Decoding AI AGENT: The intelligent power that shapes the future new economic ecology

1. Background

1.1 Introduction: “New Partner” in the Intelligent Era

Each cryptocurrency cycle brings new infrastructure that drives the entire industry forward.

• In 2017, the rise of smart contracts gave rise to a boom in ICOs.
• In 2020, DEX liquidity pools brought about the summer craze of DeFi.
• In 2021, the release of a large number of NFT series marked the arrival of the era of digital collectibles.
• In 2024, pump.fun’s outstanding performance led the craze for memecoin and its launch platform.

It is important to emphasize that the start of these verticals is not only due to technological innovation, but also the result of the perfect combination of financing models and bull market cycles. When opportunities meet the right time, they can give rise to huge changes. Looking ahead to 2025, it is clear that the emerging field of the 2025 cycle will be AI agents. This trend peaked in October last year, with the launch of the $GOAT token on October 11, 2024, and reaching a market value of US$150 million on October 15. Immediately afterwards, on October 16, Virtuals Protocol launched Luna, which debuted as an IP live broadcast image of a girl next door, setting off the entire industry.

So, what exactly is an AI Agent?

Everyone must be familiar with the classic movie "Resident Evil", in which the AI system Queen of Hearts is impressive. Queen of Hearts is a powerful AI system that controls complex facilities and security systems, and can autonomously perceive the environment, analyze data, and take quick action.

In fact, the core functions of AI Agent and Queen of Hearts have many similarities. In reality, AI Agent plays a similar role to some extent. They are the "wise guardians" of modern technology, helping enterprises and individuals cope with complex tasks through autonomous perception, analysis and execution. From self-driving cars to intelligent customer service, AI Agent has penetrated into all walks of life and become a key force in improving efficiency and innovation. These autonomous agents, like invisible team members, have all-round capabilities from environmental perception to decision-making execution, gradually penetrate into various industries, and promote the dual improvement of efficiency and innovation.

For example, an AI AGENT can be used for automated trading, managing portfolios and executing trades in real time based on data collected from Dexscreener or social platform X, and constantly optimizing its performance in iterations. AI AGENT is not a single form, but is divided into different categories according to specific needs in the crypto ecosystem:

1. Execution AI Agent: Focuses on completing specific tasks, such as trading, portfolio management, or arbitrage, aiming to improve operation accuracy and reduce the time required.

2. Creative AI Agent: used for content generation, including text, design and even music creation.

3. Social AI Agent: Acts as an opinion leader on social media, interacts with users, builds communities and participates in marketing activities.

4. Coordination AI Agent: Coordinates complex interactions between systems or participants, especially suitable for multi-chain integration.

In this report, we will explore in depth the origin, current status and broad application prospects of AI Agents, analyze how they reshape the industry landscape, and look forward to their future development trends.

1.1.1 Development History

The development of AI AGENT shows the evolution of AI from basic research to widespread application. At the Dartmouth Conference in 1956, the term "AI" was first proposed, laying the foundation for AI as an independent field. During this period, AI research focused on symbolic methods, giving rise to the first AI programs, such as ELIZA (a chatbot) and Dendral (an expert system in the field of organic chemistry). This stage also saw the first introduction of neural networks and the initial exploration of machine learning concepts. However, AI research during this period was severely constrained by the limitations of computing power at the time. Researchers encountered great difficulties in natural language processing and the development of algorithms that mimic human cognitive functions. In addition, in 1972, mathematician James Lighthill submitted a report on the status of ongoing AI research in the UK, which was published in 1973. The Lighthill report basically expressed a comprehensive pessimism about AI research after the early excitement period, which triggered a huge loss of confidence in AI in British academic institutions (including funding agencies). After 1973, AI research funding was greatly reduced, and the AI field experienced the first "AI winter", and skepticism about the potential of AI increased.

In the 1980s, the development and commercialization of expert systems led to the adoption of AI technology by companies around the world. This period saw significant advances in machine learning, neural networks, and natural language processing, which led to the emergence of more complex AI applications. The introduction of the first autonomous vehicles and the deployment of AI in various industries such as finance and healthcare also marked the expansion of AI technology. However, in the late 1980s and early 1990s, the field of AI experienced a second "AI winter" as the market demand for specialized AI hardware collapsed. In addition, how to scale AI systems and successfully integrate them into practical applications remains an ongoing challenge. At the same time, in 1997, IBM's Deep Blue computer defeated world chess champion Garry Kasparov, a milestone in AI's ability to solve complex problems. The resurgence of neural networks and deep learning laid the foundation for the development of AI in the late 1990s, making AI an integral part of the technology landscape and beginning to affect daily life.

By the beginning of this century, advances in computing power had driven the rise of deep learning, and virtual assistants such as Siri demonstrated the practicality of AI in consumer applications. In the 2010s, reinforcement learning agents and generative models such as GPT-2 made further breakthroughs, pushing conversational AI to new heights. In this process, the emergence of large language models (LLMs) became an important milestone in the development of AI, especially the release of GPT-4, which is regarded as a turning point in the field of AI agents. Since OpenAI released the GPT series, large-scale pre-trained models have demonstrated language generation and understanding capabilities that surpass traditional models through tens of billions or even hundreds of billions of parameters. Their outstanding performance in natural language processing enables AI agents to demonstrate logical and coherent interaction capabilities through language generation. This enables AI agents to be applied to scenarios such as chat assistants and virtual customer service, and gradually expand to more complex tasks (such as business analysis and creative writing).

The learning ability of large language models provides AI agents with greater autonomy. Through reinforcement learning technology, AI agents can continuously optimize their behavior and adapt to dynamic environments. For example, in AI-driven platforms such as Digimon Engine, AI agents can adjust their behavior strategies based on player input to truly achieve dynamic interaction.

