although Eliza has performed well in the market, it still faces certain challenges. Its multi-agent architecture may cause complexity issues and increase system resource overhead in high concurrency scenarios. In addition, the current version is still in the early development stage, and stability and optimization are still improving. For developers, the learning curve of multi-agent systems is relatively steep and requires a certain amount of technical accumulation to make full use of their advantages. In the future, with the continued contribution of the community and the release of V2 versions, Eliza is expected to achieve further breakthroughs in scalability and stability.
8. GAME ($VIRTUAL): AI Agent Framework
GAME ($VIRTUAL) focuses on games and the metaverse. With low-code/no-code integration, GAME significantly lowers the developer threshold, allowing it to quickly build and deploy intelligent agents. At the same time, relying on the $VIRTUAL ecosystem, GAME has formed a strong developer community, accelerating product iteration and ecological expansion. Its core advantage lies in providing efficient game AI solutions that make it easier to implement functions such as programmatic content generation, dynamic adjustment of NPC behavior, and on-chain governance.
In terms of technical architecture, GAME adopts an API + SDK model to provide a convenient integration method for game studios and metaverse developers. Its agent prompt interface optimizes the interaction between user input and AI agents, making intelligent behavior within the game more natural. The strategic planning engine divides the logic of the AI agent into high-level goal planning and low-level policy execution, making it more adaptable in complex game environments. In addition, GAME also supports blockchain integration, which enables decentralized agent governance and on-chain wallet operations, giving it a unique advantage in the field of Web3 games.
GAME has optimized performance for high-concurrency game scenarios and performed well in handling game engine constraints. However, its overall performance is still affected by the complexity of agent logic and blockchain transaction overhead, which may pose challenges to real-time interactivity. At the same time, as an AI agent framework focusing on games and the metaverse, GAME has limited versatility in other fields. In addition, the complexity of blockchain integration still needs to be optimized to reduce development costs and further attract a wider developer community.
9. Rig ($ARC): AI Agent Framework
Rig ($ARC) has a 15% market share in the enterprise-level AI agent market. Its high-performance and modular architecture based on the Rust language makes it perform well in high throughput and low latency scenarios, especially suitable for Solana and other high-performance blockchain ecosystem. With its strong system stability and efficient resource management, Rig has become an ideal choice for on-chain financial applications, large-scale data analysis and distributed computing tasks. Its architectural design emphasizes scalability, allowing enterprise users to flexibly deploy AI agents in complex data environments and improve computing efficiency.
In terms of technical architecture, Rig adopts the Rust workspace structure to ensure modularity and readability of the code while improving the extensibility of the system. Its provider abstraction layer supports seamless integration with multiple major LLM providers such as OpenAI and Anthropic, allowing developers to switch models freely. Rig also supports vector storage and is compatible with back-end databases such as MongoDB and Neo4j, improving the efficiency of context retrieval. In addition, Rig has a built-in agent system, combined with RAG models and tool optimization capabilities, allowing it to perform complex task automation, suitable for high-performance computing and intelligent data processing scenarios.
Rig relies on Rust’s asynchronous runtime to achieve excellent concurrency performance and can scale to high-throughput enterprise-class workloads. However, Rust itself has a steep learning curve, which may cause certain entry barriers for some developers. In addition, Rig’s developer community is relatively small and its ecological driving force needs to be strengthened. Despite this, with the growing demand for Web3 and high-performance computing, Rig still has broad market potential and is expected to further increase market penetration by optimizing the developer experience and enhancing community building in the future.
10. ZerePy ($ZEREBRO): AI Agent Framework
ZerePy ($ZEREBRO) has a 5% market share in creative content and social media automation, with a total market value of US$300 million. Its core advantage lies in its community-driven innovation ecosystem, which has enabled it to accumulate loyal user groups in application scenarios such as NFT, digital art and social content automation. By lowering the development threshold for AI agents, ZerePy enables content creators and community operators to easily deploy intelligent agents to automate content creation, social interactions and community management, and enhance user engagement and content influence.
In terms of technical architecture, ZerePy is based on the Python ecosystem to provide AI/ML developers with a friendly development environment. At the same time, it uses the modular Zerebro backend to achieve proxy autonomy for social tasks. Its social platform integration features optimize Twitter-like interactions, allowing agents to automatically complete tasks such as publishing, replying, and retweets, enhancing the automation capabilities of social media. In addition, ZerePy combines a lightweight architectural design to make it more suitable for the AI agent needs of individual creators and small communities without having to bear high computing costs.
ZerePy performs well in social interactions and creative content generation, but its scalability is mainly suitable for small-scale communities and is less suitable for high-intensity enterprise-level tasks. At the same time, due to its concentrated application scope, its applicability outside the creative field still needs to be further verified. For scenarios that require more complex creative output, ZerePy may require additional parameter tuning and model optimization to meet broader market needs. With the development of the creative economy, ZerePy is expected to further expand its application scenarios in the direction of NFT generation and personalized social agents in the future.
8. AI Launchpad
AI Launchpad not only provides customized growth paths for emerging projects, covering technical support, funding raising, marketing and cooperation opportunities with industry experts, but also helps projects quickly integrate into the global AI community through its extensive cooperation network.
