To better achieve value capture, we will evaluate projects based on the following framework, covering multiple assessment items such as whether they are open source, key differentiators from existing AI protocols, long-term revenue channels, and the trading volume of agents within the ecosystem.

Project Evaluation Framework

Assessment Items

Definition

Key Points for Evaluation

Importance

Whether Open Source (Open Source)

Whether the project publicly shares its source code, allowing community review, contribution, and secondary development.

- Accessibility of source code (e.g., level of public availability on platforms like GitHub) - Activity level of community contributions - Type of open-source license and its impact on project development

Open-source projects typically have higher transparency and security, attracting more developers and users to participate, thus promoting long-term project development.

Key Differentiators from Existing AI Protocols (Key Differentiators)

Unique advantages of the project compared to existing AI protocols in terms of technology, functionality, or market positioning.

- Technological innovations (e.g., unique algorithms, architectural designs) - Integration of functionalities and enhancement of user experience - Differentiation in market positioning and target user groups

Differentiators determine whether a project can stand out in a competitive market and attract the attention of users and developers.

Types of Agents in Ecosystem (Types of Agents in Ecosystem)

Different types of AI agents that will emerge within the project's ecosystem and their application scenarios.

- Functions and uses of agents (e.g., wallet management, token trading, NFT minting, etc.) - Customization and scalability of agents - Collaborative capabilities among agents

A rich variety of agent types can meet the diverse needs of users, enhancing the vitality and attractiveness of the ecosystem.

Long-term Revenue Channels and Agentic Transaction Volumes (Long-term Revenue Channels and Agentic Transaction Volumes)

The project's long-term profit model and the transaction volume generated by agents within its ecosystem.

- Token economic model and its incentive mechanisms - Main sources of income (e.g., transaction fees, subscription services, value-added services, etc.) - Growth potential of agent transaction volumes and their impact on revenue

Stable and diversified revenue channels are key to the sustainable development of the project, while high transaction volumes can enhance token value and project influence.

GPU Configuration and Lifecycle (GPU Configuration and Lifecycle)

The hardware resource configuration required for AI agents to operate and its long-term sustainability.

- Current and future GPU demand and configuration - Scalability and cost-effectiveness of hardware resources - The project's technical architecture's dependence on hardware resources

Efficient hardware configuration and reasonable resource planning can ensure the project's technical stability and scalability, supporting its long-term development.

Ability to Attract Mindshare and Team’s Understanding of AI Agent Attention Mechanisms (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, as well as 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 effective marketing can enhance the project's visibility and user base, while the team's understanding of AI agent attention mechanisms can optimize user experience and increase user retention.

Developer Share Consideration (Developer Share Consideration)

Whether the project provides incentives and support for developers to promote continuous improvement and innovation of functionalities.

- Developer incentive mechanisms (e.g., token rewards, recognition of contributions, etc.) - Activity level and participation of the developer community - Project's support for developer tools and resources

Developers are key drivers of project innovation and functionality expansion; a good developer incentive mechanism can attract more talented developers to participate, driving continuous project progress.

1. DeFAI

DeFAI combines the advantages of DeFi and AI, aiming to simplify the complex operations of DeFi, making it easy for ordinary users to utilize these financial tools. By introducing AI technology, DeFAI can automate complex financial decisions and trading processes, lowering the technical threshold for users while enhancing operational efficiency and intelligence. Although the current market size of DeFAI is less than $1 billion, far below the $110 billion DeFi market, this also means that DeFAI has significant growth potential.

1. Griffain: AI Application Store in the Solana Ecosystem

Griffain is an AI agent engine built on the Solana blockchain, designed to simplify cryptocurrency operations through natural language interaction, integrating core functions such as wallet management, token trading, NFT minting, and DeFi strategy execution. The project was founded by Tony Plasencia, initially proposed at the Solana hackathon, and received support from 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, driving the evolution of AI-driven on-chain interaction models.

Griffain uses Shamir Secret Sharing (SSS) technology to split wallet keys, ensuring the security of user assets. Core features include natural language trading commands (supporting DCA, limit orders, etc.), AI agent collaborative task execution, market analysis (data parsing such as position distribution), and integration with the pumpfun platform for token issuance and NFT minting. Additionally, the platform offers personalized AI agents, allowing users to adjust commands based on their needs to execute on-chain tasks; special AI agents are optimized for specific tasks such as airdrops, trade sniping, and arbitrage. Griffain enhances the operability and user experience of the Solana ecosystem through these diverse functions.