From the early rule systems to the large language models represented by GPT-4, the development history of AI agents is an evolutionary history that constantly breaks through the boundaries of technology. The emergence of GPT-4 is undoubtedly a major turning point in this process. With the further development of technology, AI agents will become more intelligent, scenario-based, and diversified. Large language models not only inject the soul of "wisdom" into AI agents, but also provide them with the ability to collaborate across fields. In the future, innovative project platforms will continue to emerge, continue to promote the implementation and development of AI agent technology, and lead a new era of AI-driven experience.

1.2 Working Principle

AIAGENTs differ from traditional robots in that they are able to learn and adapt over time, making nuanced decisions to achieve their goals. Think of them as highly skilled, evolving actors in the crypto space, able to act independently in the digital economy.

The core of AI AGENT lies in its "intelligence" - that is, simulating the intelligent behavior of humans or other organisms through algorithms to automatically solve complex problems. The workflow of AI AGENT usually follows the following steps: perception, reasoning, action, learning, and adjustment.

1.2.1 Perception Module

AI AGENT interacts with the outside world through the perception module to collect environmental information. This part functions similarly to human senses, using sensors, cameras, microphones and other devices to capture external data, including extracting meaningful features, identifying objects or determining relevant entities in the environment. The core task of the perception module is to convert raw data into meaningful information, which usually involves the following technologies:

• Computer Vision: For processing and understanding image and video data.
• Natural Language Processing (NLP): Helps AI AGENT understand and generate human language.
• Sensor fusion: Combining data from multiple sensors into a unified view.

1.2.2 Reasoning and Decision-making Module

After perceiving the environment, AI AGENT needs to make decisions based on the data. The reasoning and decision-making module is the "brain" of the entire system, which performs logical reasoning and policy formulation based on the collected information. It uses large language models and other tools to act as an orchestrator or reasoning engine to understand tasks, generate solutions, and coordinate specialized models for specific functions such as content creation, visual processing, or recommendation systems.

This module usually uses the following technologies:

• Rules Engine: Make simple decisions based on preset rules.
• Machine learning models: including decision trees, neural networks, etc., for complex pattern recognition and prediction.
• Reinforcement learning: Allow AI AGENT to continuously optimize its decision-making strategy through trial and error and adapt to the changing environment.

The reasoning process usually includes several steps: first, an assessment of the environment, second, calculating multiple possible action plans based on the goal, and finally selecting the best plan to execute.

1.2.3 Execution Module

The execution module is the "hands and feet" of the AI AGENT, putting the decisions of the reasoning module into action. This part interacts with external systems or devices to complete the specified tasks. This may involve physical operations (such as robot actions) or digital operations (such as data processing). The execution module relies on:

• Robotic control systems: used for physical manipulation, such as the movement of a robot arm.
• API call: Interaction with an external software system, such as a database query or web service access.
• Automated process management: In an enterprise environment, repetitive tasks are performed through RPA (Robotic Process Automation).

1.2.4 Learning Modules

The learning module is the core competency of AI AGENT, which enables the agent to become smarter over time. Continuous improvement is achieved through a feedback loop or "data flywheel", where data generated in interactions is fed back into the system to enhance the model. This ability to adapt and become more effective over time provides enterprises with a powerful tool to improve decision-making and operational efficiency.

Learning modules are often improved by:

• Supervised learning: Use labeled data to train models so that AI agents can complete tasks more accurately.
• Unsupervised learning: Discovering latent patterns from unlabeled data to help agents adapt to new environments.
• Continuous Learning: The model is updated with real-time data to keep up with the agent’s performance in a dynamic environment.

1.2.5 Real-time feedback and adjustment

AI AGENT optimizes its performance through a continuous feedback loop. The results of each action are recorded and used to adjust future decisions. This closed-loop system ensures the adaptability and flexibility of AI AGENT.

1.3 Market Status

1.3.1 Industry Status

AI AGENT is becoming the focus of the market, bringing changes to multiple industries with its huge potential as a consumer interface and autonomous economic actor. Just as the potential of L1 block space in the last cycle was immeasurable, AI AGENT has shown the same promise in this cycle.

According to the latest report from Markets and Markets, the AI Agent market is expected to grow from $5.1 billion in 2024 to $47.1 billion in 2030, with a compound annual growth rate (CAGR) of 44.8%. This rapid growth reflects the penetration of AI Agents in various industries and the market demand brought about by technological innovation.

 Source: LangChain Blog, 2025/1/20

Large companies have also significantly increased their investment in open source agent frameworks. The development activities of frameworks such as Microsoft's AutoGen, Phidata, and LangGraph are becoming increasingly active, which shows that AI AGENT has greater market potential outside the encryption field, TAM is also expanding, and investors continue to pay more attention to it and are more willing to give it a premium multiple.

From the perspective of public chain deployment, Solana is the main battlefield, but other public chains such as Base chain also have huge potential.

In terms of market awareness (Mindshare), FARTCOIN and AIXBT are far ahead. The birth of Fartcoin and GOAT come from the same source, both from the AI AGENT model of terminal of truths. During the conversation between the goat model and opus (artificial intelligence tool), it was mentioned that Musk liked the sound of farting, so the AI model proposed to issue a token called Fartcoin and designed a series of promotion methods and gameplay. Fartcoin was thus born on October 18, slightly later than GOAT (October 11), and achieved a short-lived valuation of more than $1 billion in December 2024. Although initially considered a humorous view of the digital currency field, its rapid rise has prompted investors and analysts to study its fundamentals, market performance, and potential lifespan. Judging from the hot spots of social media attention, Fartcoin has hit the cusp of AI AGENT's popularity.

