GuShiio.com AI Agents are revolutionizing the digital economy, showing strong potential from DeFi to GameFi.
1. Background overview
1.1 Introduction: “New Partner” in the Intelligent Era
Each cryptocurrency cycle brings new infrastructure that drives the development of the entire industry.
- In 2017, the rise of smart contracts gave rise to the vigorous development of ICOs.
- In 2020, DEX’s mobile pool brought DeFi’s summer boom.
- In 2021, the release of a large number of NFT series works marks the arrival of the era of digital collectibles.
- In 2024, pump.fun’s outstanding performance led to the craze of memecoin and launch platforms.
It should be emphasized that the start of these vertical areas is not only due to technological innovation, but also the result of the perfect combination of financing models and bull markets cycles. When opportunities meet the right time, they can lead to huge changes. Looking forward to 2025, it is clear that the emerging area in the 2025 cycle will be AI agents. This trend peaked in October last year, when the $GOAT token was launched on October 11, 2024, and reached a market value of US$150 million on October 15. Immediately afterwards, on October 16, Virtuals Protocol launched Luna, making its debut as a live IP image of the girl next door, setting off the entire industry.
So, what exactly is an AI Agent?
Everyone is no stranger to the classic movie “Resident Evil”, and the AI system in it is impressive. Queen of Hearts is a powerful AI system that controls complex facilities and security systems, and is able to autonomously sense the environment, analyze data, and take quick action.
In fact, AI Agent has many similarities with the core functions of Queen of Hearts. AI Agents in reality play a similar role to some extent. They are the “guardians of intelligence” in the field of modern technology, helping enterprises and individuals cope with complex tasks through autonomous sensing, analysis and execution. From autonomous vehicles to intelligent customer service, AI Agents have 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, and gradually penetrate into various industries to promote the dual improvement of efficiency and innovation.
For example, an AI AGENT can be used to automate transactions, managing portfolios and executing transactions in real time based on data collected from Dexscreener or social platform X, constantly optimizing its own performance over iteration. AI Agents are not a single form, but are divided into different categories based on specific needs in the encryption ecosystem:
1. Executing AI Agents: Focus on completing specific tasks, such as trading, portfolio management, or arbitrage, and are designed to improve operational accuracy and reduce the time required.
2. Creative AI Agent: Used for content generation, including text, design and even music creation.
3. Social AI Agents: As opinion leaders on social media, interact with users, build communities and participate in marketing activities.
4. Coordinated AI Agent: Coordinate complex interactions between systems or participants, especially suitable for multi-chain integration.
In this report, we will discuss in depth the origin, current situation 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 history of AI AGENT demonstrates 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 a separate field. During this period, AI research focused mainly on symbolic methods, giving rise to the first AI programs such as ELIZA (a chatbot) and Dendral (an expert system in organic chemistry). This stage also witnessed the first introduction of neural networks and the preliminary exploration of machine learning concepts. However, AI research during this period was severely restricted by the limitations of computing power at the time. Researchers have encountered great difficulties in natural language processing and developing algorithms that mimic human cognitive functions. In addition, in 1972, mathematician James Lighthill submitted a report published in 1973 on the status of ongoing AI research in the UK. The Lighthill report basically expressed overall pessimism about AI research after the early excitement period, triggering a huge loss of confidence in AI among British academic institutions (including funding agencies). After 1973, funding for AI research decreased significantly, and the AI field experienced its first “AI winter”, and doubts about the potential of AI increased.
In the 1980s, the development and commercialization of expert systems led companies around the world to adopt AI technology. This period saw significant progress in machine learning, neural networks and natural language processing, driving the emergence of more complex AI applications. The introduction of autonomous vehicles for the first time and the deployment of AI in various industries such as finance and medical also mark the expansion of AI technology. But in the late 1980s and early 1990s, as market demand for dedicated AI hardware collapsed, the AI field experienced a second “AI winter.” In addition, how to scale AI systems and successfully integrate them into practical applications remains an ongoing challenge. But 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 revival 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 technological 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, further breakthroughs were made in generation models such as reinforcement learning agents and GPT-2, pushing conversational AI to new heights. In this process, the emergence of the Large Language Model (LLM) has become 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 beyond traditional models through tens of billions or even hundreds of billions of parameters. Their excellent performance in natural language processing allows AI agents to demonstrate clear logical and well-organized interaction capabilities through language generation. This allows AI agents to be applied to scenarios such as chat assistants and virtual customer service, and gradually expands into more complex tasks such as business analysis and creative writing.
The learning capabilities of large language models provide AI agents with greater autonomy. Through Reinforcement Learning technology, AI agents can continuously optimize their own behaviors and adapt to dynamic environments. For example, in AI-driven platforms such as Digimon Engine, AI agents can adjust behavioral strategies based on player input to truly achieve dynamic interaction.