11. Vvaifu: The first AI Launchpad on the Solana chain
vvaifu.fun is the first AI agent Launchpad based on the Solana chain, allowing users to create, manage and trade AI agents without any coding skills. The platform allows each AI agent to have exclusive tokens, thus forming a decentralized ecosystem. Users can not only co-own these agents, but also interact with AI-driven assets. The platform supports independent interaction of agents on social media platforms such as Twitter, Discord, and Telegram, and has on-chain wallet management functions, which greatly enhances its practicality in various application scenarios.
vvaifu.fun’s business model is based on its unique token economy model. The platform’s main token,$VVAIFU, is the first AI proxy token launched on the Dasha platform. It has deflation characteristics and burns a certain amount of $VAIFU whenever an agent is created or a function is unlocked. In addition, the platform has designed a number of burning mechanisms to ensure the stability of the token value, including burning 750 $VAIFU when the agent is created, consuming $VAIFU and SOL fees when the function is unlocked. Each initiating agent will also allocate 0.90% of the new agent tokens to community funds or directly to the team vault, thereby promoting community participation and ecological construction.
The platform’s community participation mechanism enhances user interaction and governance rights. Token holders can accumulate 0.90% of agent-initiated supplies through community wallets and vote on the use of these resources. vvaifu.fun has also set a platform transaction fee of 0.009 SOL, which provides sustainable financial support for the operation of the platform. Through these mechanisms, vvaifu.fun provides creators and users of AI agents with a comprehensive decentralized interactive platform, which not only promotes the development of creative projects, but also inspires active participation from the global community.
12. Clanker: AI reply robot
Clanker is a Farcaster-based AI reply robot designed specifically for users to create and deploy memecoins and tokens. Through the platform, users can create their own tokens simply by interacting with Clanker. Users only need to mark @ clicker on Farcaster to tell the robot what token they need, and provide information such as name, code, image and supply quantity. Clanker generates and provides tracking links in one minute, and eventually deploys the token to Uniswap v3. Although there is no initial liquidity, users need to manually add liquidity to price the token.
The technical architecture behind Clanker works through Next.js middleware in conjunction with LLM such as Anthropic’s Claude or ChatGPT. When a user initiates a request on Farcaster, the message is forwarded to the LLM, which executes decision logic based on the provided context to decide on the deployment of the token. This process reflects how Clanker uses AI technology to simplify the process of user generation and deployment of tokens, fully combines social platforms and blockchain technology to provide users with a convenient token creation experience.
As a platform, Clanker not only simplifies the creation process, but also deeply integrates with Uniswap v3, allowing users to deploy new tokens directly to decentralized exchanges. This process increases the speed of memecoins and token issuance, and also supports the provision of strategic value to the ecosystem through components such as Telegram robots, DEXs, and aggregators, thereby driving the growth of transaction volume on the chain. As the number of tokens increased, Clanker participated in a significant increase in transaction volume, helping users take advantage of low transaction fees and fast confirmation times, and promoting the circulation of on-chain assets such as Solana and Base.
Key conclusions
Technology drivers and infrastructure form the core of the AI agent project, ensuring efficient operation and supporting large-scale expansion through advanced programming languages and innovative algorithms. At the same time, the high-performance blockchain platform provides excellent transaction processing capabilities and multi-chain compatibility, allowing AI agents to interact seamlessly on different chains and promoting continuous optimization and upgrading of the technical foundation.
Payment and transaction infrastructure is a key pillar in the development of the AI agent ecosystem. The stablecoin payment system ensures transaction stability and liquidity and improves the interaction efficiency between AI agents and users. Decentralized autonomous trading systems achieve more efficient and secure automated transactions by eliminating human intermediaries. In addition, innovative reward and governance mechanisms, such as “proof of contribution” and “proof of cooperation”, promote AI agent collaboration and resource sharing, and ensure long-term healthy ecological development through a sound governance system.
Outlook and challenges
The necessity of AI Agent tokens is often questioned, mainly because they do not directly enhance the agent’s functions or bring obvious advantages. Many people believe that AI Agent tokens are similar to tokens in Web3 games, and the latter may not be of substantial help to the core functions of the project. Therefore, some investors may blindly follow the AI boom and ignore the actual value of these tokens, which brings high risks and may even lead to fraud. For such projects, some believe that they attract investors who don’t know the truth by disguising their legitimacy, especially when compared to meme coins, which may promise too many unrealized functions.
If a project uses tokens as the primary driver, it may result in a sacrifice of core functions and experiences, especially in non-gambling games and services. Tokens should be used as an additional element rather than a dominant factor. Many successful projects have proved that truly effective applications should focus on user experience and create high-quality products, rather than just relying on the economic incentive mechanism of tokens to attract users.
The integration of AI and DeFi will be an important trend in the future. It is expected that 80% of DeFi transactions will be completed by AI Agents, and promoters such as Modenetwork and Gizatech are also actively promoting this development. At the same time, the role of AI Agents in protocol governance will be further expanded and may even trigger AI-driven governance attacks. In addition, security-class AI Agents are expected to play an important role in protecting protocols from attacks, similar to the protection features provided by HypernativeLabs and FortaNetwork. As infrastructure continues to expand, the development of Trusted Execution Environment (TEE) and the core position of decentralized computing will enhance the resilience of AI Agents. In addition, the explosion of the AI data market will also promote the growth of data payments between AI, and projects such as Nevermined.io have laid the foundation for this.