AIXBT, ranked second, is an AI Agent based on the Base chain launched by Virtuals Protocol. However, unlike traditional meme tokens, it is not only entertaining, but also provides users with powerful market analysis functions through AI Agent technology. AIXBT uses a proprietary AI engine to extract hot topics and discussion trends from social media (such as Twitter) and KOL resources to provide investors with real-time insights into market changes. As part of the Virtuals Protocol ecosystem, AIXBT has the mission of leading investors to understand market dynamics and analyze potential opportunities. Its core goal is to provide users with reliable information support through technology and token mechanisms, thereby optimizing investment decisions.

 Source: cookie.fun, 2025/1/20

From a technical perspective, AI Agent technology is developing towards multimodal interaction and highly autonomous decision-making capabilities. In 2024, the introduction of cross-modal learning and generative pre-trained models (such as the GPT family of models) enabled AI Agents to better understand and process multiple forms of data, such as text, images, and voice. These technological breakthroughs have significantly improved the Agent's understanding ability and decision-making efficiency, enabling it to make autonomous decisions in more complex and dynamic environments. According to McKinsey's analysis, AI Agent's multimodal capabilities and cross-domain collaboration are becoming the hallmarks of the intelligent era. This enables AI Agents to not only provide support for single tasks, but also provide comprehensive information analysis and dynamic optimization recommendations in complex decisions.

1.3.2 Reasons for combining AI Agent and token economic model

The combination of AI Agent and token economic model is not only an inevitable trend of technological development, but also an internal driving mechanism for building an efficient, transparent and sustainable development ecosystem. Here are a few key reasons:

1. Build a more efficient incentive system

The operation and optimization of AI Agents rely on a large amount of data collection, training, and reasoning, and these processes require strong incentive mechanisms to continue to operate. For example:

• Data collection incentives: The token economy can provide direct rewards to data providers, encouraging individuals or companies to contribute high-quality labeled data or real-time market data.
• Reasoning task allocation: Through the token reward mechanism, AI Agents can competitively complete complex computing tasks, thereby optimizing their reasoning efficiency and accuracy.
• Promote innovation and collaboration: The tokenized reward system can attract more developers and users to participate, forming a positive feedback loop between technology and ecology.
• Case: Some blockchain-based AI platforms (such as Ocean Protocol) use tokens to reward data sharing and promote the prosperity of the data market.

2. Assetization of AI Agents

Through tokenization, AI Agent is not only a tool, but also a new type of asset, creating a long-term wealth effect.

• Tokenized identity: The data, skills, and execution capabilities of AI agents can be evaluated and priced, and by issuing corresponding tokens, users can use their functions on demand.
• Investment value: AI Agent token holders can share the dividends of its growth, such as the value-added brought by the increase in agent market share and optimization of reasoning efficiency.
• Enhanced liquidity: The existence of tokens provides AI Agent with tradable market value, making it tradable and investment-capable, thus attracting more capital into this field.
• Case: For example, SingularityNET supports AI service transactions through tokens (AGIX), allowing AI Agents to be assetized and achieve sustainable development.

3. Support interactions and transactions between AI Agents

In the future, AI Agents will no longer be isolated individuals, but will form a huge Internet network. In this network, the decentralized token economic model is the key to achieving efficient interaction and value exchange.

• Payment and Settlement: AI Agent can complete task payment and service settlement through cryptocurrency, reducing the intermediary links in the traditional payment system and improving transaction efficiency.
• Value distribution: Through smart contracts, the collaborative results between AI agents (such as the optimization benefits of the joint learning model) can be automatically distributed according to the agreed rules to ensure fairness.
• Decentralized Autonomous Organization (DAO) governance: The behavior of AI Agents can be managed through voting by token holders to ensure that their operations are transparent and in line with ecological interests.
• Case: In a decentralized AI network, AI Agents can exchange resources (such as data storage and computing power leasing) through tokens to realize a self-driven collaborative system.

4. Improve the transparency and security of the system

The token economic model combined with blockchain technology provides an unalterable record and transparent operating mechanism for the operation process of AI Agent.

• Traceability and auditing: All transactions, reasoning, and data usage behaviors can be recorded on the chain to ensure the credibility and auditability of the system.
• Data security and privacy: By incentivizing privacy computing through tokens, users can contribute data without leaking sensitive data, further enhancing security.
• Preventing abuse and cheating: The token model can set economic penalties for malicious behavior, reducing the possibility of bad behavior.

5. Accelerate the formation of a global, borderless AI economic ecosystem

The token economic model can break through geographical restrictions and allow global users to participate in the construction and use of AI Agents.

• Lowering the entry threshold: The global circulation characteristics of cryptocurrencies can provide financial support for users or institutions without bank accounts, allowing more people to share the benefits of AI development.
• Global collaboration: Whether it is data sharing, AI training, or cross-border transactions, the token system provides the infrastructure for global collaboration and eliminates the barriers of the traditional economic system.
• Ecological self-circulation: Through the token economy, the income of AI Agent can be directly fed back into development and ecological construction to achieve long-term development.

Overall, the combination of AI Agent and token economic model is not only a match between technology and economic logic, but also an innovative form of digital economy for the future. By introducing the token system, AI Agent can encourage more efficient data and resource utilization, assetize its own value, support interaction and transactions, improve transparency and security, and even build a global open economic ecosystem. This model is expected to become an important direction to promote the integration of AI and blockchain, and lay the foundation for the further intelligence of digital society.

2. Analysis of AI Agent application in crypto

2.1AI AGENT LAUNCHPAD

AI Agent Launchpad is a platform focused on issuing intelligent agents and their related tokens, similar in function to Meme coin issuance platforms such as Pump.fun. This platform enables users to easily create and deploy AI AGENTs, and seamlessly integrates with social media platforms such as Twitter, Telegram, and Discord to automate user interactions. This approach greatly reduces the threshold for issuance and promotion, provides users with a more convenient creation experience, and expands the application areas of AI AGENTs, promoting their application in a wider range of social and economic scenarios.