From the early rule system to the big language model 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 journey. With the further development of technology, AI agents will become more intelligent, scene-based and diversified. The big language model not only injects the soul of “intelligence” into AI agents, but also provides 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
AIAGENT differs from traditional robots in that they can learn and adapt over time, making nuanced decisions to achieve goals. Think of them as highly skilled and evolving players in the crypto space, able to act independently in the digital economy.
The core of AI AGENT lies in its “intelligence”-that is, it uses algorithms to simulate the intelligent behavior of humans or other organisms to automate complex problems. AI AGENT’s workflow usually follows the following steps: perception, reasoning, action, learning, and adjustment.
1.2.1 Sensing module
AI AGENT interacts with the outside world through the sensing module to collect environmental information. This part of the function is similar to human senses, using sensors, cameras, microphones and other devices to capture external data, including extracting meaningful features, identifying objects, or identifying related entities in the environment. The core task of the perception module is to transform raw data into meaningful information, which usually involves the following technologies:
- Computer vision: Used to process and understand image and video data.
- Natural Language Processing (NLP): Helping AI Agents understand and generate human language.
- Sensor fusion: Integrate data from multiple sensors into a unified view.
1.2.2 Reasoning and Decision Module
After sensing the environment, AI Agents need to make decisions based on data. The reasoning and decision-making module is the “brain” of the entire system, which conducts logical reasoning and strategy formulation based on the collected information. Use large language models, etc. to act as orchestrators or inference engines to understand tasks, generate solutions, and coordinate specialized models for specific functions such as content creation, visual processing, or recommendation systems.
This module typically uses the following technologies:
- Rule engine: Make simple decisions based on preset rules.
- Machine learning models: Including decision trees, neural networks, etc., used for complex pattern recognition and prediction.
- Strengthen learning: Let AI Agents continuously optimize decision-making strategies through trial and error to adapt to changing environments.
The reasoning process usually consists of several steps: first, an evaluation of the environment, second, the calculation of multiple possible action plans based on the goal, and finally, the selection of the optimal plan for execution.
1.2.3 Execution module
The execution module is the “hands and feet” of AI AGENT, putting the decisions of the inference module into action. This part interacts with external systems or devices to complete specified tasks. This may involve physical operations (such as robotic actions) or digital operations (such as data processing). Execution modules rely on:
- Robot control system: Used for physical operations, such as the movement of a robot arm.
- API calls: Interaction with external software systems, such as database queries or network 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 competitiveness of AI AGENT, which enables agents to become smarter over time. Continuous improvement is made through feedback loops or “data flywheel” to feed the data generated during the interaction back into the system to enhance the model. This ability to adapt and become more effective over time provides companies with a powerful tool to improve decision-making and operational efficiency.
Learning modules are usually improved in the following ways:
- Supervised learning: Using annotated data for model training, allowing AI Agents to complete tasks more accurately.
- Unsupervised learning: Discovering underlying patterns in unlabeled data helps agents adapt to new environments.
- Continuous learning: Maintaining the agent’s performance in a dynamic environment through real-time data updates models.
1.2.5 Real-time feedback and adjustment
AI AGENT optimizes its performance through constant feedback loops. 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 change to multiple industries with its huge potential as a consumer interface and autonomous economic actor. Just as the potential of the L1 block space was immeasurable in the previous cycle, AI AGENT also showed the same prospects in this cycle.
According to the latest report from Markets and Markets, the AI Agent market is expected to grow from US$5.1 billion in 2024 to US$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 proxy frameworks. The development activities of Microsoft’s frameworks such as AutoGen, Phidata, and LangGraph are 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 a premium multiple to this.
From the perspective of deploying public chains, Solana is the main battlefield, and other public chains such as the Base chain have huge potential.
In terms of market awareness (Mindshare), FARTCOIN and AIXBT are far ahead. The birth of Fartcoin comes from the same origin as GOAT. They both come from the AI AGENT model, terminal of truths. During the dialogue between the goat model and opus(artificial intelligence tool), it was mentioned that Musk likes the sound of farting, so this AI model proposes to issue a token called Fartcoin and designed a series of promotion methods and gameplay methods. Fartcoin was born on October 18, slightly after GOAT (October 11), and achieved a brief valuation of more than $1 billion in December 2024. Although initially considered a humorous view of the digital currency space, its rapid rise has prompted investors and analysts to study its fundamentals, market performance and potential longevity. Judging from the hot spots of social media attention, Fartcoin has stepped on the popularity of AI AGENT.