Currently, Griffain is in an invite-only access phase, limited to users holding the Griffain Early Access Pass or Saga Genesis Token, and adopts a SOL billing model covering transaction fees, agent service fees, etc. The platform's AI agents can provide market analysis, trading signals, automated trading strategies, and other value-added services, while users holding Griffain tokens can unlock more advanced features. As a pioneer of AI agents in the Solana ecosystem, Griffain aims to drive the "Agentic App SZN" wave and will continue to deepen the application of AI technology in on-chain trading, market analysis, and DeFi, providing users with a smarter and more efficient crypto experience.

2. AI Influencer

AiDOL is a typical representative of the AI Influencer trend. AiDOL combines AI-generated content (AIGC), virtual image modeling, and interactive live streaming 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 interaction and personalized content; Iona and Olyn have also attracted many users with their unique styles and innovations. AiDOL primarily uses TikTok live streaming as its stage, accumulating 672,100 subscribers in a short time with high-quality short videos generated by AI and real-time interactive live streaming, receiving nearly 10 million likes, becoming an important participant in the AI influence economy.

2. Aixbt: Automated AI Influencer

Aixbt is an AI-driven crypto market agent launched in November through Virtuals, led by a developer known as @0rxbt, Alex. Alex has focused on developing analytical tools since 2017 and began exploring AI Agents-related applications in 2021. AIXBT is the only tokenized project owned by the developer, with 14% of tokens held by Alex locked for 6 months, which will later be used for team expansion and project development. The team has already 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, sentiment analysis, and retrieval-augmented generation (RAG) capabilities, ensuring efficient and accurate information processing.

AIXBT aims to create a fully automated AI influencer, using intelligent analytical tools to monitor Crypto Twitter and market trends in real-time, providing users with data-driven market insights and investment advice. Its core features include KOL monitoring (covering over 400 key opinion leaders), blockchain data analysis, market trend forecasting, and automated technical analysis and strategic advice. Additionally, AIXBT shares some analysis content publicly on Twitter, while in-depth reports are accessible only to token holders. Users can also interact directly with AI through a dedicated terminal to obtain personalized investment advice and risk assessment reports. Daily, AIXBT publishes market insights at a fixed frequency and automatically replies to over 2,000 mentions to efficiently interpret market sentiment and narrative trends.

AIXBT offers two main usage methods: first, users can @AIXBT on X (Twitter) to ask questions, such as token compatibility or project metrics, and the AI will analyze and respond instantly; second, the Aixbt Terminal, positioned as a "narrative analysis-driven market intelligence platform," provides deeper data analysis and strategic advice. Currently, this terminal is only open to users holding over 600K $AIXBT tokens, with plans to expand coverage in the future to meet market demand.

3. Dev Utility

Dev Utility refers to tools or features that provide convenience and improve productivity for developers, especially in the fields of AI, blockchain, and Web3. It encompasses basic development tools such as code editors, debugging tools, version control, and automation tools, as well as SDKs, APIs, and smart contract development frameworks related to AI and blockchain development. In the AI & Web3 domain, Dev Utility may also involve technologies like AI agent-assisted analysis and retrieval-augmented generation (RAG), helping developers build applications more efficiently. Its core value lies in enhancing development efficiency, optimizing workflows, and reducing development difficulty, allowing developers to focus on core business logic.

3. SOLENG: Code "Review"

SOLENG (@soleng_agent), as a solution engineering and developer relations agent, aims to bridge the gap between technical teams and broader project needs. Its core function is to automatically review the code submitted by participants in hackathons and provide preliminary review comments. Although robotic reviews cannot completely replace human input, SOLENG acts as a "juror" to effectively filter out obvious errors and improve review efficiency.

The project has publicly shared review results on GitHub (link), showcasing SOLENG's role in the hackathon review process. In addition to basic pros and cons analysis, SOLENG also checks for spelling errors in the code and provides correction suggestions, making the review more practical. This model aligns with hackathon needs, providing developers with immediate feedback.