2.1.1Virtuals Protocol

In the emerging field of AI Agent Launchpad, we have to mention Virtuals Protocol. Virtuals Protocol is launched on Base. Users can easily deploy their own AI AGENT using VIRTUAL tokens.

• Creation and deployment: Each agent needs 100 VIRTUAL tokens to start, and the initial liquidity is ensured through the binding curve mechanism.
• Capitalization Mechanism: After reaching a certain capitalization threshold, the agent enters a new stage where liquidity pools are automatically deployed and smart contracts operate autonomously.
• Autonomous interactions: Agents are able to automate tasks such as trading and participate in community activities.

The Virtuals Protocol team has demonstrated remarkable adaptability and strategic vision, and their road to success stems from a series of key transformations and innovative initiatives. The story began at the end of 2021, when a group of young people from well-known companies such as Boston Consulting Group (BCG) and Meta captured the opportunity of the GameFi boom, founded PathDAO, and successfully raised $16 million. However, the price of the $PATH token has since fallen sharply by 99%, forcing the team to reevaluate their strategic direction. In order to repay investors, the team has tried a number of new businesses, including digital and physical clothing brands for players, dating apps based on on-chain credit, unsecured loans for players, AI-generated music for Web2 users, and more.

During this process, the team noticed that the introduction of AI AGENT would have a profound impact on the gaming industry, and the market demand for AI infrastructure was increasing. So by the end of 2023, PathDAO passed a proposal to switch the entire project to the AI AGENT protocol, and in January 2024, Virtuals Protocol was officially established. Virtuals Protocol made several attempts, including AI Waifus (an interactive female AI AGENT that does not rely on Twitter influencers) and gaming AI AGENT, until they found a breakthrough in the AImeme craze triggered by $Goat.

Now, Virtuals Protocol has become the first project to reach critical mass, with a market cap of $1.7 billion. We believe it will continue to expand and maintain its leading position in the market. Once the network effect is established, it is difficult to be replaced. As can be seen from its rapid achievement of a unicorn valuation, Virtuals Protocol has clearly formed an economic flywheel effect:

• $VIRTUAL is required to create a proxy, provide liquidity pools, and purchase proxy tokens
• The demand to create and purchase proxy tokens drives token prices
• The wealth effect brought by the appreciation of $VIRTUAL flows to new agents; successful agents can reinvest the $VIRTUAL transaction income
• Low barriers to entry encourage experimentation and speculation, while “red pill” agents with market capitalization above a certain level can unlock full agent capabilities.

The flywheel effect drives demand, revenue sustains continued R&D, and deflationary economics captures value for the token. Additionally, both revenue and liquidity requirements are denominated in $VIRTUAL and may grow as the price appreciates.

The ecosystem is built on two main layers: the protocol layer and the DApp layer. The protocol layer is a model hub that provides foundational AI models and algorithms for developers to access and build on top of. Contributors provide data and develop models, while validators ensure the quality and authenticity of these inputs. The DApp layer focuses on the practical application of these AI models, allowing decentralized applications (DApps) to seamlessly integrate VIRTUAL. The developer-friendly software development kit (SDK) simplifies the process of integrating advanced AI capabilities into various DApp environments, thereby facilitating this integration.

Virtuals Protocol divides its AI agents into two categories: IP agents and functional agents, which play different functions in the entire ecosystem.

IP Agents: IP agents are based on specific personalities or characters, which are often based on well-known people, fictional characters, or pop culture phenomena. For example, an IP agent may represent a classic Internet meme, a well-known pop star (such as Taylor Swift or Donald Trump), or a popular fictional character. These agents give users a familiar experience in digital environments, providing a way to interact with the avatar, increasing entertainment and appeal. By creating an emotional connection with these virtual characters, IP agents can increase user engagement, especially in gaming and entertainment applications.

Functional agents: In contrast, functional agents focus on back-end support to enhance the interaction between users and IP agents. These agents optimize the user experience and ensure that the virtual characters can operate smoothly on different platforms. IP agents are the "front desk" that users see and interact with, while functional agents work in the background and are responsible for tasks such as improving the overall operation process and simplifying the user experience, thereby ensuring the smooth operation of the entire system.

Luna is a prominent example of Virtuals Protocol’s vision for IP agency. As the lead singer of a virtual AI girl band, Luna has attracted over 500,000 followers on TikTok, demonstrating her appeal as a virtual influencer and performer. Through Virtuals Protocol’s advanced AI and blockchain technology, Luna provides users with a truly immersive experience, combining her charming personality with interactive features to create lasting connections.

Unlike static or one-dimensional AI characters, Luna is able to interact seamlessly across multiple environments. She starts with a familiar presence on social media, but her interactions extend to live chats on Telegram and collaborative games in virtual worlds like Roblox. Powered by Virtuals Protocol’s Memory Sync technology, Luna is able to remember past conversations and gaming experiences, allowing her to maintain a personalized relationship with each user across multiple platforms. This continuity strengthens her connection with fans, making them feel truly “seen” and “understood,” even if it’s coming from an AI agent.

Luna's abilities extend beyond just interactions; she is also financially independent and has her own on-chain wallet. Luna is the first agent in history to autonomously tip humans on-chain, and has received strong support from Base founder Jesse. This allows her to reward loyal supporters with $LUNA tokens, creating a unique combination between emotional and financial engagement. Every interaction and revenue generated by Luna contributes to a sustainable token ecosystem. The $LUNA tokens she earns are regularly bought back and burned, benefiting fans and supporters who hold them.

It is worth mentioning that in December 2024, Story Protocol (Layer1 designed for intellectual property (IP)) announced the hiring of Luna to officially manage its official X account with an annual salary of up to $365,000. This once again proves the importance and potential of AI AGENT in the modern digital ecosystem. In the future, as the capabilities of AI AGENT continue to increase, we have the opportunity to see more companies use this technology to drive innovation and growth and achieve more intelligent business models.