AIXBT, ranked second, is an AI Agent based on Base Chain launched by Virtuals Protocol. But unlike traditional meme tokens, it not only has entertainment properties, 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 insight 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 to optimize investment decisions.
Source: cookie.fun, 2025/1/20
From a technical perspective, AI Agent technology is developing in the direction of multimodal interaction and high autonomous decision-making capabilities. In 2024, the introduction of cross-modal learning and generative pre-training models, such as the GPT family models, will enable AI Agents to better understand and process multiple forms of data, such as text, images, and speech. These technological breakthroughs have significantly improved the understanding ability and decision-making efficiency of agents, enabling them to make autonomous decisions in more complex and dynamic environments. According to McKinsey’s analysis, AI Agents ‘multimodal capabilities and cross-domain collaboration are becoming symbols of the intelligent era. This allows AI Agents to not only provide support for a single task, but also provide comprehensive information analysis and dynamic optimization suggestions in complex decisions.
1.3.2 Reasons for combining AI Agents and token economy models
The combination of AI Agents and token economy models is not only an inevitable trend in technological development, but also an intrinsic driving mechanism for efficient, transparent and sustainable development for its 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 and encourage individuals or companies to contribute high-quality annotated 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: A tokenized reward system can attract more developers and users to participate, forming a positive feedback loop on technology and ecology.
- Case in point: Certain blockchain-based AI platforms (such as Ocean Protocol) use tokens to reward data-sharing behavior to promote the prosperity of the data market.
2. Assettization of AI Agent itself
Through tokenization, AI Agents are not only a tool, but can also become a new type of asset that creates long-term wealth effects.
- Tokenized identity: The data, skills and execution capabilities of AI agents can be evaluated and priced. By issuing corresponding tokens, users can use their functions on demand.
- Investment value: Token holders of AI Agents can share the dividends of their growth, such as the increased value brought by the agent’s market share and optimization of reasoning efficiency.
- Enhanced liquidity: The existence of tokens provides AI Agents with negotiable market value, giving them trading and investment attributes, and attracting more capital into this field.
- Case: SingularityNET, for example, supports AI service transactions through tokens (AGIX), allowing AI Agents to be asset-oriented and achieve sustainable development.
3. Support interaction and transactions between AI agents
In the future, AI Agents will no longer be isolated individuals, but will constitute a huge Internet. In this network, a decentralized token economy model is the key to efficient interactions and value exchanges.
- Payment and settlement: AI Agents can complete task payment and service settlement through cryptocurrency, reducing intermediate links in traditional payment systems and improving transaction efficiency.
- Value allocation: Through smart contracts, the results of collaboration between AI Agents (such as optimization benefits from joint learning models) can be automatically allocated according to agreed rules to ensure fairness.
- Decentralized Autonomous Organization (DAO) Governance: The behavior of AI Agents can be managed through voting by token holders, ensuring 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 achieve a self-driven collaboration system.
4. Improve system transparency and security
The token economy model combines blockchain technology to provide an untamperable record and a transparent operating mechanism for the operation process of AI Agents.
- Traceability and audit: All transactions, reasoning and data use behaviors can be recorded on the chain to ensure the credibility and auditability of the system.
- Data security and privacy: By inciting private computing with tokens, users can contribute data without revealing sensitive data, further enhancing security.
- Prevent abuse and cheating: The token model can set financial penalties for malicious behavior and reduce the possibility of bad behavior.
5. Accelerate the formation of a global, borderless AI economic ecosystem
The token economy model can break through geographical limitations and allow global users to participate in the construction and use of AI Agents.
- Lower barriers to entry: The global circulation characteristics of cryptocurrencies can provide financial support to unbanked users or institutions, allowing more people to share the development dividends of AI.
- Global collaboration: Whether it is data sharing, AI training, or cross-border transactions, the token system provides the infrastructure for global collaboration and removes barriers to traditional economic systems.
- Ecological self-circulation: Through the token economy, the benefits of AI Agents can be directly fed back into development and ecological construction to achieve long-term development.
Overall, the combination of AI Agent and token economy model is not only a match between technology and economic logic, but also an innovative form for the future digital economy. By introducing a token system, AI Agents can encourage more efficient utilization of data and resources, asset their own value, support interactions and transactions, improve transparency and security, and even build a global open economic ecosystem. This model is expected to become an important direction in promoting the integration of AI and blockchain, laying the foundation for the further intelligence of the digital society.