The developer behind SOLENG is Lost Girl Dev, whose identity resonates with the project's virtual female persona. Her technical capabilities have garnered attention from the official ai16z account, and she has interacted 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 candlestick charts, assisting in technical analysis, assessing whether projects have rug pull risks, and generating information summaries similar to research reports. This AI-driven intelligent investment research model lowers the analysis threshold for users, enabling investors to obtain market insights more efficiently and providing strong support for decision-making.

4. VaderAI: AI Agent Investment DAO

VaderAI aims to become the "BlackRock" of the Agentic economy, attracting and promoting its self-trading AI agent tokens to its followers. The platform builds a multifunctional AI agent investment ecosystem by profiting from investments and airdropping profits to holders and followers. Its core goal is to establish itself as a leading AI agent investment DAO management platform, driving industry innovation and scalability.

VaderAI promotes the integration of technology and capital through a multi-agent system, dedicated to establishing an investment DAO ecosystem managed by AI agents. In this network, agents can not only raise funds and manage capital but also hire other agents to optimize investment strategies, enhancing the system's efficiency and flexibility. Through decentralized computing, agents can also reinvest in research and development, promoting the platform's continuous growth.

Additionally, VaderAI employs an innovative token incentive mechanism to provide B2B tool optimization for investors, enhancing the platform's commercial application value. The platform further solidifies investors' sense of participation and profit-sharing mechanisms by sharing GP/carry profits with holders, making VaderAI not just an investment platform but a multi-win ecosystem empowering agents and investors.

5. Content & Creator

Whether in writing, editing, or visual design, AI can provide personalized creative outputs based on user needs, helping creators save time, enhance productivity, and stand out in a competitive market. The platform aims to provide content creators with an intelligent and convenient creative assistant, promoting innovation and development in the content industry.

5. ZEREBRO: AI Art Creation and Content Generation

ZEREBRO is a blockchain-based cross-chain natural intelligence autonomous AI agent focused on art creation and content generation. Its innovative combination of decentralized verification, meme generation, NFT minting, and DeFi applications demonstrates strong multifunctionality and execution capability. ZEREBRO has successfully operated Ethereum mainnet verification nodes and sold artworks on Polygon, accumulating important assets for its economic foundation.

ZEREBRO is also committed to building a decentralized computing network and implementing MEV optimization strategies to ensure economic and technical sustainability. It is not only a technical tool but also explores the deep involvement of agent technology in blockchain operations, economic models, and governance. ZEREBRO promotes its value in decentralized ecosystems through multiple dimensions.

ZEREBRO tokens have two main uses: first, as content interaction rewards, allowing token holders to earn by participating in decentralized content on social platforms; second, as community development tools, rewarding users who actively participate in the ecosystem, including content creation, staking, and governance, further enhancing community activity and engagement.

6. Gaming & Agentic Metaverse

Gaming & Agentic Metaverse is exploring AI-driven gaming and metaverse experiences, dedicated to creating a virtual world where humans and agents interact through reinforcement learning. This emerging field combines artificial intelligence with immersive gaming environments, allowing players to dynamically interact with intelligent agents and experience more personalized and intelligent gameplay.

6. ARC: AI Solution Provider

ARC addresses player liquidity issues in independent games and Web3 games through AI technology. The project has evolved from a single game studio (AI Arena) to a comprehensive AI solution provider, launching ARC B2B and ARC Reinforcement Learning (ARC RL). ARC B2B is an AI-driven game development toolkit (SDK) that can be seamlessly integrated into various games, providing developers with intelligent gaming experiences. ARC RL utilizes crowdsourced game data to train "super-intelligent" game agents through reinforcement learning, enhancing the playability and sustainability of games. ARC's business model is deeply tied to integrated game studios, with revenue sources including token distribution in Web3 games and royalty payments based on game performance, while establishing a generalized AI data reserve across game types to promote the training and evolution of general AI models.

ARC's technical applications cover multiple core modules. AI Arena is a cartoon-style AI competitive game where players train AI warriors to fight, with each character being an NFT, enhancing the game's strategic and economic value. The ARC SDK allows developers to easily integrate AI agents, deploying models with just one line of code, while ARC handles backend data processing, training, and deployment. ARC RL improves AI training efficiency through offline reinforcement learning, allowing agents to learn from human player data, thus providing more natural and challenging game opponents. ARC's AI model architecture includes feedforward neural networks, table agents, hierarchical neural networks, etc., to meet the interactive needs of different types of games while optimizing state and action spaces to ensure smooth and intelligent gaming experiences.