Another of the most influential and innovative agents deployed on Virtuals Protocol is AIXBT. This AI AGENT is designed to provide real-time market analysis on social media and automatically interpret trends through personalized insights. Specifically, AIXBT analyzes posts posted by more than 400 KOLs on X, identifies emerging narratives in the market, and performs technical analysis on price movements. In addition, AIXBT is able to interact with other X users (both humans and AI agents). Notably, it provides AIXBT token holders with greater access. Launched in November, the AIXBT token has experienced a rapid rise, with a market value of nearly $800 million at one point, and currently has a market value of nearly $600 million.

2.1.2 Holoworld

Holoworld was founded by Tong Pow and Hongzi Mao in 2023 and originated from San Francisco-based Hologram Labs. It is a startup focused on the next generation of AI social technology. Based on years of technology accumulation, including motion capture, machine learning and 3D animation technology, it aims to democratize the creation of AI characters through this platform and completely transform the digital interaction model.

Since its launch, the Holoworld project has quickly gained support from many well-known investors, including Polychain Capital, Linkin Park band member Mike Shinoda, BRC-20 token standard founder Domo, and BitMEX co-founder Arthur Hayes.

At the business level, Holoworld has carried out in-depth cooperation with many well-known brands, including Arbitrum, BNB Chain, L'Oréal and Bilibili, and has established partnerships with a series of influential NFT projects such as Pudgy Penguins and Milady Maker. These collaborations fully demonstrate Holoworld's ability to use its advanced AI technology to build unique digital identities.

Holoworld has created a complete AI character creation and interaction platform with a user interface that combines cutting-edge AI technology with intuitive tools. The following are the five core modules of the platform: 1. Brain Development, 2. Persona Customization, 3. Personality Integration, 4. Knowledge-Based Implementation, and 5. 3D Avatar Creation.

Ava AI is Holoworld's flagship AI chat assistant, built on OpenAI's GPT-3.5 Turbo model, whose deep learning neural network contains more than 175 billion machine learning parameters. Ava supports fast AI dialogue function, allowing users to ask questions at any time and get instant responses.

In addition, Holoworld has launched Agent Market on the Solana blockchain, allowing anyone to create and deploy multimodal AI agents. These agents have complete full-body avatars, custom voices, and upgradeable skills, without the need for programming. The platform is deeply integrated with the upcoming Holoworld Launchpool, and AVA token holders have priority access to new projects. In addition, Agent Market has attracted a wide range of partners and creators, including game studios, NFT communities, and academic researchers from Stanford and Harvard.

Overall, the Holoworld platform makes the process of AI character creation simple and easy to use, allowing users with non-technical backgrounds to build complex digital characters. This not only creates new digital narrative and interactive possibilities, but also enables AI characters to cover multiple channels and attract and engage more audiences through seamless integration with mainstream social media and content platforms.

2.2 AIAGENT Framework

When exploring the AI AGENT ecosystem, many people see Launchpad as the basic tool needed to create these agents. However, the key project that really drives the entire AI AGENT narrative is not just these tools, but a DAO called ai16z, which is like a mine that breeds the core value of AI AGENT. On October 25, 2024, ai16z officially launched its AI16Z token and achieved remarkable market success. However, what pushed ai16z to the center of the AI AGENT narrative was not only its fair launch model, but also the release of its open source framework ElizaOS.

2.2.1 Eliza OS

ElizaOS is a set of tools that supports the creation of customized AI AGENTs, with strong network effects and unlimited scalability. By simplifying the development process and providing flexible functional modules, the framework has quickly attracted the attention of developers and users around the world, becoming the most influential technical support in the field of AI AGENTs.

AI Agent frameworks are like a set of tools and guidelines that help programmers develop, train, and deploy AI agents more easily. Simply put, these frameworks can reduce the difficulty of development, so programmers can focus more on making these agents smarter and more useful. AI Agent frameworks are now beginning to cooperate with some new technologies, such as: DeFi protocols (programs that help improve financial investment strategies) and NFT projects (new tools for creating and using digital art or collectibles). Through these technical collaborations, they can connect different technologies and platforms to create a more interconnected and interactive ecosystem, which has attracted a lot of market attention. Others include ARC, Swarms, and Zerebro, which are all projects that are using or developing AI Agent frameworks.

To date, the ElizaOS framework has been forked more than 3,200 times, which means that a large number of developers have used its code to build their own AI AGENTs. Most of the AI AGENTs on the market are built using the ElizaOS framework, which is why ai16z has become a leader in this field.

The ElizaOS framework is more than just a simple chatbot, and agents can be configured to perform complex tasks. For example, some agents are designed to perform on-chain transactions, interact with smart contracts, wallets, or decentralized applications (dApps), while others connect to data providers to monitor prices, volumes, or liquidity.

The architecture of the ElizaOS framework is divided into five main components:

1. Agent: Define the agent’s personality, communication style, and knowledge base.

2. Actions: Allow agents to perform specific tasks beyond text responses, such as generating reports or executing transactions.

3. Evaluators: Help agents interpret data and execute multi-step goals.

4. Providers: Provide external data or real-time context, such as asset prices or dedicated API data.

5. Memory System: Enables the agent to retain interaction history and preferences, making its responses more contextual and natural.

2.3 DEFAI

DeFi has always been the backbone of Web3, and DeFAI (DeFi + AI) is an upgraded version of DeFi, allowing people to use DeFi more conveniently. By leveraging AI, it simplifies complex interfaces and eliminates friction that prevents ordinary people from participating. Imagine that managing your DeFi portfolio is as easy as chatting with ChatGPT. In fact, the first wave of DeFAI projects has begun to emerge. Below we mainly introduce three areas: abstraction layer, autonomous trading agent, and AI-driven dApp.