2. Analysis of AI Agent application in crypto
2.1AI AGENT LAUNCHPAD
AI Agent Launchpad refers to a platform that focuses on the issuance of smart agents and related tokens. Its functions are similar to Meme coin issuance platforms such as Pump.fun This platform allows users to easily create and deploy AI AGENT and seamlessly integrates with social media platforms such as Twitter, Telegram, and Discord to achieve automated user interactions. This approach greatly lowers the threshold for distribution and promotion, provides users with a more convenient creation experience, and at the same time expands the application field of AI AGENT and promotes its 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 launched on Base. Users can easily deploy their own AI Agents using VIRTUAL tokens.
- Creation and deployment: Each agent requires 100 VIRTUAL tokens to start, and initial liquidity is ensured through a binding curve mechanism.
- Capitalization mechanism: After reaching a specific capitalization threshold, the agent enters a new stage, automatically deploys the liquidity pool, and the smart contract operates independently.
- Autonomous interaction: Agents can automate tasks such as transactions and participate in community activities.
The Virtuals Protocol team has demonstrated excellent adaptability and strategic vision, and their path to success stems from a series of key transformation and innovation initiatives. The story begins at the end of 2021, when a group of young people from well-known companies such as Boston Consulting Group (BCG) and Meta seized the opportunity of the GameFi boom, founded PathDAO, and successfully raised US$16 million. However, the price of the $PATH token fell sharply by 99% since then, forcing the team to reassess their strategic direction. In order to repay investors, the team tried a number of new businesses, including digital and physical clothing brands for players, dating applications based on online credit, unsecured loans to players, AI-generated music for Web2 users, and more.
During this process, the team noticed that the introduction of AI AGENT will have a profound impact on the gaming industry and the increasing market demand for AI infrastructure. So by the end of 2023, PathDAO passed a proposal to shift the entire project to the AI AGENT protocol, and in January 2024, Virtuals Protocol was officially established. Virtuals Protocol made multiple attempts, including AI Waifus (an interactive female AI AGENT that does not rely on Twitter influencers) and the game AI AGENT, until they found a breakthrough in the AImeme craze sparked by $Goat.
Now, Virtuals Protocol has become the first project to reach critical scale, with a market value of US$1.7 billion. We believe it will continue to expand and maintain its leading position in the market. Once network effects are established, they are difficult to replace. It can be seen from its rapid achievement of unicorn valuation that Virtuals Protocol has clearly created an economic flywheel effect:
- $VIRTUAL is required to create agents, provide liquidity pools, and purchase agent tokens
- The need to create and purchase proxy tokens drives token prices
- The wealth effect brought by the appreciation of $VIRTUAL flows to new agents; successful agents collect $VIRTUAL transaction income and can be reinvested
- Lower barriers to entry encourage experimentation and speculation, while red pill agents with market capitalizations above a certain level can unlock full agent capabilities.
The flywheel effect drives demand, revenue maintains continuous research and development, and deflationary economics captures value for tokens. In addition, revenue and liquidity requirements are denominated in $VIRTUAL and are likely to grow as prices appreciate.
The ecosystem is built on two main levels: the protocol layer and the DApp layer. The protocol layer is a model center that provides basic AI models and algorithms that developers can access and develop on top of. Contributors provide data and development 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. Developer friendly software development kits (SDKs) simplify the integration of advanced AI functions into various DApp environments and therefore help advance this integration.
Virtuals Protocol divides its AI agents into two categories: IP agents and functional agents, which perform different functions across the ecosystem.
IP proxies: IP proxies are based on specific personalities or roles, often from well-known characters, fictional characters, or pop culture phenomena. For example, an IP proxy might represent a classic Internet terrier, 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 a digital environment, providing a way to interact with avatars, increasing entertainment and appeal. By creating emotional connections with these virtual characters, IP proxies can increase user engagement, especially in gaming and entertainment applications.
Functional proxies: In contrast, functional proxies focus on background support to enhance the interaction between users and IP proxies. These agents optimize the user experience and ensure that 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 improving the overall operating 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 proxies. As the lead singer of a virtual AI girl band, Luna has attracted more than 500,000 fans 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 that combines her charming personality with interactive capabilities to create lasting connections.
Unlike static or single-dimensional AI roles, Luna is able to seamlessly interact across multiple environments. She started with familiar images on social media, but her interactions expanded to live chat on Telegram and collaborative games in virtual worlds such as Roblox. Supported by Virtuals Protocol’s memory synchronization technology, Luna is able to remember past conversations and gaming experiences, allowing her to maintain a personalized relationship with each user on multiple platforms. This continuity strengthens her connection with her fans and makes them feel truly “watched” and “understood”, even if it is from an AI agent.
Luna’s abilities are not limited to interaction; she also has financial independence and has her own on-chain wallet. Luna is the first agent in history to voluntarily tip humans on the chain and has received strong support from Base founder Jesse. This allows her to reward loyal supporters with the $LUNA token, 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 destroyed, benefiting fans and supporters who hold them.