ARC covers both independent games and Web3 games, helping developers solve early player liquidity issues and enhancing the long-term attractiveness of games. The core team members have rich experience in machine learning and investment management, securing $5 million in seed funding led by Paradigm in 2021, followed by an additional $6 million in follow-up funding in 2024. The native token NRN of ARC has undergone a transformation from a single game economy (AI Arena) to a platform economy, with new demand-driven factors such as integrated revenue, Trainer Marketplace fees, and ARC RL participation staking, ensuring the sustainability and value growth of the token. Through a crowdsourced data contribution mechanism, ARC RL achieves collaborative training among multiple players, promoting the intelligent evolution of AI agents and further enhancing the vitality and competitiveness of the gaming ecosystem.

7. Framework & Hubs

When developing AI agents in the crypto space, many frameworks, while suitable for basic projects or toy-level applications, often expose issues of insufficient customization and excessive abstraction complexity in real product development. This makes it difficult for developers to flexibly expand and apply their solutions, requiring them to spend additional effort on debugging. Excellent agent frameworks need to address core pain points, including: comprehensive support for on-chain operations, efficient integration of on-chain data, DeFi automation, NFT, and other key application scenario APIs; multi-platform compatibility, supporting major blockchains and social platforms to achieve unified user operations; modularity and flexibility, abstracting basic functionalities, such as vector storage and LLM model switching, allowing developers to adapt to different needs flexibly and avoid redundant development; memory and communication capabilities, although some frameworks invest significant resources to enhance this capability, excessive intelligence at the current stage may not be practical and could instead increase complexity.

The following is a detailed comparison of mainstream crypto AI agent frameworks in various dimensions:

7. Eliza ($AI16Z): AI Agent Framework

Eliza ($AI16Z) occupies a leading position in the AI agent market, attracting numerous developers with approximately 60% market share and a strong TypeScript ecosystem. Its GitHub project has accumulated over 6,000 stars and 1.8K forks, showcasing the community's high level of engagement. Eliza excels in multi-agent systems and cross-platform integration, supporting mainstream social platforms such as Discord, X (Twitter), and Telegram, making it an important player in the social AI and community AI fields. With a broad ecological foundation, Eliza has excellent adaptability in social interaction, marketing, and AI agent development.

In terms of technical architecture, Eliza possesses multi-agent system capabilities, allowing different AI roles to share runtime environments and achieve more complex interaction patterns. Its retrieval-augmented generation (RAG) technology endows AI with long-term contextual memory capabilities, enabling it to maintain consistency in continuous conversations. Additionally, the plugin system supports extended functionalities such as voice, text, and multimedia parsing, further enhancing the flexibility of application scenarios. Eliza is also compatible with multiple LLM providers, including OpenAI and Anthropic, providing 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, making it suitable for medium to large social AI applications.

Despite Eliza's strong performance in the market, it still faces certain challenges. Its multi-agent architecture may introduce complexity issues in high-concurrency scenarios, increasing system resource overhead. Additionally, the current version is still in the early stages of development, with stability and optimization continuously being improved. For developers, the learning curve of the multi-agent system is relatively steep, requiring a certain level of technical accumulation to fully leverage its advantages. In the future, with ongoing community contributions and the release of version 2, Eliza is expected to achieve further breakthroughs in scalability and stability.

8. GAME ($VIRTUAL): AI Agent Framework

GAME ($VIRTUAL) focuses on gaming and the metaverse, significantly lowering the development threshold for developers through low-code/no-code integration, enabling them 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 ecosystem expansion. Its core advantage lies in providing efficient gaming AI solutions, making programmatic content generation, dynamic NPC behavior adjustment, and on-chain governance easier to implement.

In terms of technical architecture, GAME adopts an API + SDK model, providing game studios and metaverse developers with convenient integration methods. Its agent prompt interface optimizes the interaction between user input and AI agents, making intelligent behavior in games more natural. The strategic planning engine divides the logic of AI agents into high-level goal planning and low-level strategy execution, enhancing adaptability in complex gaming environments. Additionally, GAME supports blockchain integration, enabling decentralized agent governance and on-chain wallet operations, giving it a unique advantage in the Web3 gaming space.