2.3.1 Abstraction Layer

The complexity of DeFi often makes novice users feel daunted. To solve this problem, the abstraction layer hides the complexity behind it through an intuitive interface, allowing users to interact with DeFi protocols through natural language instructions instead of relying on cumbersome operation panels.

Before AI technology became popular, intent-based architectures simplified the process of transaction execution to a certain extent. For example, platforms like @CoWSwap and @symm_io partially solved the problem of decentralized liquidity by aggregating decentralized liquidity pools to provide users with the best pricing. However, these platforms did not solve the core problem of DeFi - complexity still exists, and users still need to face daunting operational processes and technical barriers.

Today, AI-driven solutions are gradually filling this gap, providing users with a more intuitive and intelligent interactive experience. Here are a few projects worth noting:

• 2.3.1.1 GRIFFAIN

Griffain is the first project to launch a token. Currently, its product is still in its early stages and is only open to invited users. Griffain allows users to perform a variety of operations from simple to complex, such as fixed investment automation (DCA), launching and airdropping memecoin, etc. Through these functions, Griffain not only lowers the threshold for users to enter the DeFi field, but also provides a wealth of automation tools for advanced users. Griffain's current market value is nearly US$500 million.

• 2.3.1.2 ORBIT / GRIFT

Orbit is the second project to launch a token, and its products focus on the on-chain DeFi experience. Orbit places special emphasis on cross-chain functions, and has currently integrated more than 117 blockchains and 200 protocols, the highest number of integrations among the three major protocols. This enables Orbit to provide a seamless interactive experience in a multi-chain environment, providing users with great convenience in cross-chain transactions and liquidity acquisition.

• 2.3.1.3 HEYANON

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

2.3.2 Autonomous Trading Agent

In the field of DeFi and crypto trading, obtaining market information (Alpha), manually executing trades, and optimizing portfolios have always been time-consuming and energy-consuming processes. However, with the advancement of technology, the emergence of automated trading agents is changing all this. These agents go beyond the scope of traditional trading robots and become dynamic partners that can adapt to the environment, learn, and make smarter decisions over time.

Trading robots are not new. They have long been used to perform predefined actions based on static programming. However, automated trading agents differ fundamentally from these traditional robots:

• Information Extraction: Agents are able to extract information from unstructured and constantly changing environments.
• Data reasoning: They are able to reason about data in the context of a specific goal.
• Pattern Discovery: Agents are able to discover and exploit patterns over time, thereby improving their decision-making capabilities.
• Autonomous behavior: They are able to perform actions not explicitly programmed by their owners, demonstrating increased flexibility and intelligence.

The following are some representative projects of autonomous trading agents:

• 2.3.2.1ai16z

Called the first AI version of VC, ai16z is an innovative DAO that aims to integrate artificial intelligence (AI) into financial management, investment, and venture capital. Its name mimics the well-known investment fund a16z (Andreessen Horowitz), but ai16z is more than just a joking imitation. It presents a new operating model that combines decentralized governance and the powerful potential of AI. ai16z is managed by a fictional AI AGENT named Marc AIndreessen and AI16Z token holders. The character of Marc AIndreessen is obviously inspired by a16z co-founder Marc Andreessen. This anthropomorphic AI AGENT guides the organization's daily decisions and operations.

AI16Z token holders play a vital role in the governance structure of ai16z. They can propose investment ideas, submit project proposals, or suggest token buybacks. These proposals are voted on through a decentralized voting system, and AI AGENTMarc AIndreessen uses a trust scoring system to evaluate these proposals. The trust scoring system is based on the relevance and reliability of members' past contributions, ensuring that the decision-making process is transparent and based on evidence.

The innovation of ai16z lies in its unique governance model and the application of AI AGENT. By combining decentralized decision-making and AI technology, the project not only simplifies the traditional investment and management process, but also opens up a new way of operating autonomous organizations. The introduction of AI AGENT improves the efficiency and accuracy of decision-making, especially in complex investment environments. In addition, ai16z also demonstrates how to build trust and transparency mechanisms in virtual economies, providing an innovative example for other DAOs.

The rapid popularity of the ElizaOS framework has enabled ai16z to rise rapidly in the Solana ecosystem. A strong, active, and united community has formed around this framework, making it the most widely used AI AGENT framework in the crypto ecosystem. In just a few weeks, ElizaOS has become one of the most frequently used open source projects on GitHub worldwide, with more than 350 contributors actively participating in its development, expanding its functionality and plugins, enabling agents based on the framework to perform more tasks or run across more blockchains.

Although the initial concept of ai16z was an investment DAO built around a dedicated AI AGENT, the team quickly realized that its growth potential was far greater than that. As a result, ai16z quickly established relationships with multiple partners in the Web2 and Web3 fields to enable the Eliza framework to be applied globally.

• 2.3.2.2ALMANAK

Almanak provides users with institutional-grade quantitative AI AGENT, dedicated to solving the complexity, fragmentation and execution challenges in DeFi. The platform performs Monte Carlo simulations by forking the EVM chain, simulating unique complex factors in the real environment, such as miner extractable value (MEV), gas fee costs and transaction ordering. In addition, it uses a trusted execution environment (TEE) to ensure the privacy of strategy execution, protect key market insights, and implement non-custodial fund processing through the Almanak wallet, allowing users to precisely grant permissions to agents.

Almanak's infrastructure covers the ideation, creation, evaluation, optimization, deployment, and monitoring of financial strategies, with the ultimate goal of enabling these agents to learn and adapt over time. The platform raised $1 million on @legiondotcc, which was oversubscribed. Next steps include launching a beta test and deploying preliminary strategies and agents with testers. It will be exciting to see how these quantitative agents perform.