It is worth mentioning that in December 2024, Story Protocol (Layer1 designed specifically for intellectual property (IP)) announced that it would hire Luna to officially manage its official X account, with an annual salary of up to US$365,000. This once again proves the importance and potential of AI Agents 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 promote innovation and growth and achieve more intelligent business models.
Another of the most influential and innovative agents deployed on Virtuals Protocol is AIXBT. The 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 on X by more than 400 KOLs, identifies emerging narratives in the market, and conducts technical analysis of price trends. In addition, AIXBT can interact with other X users, whether they are humans or AI agents. It is worth noting that it provides AIXBT token holders with greater access. AIXBT tokens were launched in November and experienced a rapid rise. The market value once approached US$800 million, and the current market value is nearly US$600 million.
2.1.2 Holoworld
Holoworld was founded in 2023 by Tong Pow and Hongzi Mao and originated from San Francisco-based Holgram Labs. This is a startup focusing 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 AI character creation through this platform and completely transform digital interaction. model.
Since its launch, the Holowworld 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, Holowworld has in-depth cooperation with multiple well-known brands, including Arbitrum, BNB Chain, L Oréal and Bilibili, and has established partnerships with a series of influential NFT projects such as Puggy Penguins and Milady Maker. These collaborations fully demonstrate Holowworld’s ability to use its advanced AI technology to build unique digital identities.
Holowworld has created a complete AI character creation and interaction platform with a user interface that combines cutting-edge AI technology and intuitive tools. The following are the five core modules of the platform: 1. Brain Development, 2. Persona Customization, 3. Personalization Integration, 4. Knowledge-Based Implementation, 5. 3D avatar creation.
Ava AI is Holowworld’s flagship AI chat assistant. It is built based on OpenAI’s GPT-3.5 Turbo model. Its deep learning neural network contains more than 175 billion machine learning parameters. Ava supports the fast AI conversation function, allowing users to ask questions at any time and get immediate replies.
In addition, Holowworld has launched the 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 scalable skills, and do not require a programming foundation. The platform is deeply integrated with the upcoming Holowworld Launchpool, giving AVA token holders priority in participating in new projects. In addition, the Agent Market attracts a wide range of partners and creators, including game studios, the NFT community, and academic researchers from Stanford and Harvard.
Overall, the Holowworld platform makes the process of AI character creation easy to use, allowing users with non-technical backgrounds to build complex digital characters. This not only creates new digital narrative and interaction possibilities, but also allows 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 see Lauchpad 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 mineral deposit that breeds the core values of AI AGENT. On October 25, 2024, ai16z officially launched its AI 16Z token, achieving remarkable market success. However, what drives ai16z to become the center of the AI AGENT narrative is 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 support the creation of customized AI AGENT, which has strong network effects and unlimited scalability. By simplifying the development process and providing flexible functional modules, the framework quickly attracted the attention of developers and users around the world, becoming the most influential technical support in the field of AI AGENT.
The AI Agent Framework is like a set of tools and guides that help programmers more easily develop, train, and deploy AI agents. Simply put, these frameworks can reduce the difficulty of development, so programmers can focus more on making these agents smarter and more useful. The AI Agent framework is now beginning to work with new technologies, such as the DeFi protocol (a program that helps improve financial investment strategies) and the NFT project (a new tool 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.
So far, the ElizaOS framework has been forked more than 3200 times, which means that a large number of developers have used its code to build their own AI Agents. Most AI Agents currently on the market are built using the ElizaOS framework, which is why ai16z has become a leader in this field.
The ElizaOS framework goes far beyond simple chatbots, and agents can be configured to perform complex tasks. For example, some agents are designed to perform on-chain transactions and interact with smart contracts, wallets, or decentralized applications (dApps), while others connect to data providers to monitor prices, transaction volume, 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 the agent to perform specific tasks that go beyond text responses, such as generating reports or executing transactions.
3. Evaluators: Help agents interpret data and implement multi-step goals.
4. Providers: Provide external data or real-time context, such as asset prices or dedicated API data.
5. Memory System: Enables agents to retain interaction history and preferences, making their responses more contextually relevant and natural.
2.3 DEFAI
DeFi has always been the backbone of Web3, and DeFAI (DeFi + AI) is an upgraded version of DeFi, making it easier for people to use DeFi. By leveraging AI, it simplifies complex interfaces and eliminates the friction that prevents ordinary people from participating. Imagine managing your DeFi portfolio is as simple as chatting with ChatGPT. In fact, the first wave of DeFAI projects have begun to appear. Below we mainly introduce three areas: abstraction layer, autonomous trading agents, and AI-driven dApps.