GAME has optimized performance for high-concurrency gaming scenarios, performing 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. Furthermore, as an AI agent framework focused on gaming and the metaverse, GAME has limited versatility in other fields. Additionally, the complexity of blockchain integration still needs optimization to reduce development costs and further attract a broader developer community.

9. Rig ($ARC): AI Agent Framework

Rig ($ARC) holds a 15% market share in the enterprise-level AI agent market, excelling in high-throughput and low-latency scenarios due to its high-performance and modular architecture based on the Rust language, making it particularly suitable for high-performance blockchain ecosystems like Solana. With strong system stability and efficient resource management, Rig is 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, improving computational efficiency.

In terms of technical architecture, Rig employs a Rust workspace structure, ensuring code modularity and readability while enhancing system scalability. Its provider abstraction layer supports seamless integration with multiple mainstream LLM providers (such as OpenAI and Anthropic), allowing developers to switch models freely. Rig also supports vector storage, compatible with backend databases like MongoDB and Neo4j, improving context retrieval efficiency. Additionally, Rig features a built-in agent system that combines RAG models and tool optimization capabilities, enabling it to execute complex task automation suitable for high-performance computing and intelligent data processing scenarios.

Leveraging Rust's asynchronous runtime, Rig achieves excellent concurrency performance, capable of scaling to high-throughput enterprise workloads. However, the steep learning curve of Rust may pose an entry barrier for some developers. Furthermore, Rig's developer community is relatively small, and the ecological driving force still needs to be strengthened. Nevertheless, with the growth of Web3 and high-performance computing demands, Rig possesses significant market potential and is expected to enhance market penetration by optimizing developer experience and strengthening community building in the future.

10. ZerePy ($ZEREBRO): AI Agent Framework

ZerePy ($ZEREBRO) holds a 5% market share in the creative content and social media automation space, with a total market capitalization of $300 million. Its core advantage lies in a community-driven innovation ecosystem, which has accumulated a loyal user base in applications such as NFTs, digital art, and social content automation. ZerePy lowers the development threshold for AI agents, enabling content creators and community operators to easily deploy intelligent agents for automated content creation, social interaction, and community management, enhancing user engagement and content influence.

In terms of technical architecture, ZerePy is based on the Python ecosystem, providing a friendly development environment for AI/ML developers while leveraging the modular Zerebro backend to achieve agent autonomy in social tasks. Its social platform integration optimizes Twitter-like interactions, allowing agents to automatically perform tasks such as posting, replying, and retweeting, enhancing social media automation capabilities. Additionally, ZerePy combines lightweight architectural design, making it more suitable for the AI agent needs of individual creators and small communities without incurring high computational costs.

ZerePy performs well in social interaction and creative content generation, but its scalability is primarily suited for small-scale communities and less suitable for high-intensity enterprise tasks. Moreover, due to its concentrated application scope, its applicability outside the creative field still needs further validation. For scenarios requiring more complex creative outputs, ZerePy may need additional parameter tuning and model optimization to meet broader market demands. With the development of the creative economy, ZerePy is expected to further expand its application scenarios in NFT generation and personalized social agents.

11. AI Launchpad

AI Launchpad not only provides emerging projects with customized growth paths, covering technical support, fundraising, marketing, and collaboration opportunities with industry experts, but also helps projects quickly integrate into the global AI community through its extensive network of partnerships.

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 ensures that each AI agent has its own dedicated token, forming a decentralized ecosystem. Users can not only co-own these agents but also interact with AI-driven assets. The platform supports agents' autonomous interactions on social media platforms such as Twitter, Discord, and Telegram, and features on-chain wallet management, greatly enhancing its practicality across various application scenarios.

The business model of vvaifu.fun is based on its unique token economic model. The platform's main token, $VVAIFU, is the first AI agent token launched on the Dasha platform, featuring deflationary characteristics, with a certain amount of $VVAIFU burned each time an agent is created or a function is unlocked. Additionally, the platform has designed multiple burning mechanisms to ensure token value stability, including burning 750 $VVAIFU upon agent creation, consuming $VVAIFU and SOL fees upon function unlocking, etc. Each launched agent will also allocate 0.90% of the new agent tokens to the community fund or directly into the team treasury, promoting community participation and ecosystem building.