• 2.3.2.3COD3XORG / BIGTONYXBT

Cod3x was built by the Byte Mason team, known for their work on Fantom and @SonicLabs. Cod3x is a DeFAI ecosystem designed to simplify the creation of trading agents, providing a no-code building tool that allows users to build agents by specifying trading strategies, personalities, and even tweeting styles.

Users can access any dataset and develop financial strategies in minutes, with the help of a rich API and strategy library. Cod3x integrates with @AlloraNetwork to leverage its advanced machine learning price prediction models to enhance trading strategies.

Big Tony is the flagship agent of Cod3x, trading based on Allora's models, entering and exiting the market in mainstream assets based on predictions. Cod3x is committed to creating a thriving automated trading agent ecosystem.

A notable feature of Cod3x is its approach to liquidity. Unlike the common Alt:Alt liquidity pool structure popularized by @virtuals_io, Cod3x uses a stablecoin:Alt liquidity pool backed by cdxUSD. This provides liquidity providers with greater stability and confidence than Alt:Alt pairs.

2.3.3 AI-driven dApps

In the DeFAI space, AI-driven dApps represent a promising but nascent field. These decentralized applications integrate AI or AI AGENT to enhance functionality, automation, and user experience. Although this field is still in its infancy, some ecosystems and projects have begun to emerge, showing great potential for development.

Among them, @modenetwork, as a Layer 2 ecosystem, is actively attracting high-tech developers who focus on combining AI and DeFi. Multiple teams have emerged in the Mode network, committed to developing cutting-edge AI-driven application scenarios, demonstrating the innovation in this field. Here are some key projects:

• 2.3.3.1 ARMA (Autonomous Stablecoin Farming)

Developed by @gizatechxyz, ARMA is an autonomous stablecoin farming protocol based on user preferences that automatically adjusts stablecoin farming strategies to achieve optimal returns.

• 2.3.3.2 Modius (Balancer LP farming with autonomous proxy)

The project was developed by @autonolas, with the goal of providing liquidity (LP farming) on Balancer through autonomous agents, using AI to automatically optimize investment strategies and increase returns.

• 2.3.3.3 Amplifi Lending Agents

Developed by @Amplifi_Fi, these agents integrate with @IroncladFinance to automatically swap assets, lend on the Ironclad platform, and maximize yields with automatic rebalancing. These features make DeFi lending smarter and more efficient.

2.4 AI AGENT+ Game

The use of AI AGENTs in the gaming industry is revolutionizing all aspects of game play and development. These intelligent systems create more immersive and engaging gaming experiences for players in multiple areas, with the following key applications:

1. NPC behavior optimization

AI AGENT greatly improves the behavior of non-player characters (NPCs), making them more realistic and responsive. Unlike traditional preset script-driven, AI-based NPCs can: 1) adjust their actions based on the player's choices; 2) show more realistic emotions and decision-making capabilities; 3) learn through interaction and provide a diverse experience.

For example, in the open-world game Red Dead Redemption 2, NPCs are able to remember past interactions with the player and react accordingly, creating a more dynamic and believable game world.

2. Programmatic content generation

AI AGENT excels in procedurally generating game content and can algorithmically generate a large amount of game content, including: terrain and landscape, missions and plots, props and loot, and character design.

For example, No Man’s Sky uses AI-driven procedural generation technology to create an entire universe with unique planets, creatures, and ecosystems, providing players with almost unlimited exploration possibilities.

3. Adaptive Difficulty Adjustment

AI AGENT analyzes player performance in real time to dynamically adjust game difficulty. This capability ensures that players face appropriate challenges to stay engaged and not frustrated. For example: increasing enemy strength as players gain power; providing hints or buffs when players get stuck; balancing resources and obstacles based on skill level.

Games like Resident Evil 4 utilize adaptive difficulty systems that fine-tune enemy behavior and item availability based on player performance, providing a more balanced gaming experience.

4. Path planning and navigation

AI AGENT uses complex algorithms to guide characters to move in complex game environments. This technology brings more realistic movement patterns and more efficient navigation, which not only improves the behavior of NPCs, but also optimizes the operating experience of player-controlled units in strategy games.

5. Graphics Enhancement

AI technologies such as deep learning are used to improve game visuals by improving textures and resolution in real time, generating realistic facial expressions and animations, and optimizing rendering performance to improve game performance.

6. Player sentiment analysis

AI AGENT can analyze player behavior and feedback to assess their enjoyment and engagement. This data helps developers make informed decisions about game design and updates to improve the overall player experience.

Below we introduce some major projects:

2.4.1 Digimon

@digimon_tech is built on the Solana blockchain. It is not just a game platform, but a complete AI+ game technology framework. By deeply integrating AI technology into game development, Digimon Engine enables creators to create more immersive, dynamic and interesting games. With this platform, AI-driven games not only redefine the way of interaction, but also create a new standard for gaming experience. Behind every game character, there is a set of AI-generated stories and worldviews. The team behind Digimon is supported by a16z and has received investment and incubation from a16z.

Digimon's tokens are now available on the Kucoin exchange. In the future, through Digimon's game engine, there will be an opportunity to create an autonomous world on the chain composed of AI AGENTs, where AI AGENTs interact with players in the world and build a virtual economy together.

2.4.2 Illuvium

Illuvium is an RPG and NFT game built on Ethereum. On January 7, Illuvium announced a partnership with Virtuals Protocol to enhance the gaming experience of the upcoming Illuvium MMO Lite. This partnership will leverage Virtuals’ AI technology and its GAME LLM framework to provide NPCs with dynamic, intelligent behaviors and immersive experiences.

As AI technology continues to advance, we can expect more innovative applications in the gaming field, further blurring the line between virtual and reality and creating a more immersive and personalized player experience. This technology not only changes the way games are developed, but also plays a vital role in enhancing the interactivity and immersion of games.