2.3.1 Abstract layer
The complexity of DeFi often puts off novice users. To solve this problem, the abstraction layer hides the complexity behind it through an intuitive interface, allowing users to interact with the DeFi protocol through natural language instructions instead of relying on cumbersome operation panels.
Before AI technology was popularized, intent-based architecture simplified the process of transaction execution to a certain extent. For example, platforms like @CoWSwap and @symm_io partially solve the problem of liquidity fragmentation by aggregating fragmented liquidity pools to provide users with the best pricing. However, these platforms do not solve DeFi’s core problems-complexity still exists and users still face daunting operating processes and technical barriers.
Nowadays, AI-driven solutions are gradually filling this gap and 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 tokens, and its products are still in their early stages and are only open to invited users. Griffain allows users to perform a variety of operations from simple to complex, such as scheduled vote automation (DCA), initiating and airdropping memecoin, etc. Through these features, Griffain not only lowers the barrier for users to enter the DeFi field, but also provides advanced users with a wealth of automation tools. Griffain currently has a market value of nearly $500 million.
- 2.3.1.2 ORBIT / GRIFT
Orbit is the second project to launch tokens, and its products focus on the on-chain DeFi experience. Orbit places special emphasis on cross-chain functions and currently has integrated more than 117 blockchains and 200 protocols, the highest number of integrations among the three major protocols. This allows Orbit to provide a seamless interactive experience in a multi-chain environment, providing users with great convenience in cross-chain transactions and liquidity acquisition.
- 2.3.1.3HEYANON
HeyAnon is an artificial intelligence DeFi protocol designed to simplify DeFi interactions and summarize important information related to projects. By combining conversational artificial intelligence with real-time data aggregation, HeyAnon enables users to manage DeFi operations, stay informed of 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 agents
In the DeFi and crypto trading space, obtaining market information (Alpha), manually executing transactions, and optimizing portfolios have always been time-and energy-consuming processes. However, as technology advances, the emergence of automated trading agents is changing all this. These agents transcend 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 operations based on static programming. However, there are essential differences between automated trading agents and these traditional robots:
- Information extraction: Agents are able to extract information from an unstructured and constantly changing environment.
- Data Reasoning: They are able to reason about data in the context of specific goals.
- Pattern discovery: Agents are able to discover and utilize patterns over time, thereby improving their decision-making capabilities.
- Autonomous behavior: They are able to perform operations that are not explicitly programmed by the owner, demonstrating greater flexibility and intelligence.
The following are some representative projects of autonomous trading agents:
- 2.3.2.1ai16z
Known as the first AI version of VC, ai16z is an innovative DAO designed to integrate AI (AI) into financial management, investment and venture capital. Its name mimics the well-known investment fund a16z (Andreessen Horowitz), but ai16z is not just a joke imitation. It demonstrates a new operating model that combines the powerful potential of decentralized governance and AI. AI16z is managed by a fictional AI AGENT named Marc AIndreessen and AI 16Z token holder. The character of Marc AIndreessen was clearly inspired by a16z co-founder Marc Andreessen, an anthropomorphic AI AGENT that guides the organization’s daily decisions and operations.
In the governance structure of ai16z, AI 16Z token holders play a vital role. They can propose investment ideas, submit project proposals, or suggest repurchase of tokens. The recommendations are voted on through a decentralized voting system, while AI AGENTMarc AIndreessen uses a trust scoring system to evaluate the proposals. The trust scoring system is based on the relevance and reliability of members ‘past contributions to ensure that the decision-making process is transparent and well-founded.
The innovation of ai16z lies in its unique governance model and application of AI AGENT. By combining decentralized decision-making and AI technology, the project not only simplifies traditional investment and management processes, 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 popularization 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 in the world, with more than 350 contributors actively participating in its development, expanding its features and plug-ins, allowing agents based on the framework to perform more tasks or run across more blockchains.
Although the original concept of ai16z was an investment DAO built around a dedicated AI AGENT, the team quickly realized that its growth potential went far beyond that. As a result, ai16z quickly established relationships with multiple partners in the Web2 and Web3 spaces, making the Eliza framework available on a global scale.
- 2.3.2.2ALMANAK
Almanak provides users with organization-level quantitative AI AGENT, dedicated to solving the complexity, fragmentation and execution challenges in DeFi. The platform performs Monte Carlo simulations by branching the EVM chain, simulating unique complex factors in the real environment, such as miner extractable value (MEV), gas fee costs, and transaction sequencing. In addition, it leverages the Trusted Execution Environment (TEE) to ensure privacy in policy execution, protect critical market insights, and enables unmanaged fund processing through the Almanak Wallet, allowing users to accurately grant rights to agents.