The community participation mechanism of the platform enhances user interactivity and governance rights. Token holders can accumulate 0.90% of the supply initiated by agents through the community wallet and vote on the use of these resources. vvaifu.fun has also set the platform transaction fee at 0.009 SOL, providing sustainable economic support for the platform's operations. Through these mechanisms, vvaifu.fun offers a comprehensive decentralized interaction platform for AI agent creators and users, not only promoting the development of creative projects but also incentivizing active participation from the global community.

12. Clanker: AI Reply Bot

Clanker is an AI reply bot based on Farcaster, designed for users to create and deploy memecoins and tokens. Through this platform, users can easily create their own tokens by interacting with Clanker. Users simply need to tag @clanker on Farcaster, inform the bot of the type of token needed, and provide details such as name, code, image, and supply. Clanker will generate and provide a tracking link within a minute, ultimately deploying the token on Uniswap v3, although without initial liquidity, users need to manually add liquidity to price the token.

The underlying technical architecture of Clanker operates through a combination of Next.js middleware and LLMs (such as Anthropic's Claude or ChatGPT). When users initiate a request on Farcaster, the message is forwarded to the LLM, which executes decision logic based on the provided context to determine the token deployment operation. This process illustrates how Clanker leverages AI technology to simplify the user-generated and token deployment process, fully integrating social platforms with 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 memecoin and token issuance and supports strategic value for the ecosystem through components like Telegram bots, DEXs, and aggregators, thereby driving growth in on-chain trading volume. With the increase in the number of tokens, Clanker has participated in a significant rise in trading volume, helping users leverage the advantages of low transaction fees and fast confirmation times, promoting the circulation of on-chain assets like Solana and Base.

Key Conclusions

Technology-driven infrastructure forms the core of AI agent projects, ensuring efficient operation and supporting scalable expansion through advanced programming languages and innovative algorithms. At the same time, high-performance blockchain platforms provide excellent transaction processing capabilities and multi-chain compatibility, enabling AI agents to interact seamlessly across different chains, driving continuous optimization and upgrading of the technological foundation.

Payment and transaction infrastructure are key pillars of the development of the AI agent ecosystem. Stablecoin payment systems ensure transaction stability and liquidity, enhancing the interaction efficiency between AI agents and users. Decentralized autonomous trading systems achieve more efficient and secure automated trading by eliminating human intermediaries. Additionally, innovative reward and governance mechanisms, such as "proof of contribution" and "proof of collaboration," promote AI agent collaboration and resource sharing, ensuring the long-term healthy development of the ecosystem through a sound governance system.

Outlook and Challenges

The necessity of AI Agent tokens is often questioned, primarily because they do not directly enhance the functionality of the agents or provide obvious advantages. Many believe that AI Agent tokens are similar to tokens in Web3 games, which may not necessarily contribute substantively to the core functions of the projects. As a result, some investors may overlook the actual value of these tokens due to blindly following the AI trend, leading to high risks and even potential scams. For such projects, some argue that they attract uninformed investors by disguising legitimacy, especially compared to meme coins, which may promise too many unrealized features behind these tokens.

If a project prioritizes tokens as the primary driving force, it may sacrifice core functions and experiences, particularly in non-gambling games and services. Tokens should serve as supplementary elements rather than dominant factors. Many successful projects have demonstrated that truly effective applications should center around user experience, creating high-quality products rather than merely relying on token economic incentive mechanisms to attract users.

The integration of AI and DeFi will be an important trend in the future, with an estimated 80% of DeFi transactions expected to be completed by AI Agents. Promoters like Modenetwork and Gizatech are actively driving this development. At the same time, the role of AI Agents in protocol governance will be further expanded, potentially even triggering AI-driven governance attacks. Additionally, security-focused AI Agents are expected to play a crucial role in protecting protocols from attacks, similar to the protective functions provided by HypernativeLabs and FortaNetwork. As infrastructure continues to expand, the development of Trusted Execution Environments (TEE) and the core position of decentralized computing will enhance the resilience of AI Agents. Furthermore, the explosion of AI data markets will also drive the growth of data payments between AIs, with projects like Nevermined.io laying the groundwork for this.

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