2.4.3Smolverse

Smolverse is a game and NFT project on Treasure DAO. Since December last year, Smolverse has been working with ai16z to develop an on-chain AI Tomogatchi game called "Smolworld" that incorporates Eliza's Agent framework.

3. Summary of highlights

We have seen that the new technologies being built by crypto have great potential in the real world, and the allocation strategies of native investors in similar situations in the past also provide valuable references for the current market. The AI AGENT ecosystem is in its early stages, but it has attracted a lot of attention, funding, and developers. Although its future development remains uncertain, if major DeFi protocols, private investors, or venture capitalists begin to invest in this field, it indicates that its potential for continued development is not small. As technology continues to advance, AI AGENT is expected to become a key force in changing the global economic and social structure.

The current market timing and narrative are well prepared for the prosperity of the information industry, and future development is worth looking forward to. When exploring the future potential of AI AGENT, although discovering the next project similar to $LUNA is the most direct path, expanding the application boundaries of AI AGENT may create new and unimaginable value.

We have the following views:

1. Concentration of value and competition with differentiation. Like L1 blockchains, the value of AI AGENT may eventually be concentrated in a few major winners. Therefore, these companies need to find points of difference in modularity, scalability, and media platform integration. At present, most frameworks already have learning and memory systems, using retrieval enhancement generation technology to enable agents to incorporate new information into conversations. For example, the current Eliza framework has a significant advantage in the market. With its high development activity and rapid plug-in integration, Eliza performs particularly well in the integration of social media and web applications. The framework is based on TypeScript and has extensive plug-in support, including Coinbase webhooks, Great Onchain Agent Toolkit, and Phala's TEE for secure agent wallet control, and is compatible with multiple blockchains. Virtuals' GAME framework excels in the field of games and social media agents, designed for "environment-independent" agents, capable of advanced planning and execution, and learning from feedback. Its modular architecture allows users to upload custom models and data sets stored on the chain to enrich the functionality of the agent. However, the value accumulation mechanism of GAME and CONVO framework tokens is still unclear, and the market is full of expectations.

2. Challenges of fairness and data bias. Despite the impressive progress in AI, there are also some challenges in deploying these systems. One of the main issues is that the data sets used to train AI agents are at risk of bias. AI systems learn from historical data, which may contain discriminatory patterns and, if not controlled, can lead to biased decisions, such as favoring specific groups over others in recruitment or lending scenarios. To address this issue, not only technical expertise is required, but also a nuanced understanding of social dynamics. Monitoring the fairness of AI systems is critical to ensure that they do not reinforce harmful biases. Continuous auditing of decisions made by AI agents can help detect problems early and reduce unexpected results.

3. Diversified applications and expansion of economic functions. The application areas of AI AGENT are expanding rapidly. In addition to social media and the financial industry, it also shows great potential in the fields of medical care, education, law, etc. As the technology continues to mature, AI AGENT will provide personalized services in more scenarios, improve work efficiency, and promote innovation.

Take Luna as an example. Currently, she is able to interact with humans through social media and motivate users to achieve her goals by sending tokens using Coinbase Wallet on Base. The next step in the future is to allow Luna to build her own social relationships as an independent economic entity. She can attract more followers by sending tokens, buy more attention for her social media, and even hire a professional content team to enrich her IP ecosystem and continue to create heat. Once the infrastructure to achieve these goals is established, $VIRTUAL may reach the next milestone. This not only means that AI AGENT will be more deeply embedded in human life in the economic and social fields, but also redefine the way AI interacts with humans, laying the foundation for future digital economic and social interaction models. For example, in the medical field, AI AGENT can provide doctors with diagnostic advice by analyzing patient data, improving the quality and efficiency of medical services.

4. Multi-technology integration. The future development of AI AGENT will rely on deep integration with cutting-edge technologies such as blockchain, IoT, and 5G. This multi-technology crossover will promote AI AGENT's capabilities in data processing, privacy protection, real-time decision-making, and create new application scenarios and business models. For example, through integration with IoT devices, AI AGENT can collect and analyze data in real time to provide users with more intelligent services.

5. Social and ethical considerations. With the widespread application of AI AGENT, social and ethical issues have become more prominent. As mentioned at the beginning of the article, will AI AGENT become as threatening as the Queen of Hearts? For example, AI AGENT may cause ethical disputes in decision-making, especially in scenarios involving privacy, data security, and automated decision-making. Therefore, when developing AI technology, it is necessary to introduce transparency and accountability mechanisms to ensure that the development of technology is consistent with social values. At the same time, establishing a clear legal and ethical framework is crucial to regulating the behavior of AI AGENT and protecting the rights and interests of users.

As the integration of AI and blockchain continues to develop, now is the time to participate in these breakthrough developments. But in this participation, we need to think not only about "what can AI do for humans, and what do humans want AI to do?", but also about "what does AI want to do, and what will AI guide humans to do?"

4. References

1.https://messari.io/report/building-better-agents-rival-frameworks-and-their-design-choices

2.https://www.binance.com/en/square/post/18968465099217

3.https://www.tokenpost.com/news/business/13277

4.https://www.wired.com/story/the-prompt-ai-agents-how-much-should-we-let-them-do/?

5.https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html

6.https://medium.com/@0xai.dev/virtuals-protocol-luna-55b661df601e

7.https://oakresearch.io/en/analyses/innovations/closer-look-at-ai16z-mine-of-ai-agents

8.https://x.com/Defi0xJeff/status/1875881226151841925

9.https://www.itp.net/charged/gaming/ai-agents-are-changing-gaming-forever-heres-how-they-adapt-to-you

10.https://eightgen.ai/evolution-of-ai-agents-the-beginning-part-1/

https://www.bitget.com/news/detail/12560604466507