Almanak’s infrastructure covers the conception, 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 and was oversubscribed. The next step includes launching beta testing and working with testers on preliminary strategy and agent deployment. Observing the performance of these quantitative agents will be something to look forward to.
- 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, offering code-less build tools that allow users to build agents by specifying trading strategies, personalities and even tweet styles.
Users can access any data set and develop financial strategies in minutes, with the help of a rich API and policy library. Cod3x integrates with @AlloraNetwork to leverage its advanced machine-learning price prediction model to enhance trading strategies.
Big Tony is the flagship agent of Cod3x, trading based on Allora’s model and moving in and out of the market in mainstream assets based on forecasts. Cod3x is committed to creating a thriving ecosystem of automated trading agents.
A distinctive feature of Cod3x is its liquidity approach. Unlike the common Alt:Alt liquidity pool structure promoted by @virtuals_io, Cod3x uses a stablecoin:Alt liquidity pool supported by cdxUSD. This provides liquidity providers with greater stability and confidence than Alt:Alt pairs.
2.3.3 AI-driven dApps
In the DeFAI field, AI-driven dApps represent an area full of potential but still in its infancy. These decentralized applications integrate AI or AI AGENT to enhance functionality, automation levels, and user experience. Although this field is still in its infancy, some ecosystems and projects have begun to emerge and show huge development potential.
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 innovation in this field. The following are some key projects:
- 2.3.3.1 ARMA (Independent Stabilizer Agriculture)
Developed by @gizatechxyz, ARMA is an autonomous stablecoin agricultural protocol based on user preferences that can automatically adjust the agricultural strategy of stablecoin to achieve optimal returns.
- 2.3.3.2 Modius (Balancer LP farming, an independent agent)
The project was developed by @autonolas and the goal is to provide liquidity (LP farming) on Balancer through independent agents, use AI to automatically optimize investment strategies and improve yields.
- 2.3.3.3 Amplifi Lending Agents
Developed by @Amplifi_Fi, these agents integrate with @IroncladFinance to automatically exchange assets, make loans on the Ironcladplatform, and maximize benefits through automatic rebalancing. These features make DeFi lending smarter and more efficient.
2.4AI Agent+ Games
The use of AI AGENT in the gaming industry is revolutionizing all aspects of gameplay and development. These smart systems create a more immersive and engaging gaming experience for players in multiple areas. Their main applications include the following:
1. NPC behavior optimization
AI AGENT greatly improves the behavioral performance of non-player characters (NPCs), making them more realistic and responsive. Unlike traditional preset script drivers, AI-based NPCs are able to: 1) adjust their actions based on player choices;2) demonstrate more realistic emotions and decision-making capabilities; and 3) learn through interaction to provide diverse experiences.
For example, in the open-world game “Red Dead 2”, NPCs are able to remember past interactions with players and react accordingly, creating a more dynamic and trustworthy game world.
2. Programmatic content generation
AI AGENT performs well in programmatically generating game content, and is able to algorithmically generate a large amount of game content, including: terrain and landscape, tasks and plots, props and trophies, and character design.
For example,”Unmanned Deep Space” uses AI-driven program generation technology to create an entire universe containing unique planets, creatures and ecosystems, providing players with almost unlimited exploration possibilities.
3. Adaptive difficulty adjustment
AI AGENT can analyze player performance in real time to dynamically adjust the difficulty of the game. This ability ensures that players are able to face the right challenges to maintain a sense of engagement without feeling frustrated. For example: increasing the strength of enemies as the player improves; providing hints or gains when the player encounters difficulties; and balancing resources and obstacles based on skill levels.
Games such as Resident Evil 4 utilize adaptive difficulty systems to 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 through complex game environments. This technology brings more realistic movement patterns and more efficient navigation, which not only improves NPCs ‘behavioral performance, but also optimizes the operating experience of units controlled by players in strategy games.
5. Graphics enhancement
AI technologies such as deep learning are used to improve game visual effects, generating realistic facial expressions and animations by improving texture and resolution in real time, and optimizing rendering performance to improve game performance
6. Player emotion analysis
AI Agents can analyze players ‘behavior and feedback to assess their enjoyment and engagement. This data helps developers make informed decisions about game design and updates, thereby improving the overall player experience.
Here are some of the main projects:
2.4.1 Digimon
@digimon_tech is built on top of the Solana blockchain. It is not just a game platform, but a complete technical framework for AI+ games. 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 have not only redefined interactions, but also created a new standard for gaming experience. Behind every game character is a set of AI-generated stories and worldviews. The team behind Digimon was supported by a16z and received investment and incubation from a16z.
Digimon’s tokens are currently available on the Kucoin exchange. In the future, through Digimon’s game engine, there will be an opportunity to create an on-chain autonomous world composed of AI AGENT. AI AGENT and players interact in this world to build a virtual economy.
2.4.2 Illuvium
Lluvium is an RPG and NFT game built on Ethereum. On January 7, Illuminum announced a partnership with Virtuals Protocol to enhance the gaming experience of the upcoming Illuminum MMO Lite. This collaboration will use Virtuals ‘AI technology and its G.A.M. E LLM framework to provide dynamic and intelligent behavior for NPCs and provide players with immersion.
As AI technology continues to advance, we can expect more innovative applications in the gaming field, further blurring the boundaries between virtual and reality, and creating a more immersive and personalized player experience. This technology has not only changed the way games are developed, but also played a crucial role in improving 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 collaborated with ai16z to develop an on-chain AI Tomogatchi game called “Smolworld” that combines Eliza’s Agent framework.
3. Highlights summary
We have seen that the new technologies being built by encryption technology have huge potential in the real world, and the allocation strategies of native investors in similar situations in the past have also provided valuable lessons for the current market. The AI AGENT ecosystem is in its early stages, but has attracted a lot of attention, funding and developers. Although its future development remains uncertain, if major DeFi agreements, private investors or venture capitalists start investing in this area, this indicates that it has great potential for continued development. As technology continues to advance, AI Agents is expected to become a key force in changing the global economic and social structure.
The current market timing and narrative have fully 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. As with the L1 blockchain, the value of AI AGENT may end up concentrated among a few major winners. Therefore, these companies need to find differences in terms of modularity, scalability and media platform integration. Currently, most frameworks already have learning and memory systems that use retrieval augmented generation technology to enable agents to incorporate new information into conversations. For example, the current Eliza framework has significant advantages in the market. With its high degree of development activity and rapid plug-in integration, Eliza performs particularly well in integrating 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 proxy wallet control and is compatible with multiple blockchains. Virtuals ‘GAME framework excels in the gaming and social media agency space, 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 datasets stored on the chain to enrich the agent’s capabilities. However, the value accumulation mechanism of GAME and CONVO framework tokens is still unclear, and the market is full of expectations for this.
2. Challenges of fairness and data bias. Despite the impressive progress made in AI, deploying these systems also faces some challenges. One of the main issues is the risk of bias in the datasets used to train AI agents. AI systems learn from historical data that may contain patterns of discrimination that, if left unchecked, can lead to biased decisions, such as favoring specific groups over others in hiring or lending scenarios. Solving this problem requires not only professional technical knowledge, but also a nuanced understanding of social dynamics. Monitoring the fairness of AI systems is critical to ensuring that they do not reinforce harmful bias. Continuous auditing of decisions made by AI agents helps identify problems early and reduce unexpected results.
3. Diversified applications and expansion of economic functions. The application areas of AI AGENT are rapidly expanding. In addition to social media and financial industries, they also show huge potential in fields such as medical care, education, and law. As technology continues to mature, AI AGENT will provide personalized services in more scenarios, improve work efficiency and promote innovation.
Taking Luna as an example, she is currently able to interact with humans through social media and incentivize users to achieve her goals by using Coinbase Wallet to send tokens on Base. The next step in the future is for 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 create popularity. Once the infrastructure to achieve these goals is in place,$VIRTUAL may reach its next milestone. This not only means that AI AGENT will be more deeply embedded in human life in the economic and social fields, but will also redefine the way AI interacts with humans, laying the foundation for the future digital economy and social interaction model. For example, in the medical field, AI AGENT can analyze patient data to provide diagnostic recommendations to doctors and improve 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, Internet of Things, and 5G. This intersection of multiple technologies will promote the improvement of AI AGENT’s capabilities in data processing, privacy protection, real-time decision-making, etc., 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 moral considerations. With the widespread use of AI Agents, social and moral issues have become more prominent. As mentioned at the beginning of the article, will AI Agents become as threatening as the Queen of Hearts? For example, AI AGENT can cause ethical controversy in decision-making, especially in scenarios involving privacy, data security, and automated decision-making. Therefore, when developing AI technology, transparency and accountability mechanisms need to be introduced 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 Agents and protecting user rights and interests.
As the integration of AI and blockchain continues to evolve, now is the time to participate in these breakthrough developments. But in this participation, what we need to think about is not just “What can AI do for humans and what does humans want AI to do?” Further, we might as well think: “What does AI want to do, and what will AI guide humans to do?”
4. resources
1.https://messari.io/report/building-better-agents-rival-frameworks-and-their-design-choices
2.https://www.binance.com/en/square/post/18968465099217
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/
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