1. Background Introduction What is AI Agent?

AI Agent is an intelligent entity that can perceive the environment, make decisions and perform actions, mainly based on LLM (Large Language Model). It is autonomous and adaptable, can complete complex tasks independently, and demonstrates highly intelligent collaboration capabilities. Compared with traditional large models, which require clear instructions to interact, AI Agents can independently decompose tasks, plan action steps, and call tools to complete tasks during execution after receiving target instructions. The core advantage lies in its independence. The ability to think and act. Compared with early voice assistants such as Siri and Microsoft's Copilot, AI Agent is more like a junior "main driver" that can continuously improve the efficiency and accuracy of task completion through autonomous learning, feedback adjustment, and long-term optimization.

The working principle of AI Agent can be summarized into four core capabilities: perception, analysis, decision-making and execution. First, the AI ​​Agent senses the environment through sensors or data interfaces and obtains external information. Subsequently, analysis tools such as large language models are used to extract valuable features and patterns. Based on the analysis results, the AI ​​Agent formulates a reasonable action plan, and finally converts the decision into specific actions to complete the target task. In this process, the short-term and long-term memory modules provide information storage and review functions to enhance the ability to cope with complex tasks. In addition, the AI ​​Agent dynamically calls external tools (such as calendars, search engines, program interfaces, etc.) according to task requirements. , which solves the limitations of traditional large models limited by static training data and tool dependence, and significantly improves the scalability of model capabilities.

Image source: Lilian Weng, former Open AI chief security researcher "LLM Powered Autonomous Agents》

Overview of the development of AI Agent in Web2

In 2025, the AI ​​Agent industry is in a critical period of accelerated development. From the perspective of the industry chain, the upstream is dominated by computing power and hardware providers, data suppliers, and algorithm and large model developers, such as technology giants such as NVIDIA; the midstream focuses on the integration and platform services of AI Agents; the downstream focuses on industry verticals The development and promotion of applications and general-purpose agents are gradually showing a diversified development trend. At the application level, both the C-side and B-side markets show great potential: C-side applications focus on improving user experience and bringing moreConvenient interactive methods, while the B-side is committed to promoting the intelligent transformation of enterprises and empowering business decision-making and operations through cost reduction and efficiency improvement.

Leading companies in the industry have begun to compete fiercely in the implementation of AI Agents. Google released Gemini 2.0, along with three AI Agent products: Project Astra (general purpose), Project Mariner (browser operation), and Jules (programming). OpenAI’s Sam Altman said 2025 will be the year AI Agents become mainstream, announcing upcoming innovations covering AGI, upgraded GPT-4o, and personalization capabilities. NVIDIA CEO Jensen Huang predicts that AI Agent is expected to become the next robotics industry and create trillions of dollars in market value.

The concept of AI Agent in blockchain

The rise of AI Agent in blockchain is the product of the continuous integration and development of blockchain technology and AI. As a decentralized infrastructure, blockchain provides credible data records and transparent behavior verification mechanisms for the operation of AI Agents. The development of AI technology enables intelligent agents to have complex judgment and execution capabilities and be able to complete tasks independently. A series of economic behaviors are like a virtual economy that can operate autonomously. Under this framework, AI Agent can not only participate in the existing ecosystem of the blockchain, but also promote innovation in more scenarios, such as automatically completing market analysis, plan formulation and execution tasks through smart contracts in DeFi, or in the virtual world Create and manage digital assets as a "resident".

In addition, the application of AI Agent in the blockchain directly improves user experience and production efficiency, especially in the field of highly complex on-chain operations. One of the biggest obstacles to the current popularization of blockchain is the complexity and high threshold of operations, and the natural language interaction mode of AI Agent can complete wallet management, screen the best DeFi investment solutions, cross-chain transactions or according to the market through simple instructions. Functions such as automatic market execution plans greatly reduce the learning cost for new users, while significantly improving efficiency and convenience.

The potential of AI Agent in the blockchain ecosystem is not only reflected in the optimization of user operations, but also in a wider range of application scenarios. Creator economy, market sentiment monitoring, smart contract auditing, decentralized autonomous organization (DAO) governance voting, and even the issuance of MEME coins can all be achieved through AI Agent.efficiency and fairness. The performance of AI Agent in de-emotional and precise execution makes it more reliable than most humans under given conditions. At the same time, the non-tamperability of blockchain also provides AI with a trusted source of data, which makes up for the risks that AI systems may bring due to data quality issues. Furthermore, by utilizing on-chain data and computing power, AI Agent has the potential to subvert the existing incentive model and promote deep changes in the blockchain ecosystem.

2. Application of AI Agent in Blockchain 1. AI Agent Framework

AI Agent framework is a basic tool for developing, training and deploying agents, providing developers with Provides technical support for building intelligent agents efficiently. These frameworks reduce development complexity through standardized development environments and common components, allowing developers to focus on the implementation of innovative functions. Currently, the AI ​​Agent framework is gradually integrating DeFi protocols, NFT projects, etc. to explore cross-platform collaboration and interoperability. For example, by combining DeFi to optimize investment strategies or developing intelligent tools with NFTs, the AI ​​Agent framework is building a more open and connected ecosystem, becoming the focus of market attention. Representative projects: Ai16z, ARC, Swarms, Zerebro, etc.

2. AI Agent Launchpad

AI Agent Launchpad is used for intelligent agents and their related generations. A currency issuance platform with functions similar to Meme currency issuance platforms, such as Pump.fun, etc. Users can easily create and deploy AIAgents on these platforms while seamlessly integrating them with social media platforms such as Twitter, Telegram and Discord to automate user interactions. This model lowers the threshold for distribution and promotion, brings a more convenient creation experience to users, and expands the application scenarios of AI Agents. Representative projects: Virtuals, Clanker, etc.

3. AI Agent application scenarios

Direct use of AI Agent Application areas include investment, entertainment, data analysis, etc., showing great growth potential.

Fund Management

AI Agent has transformed from an auxiliary tool to a value creation in fund management nuclearWith the ability to formulate investment strategies, adjust asset allocation, and predict market trends in real time. These agents improve the efficiency of tasks such as arbitrage and risk hedging through automated operations, meet the needs for scale and specialization in the encryption market, and inject new competitiveness into fund management. Representative projects: AIXBT, Ai16z, etc.

DeFAI: The combination of AI and DeFi

DeFAI simplifies the Streamline operational processes and lower entry barriers. Users can issue simple instructions through natural language, such as "complete cross-chain transactions with one click" or "set up a regular investment plan", thereby achieving more efficient asset management and trading operations. The main applications of DeFAI include cross-chain operation optimization, autonomous trading agents, and intelligent information analysis. It has been implemented in multiple platforms such as Griffain, Orbit and Neur, and represents projects: GRIFFAIN, BUZZ, NEUR, etc.

DAO automated management

The application of AI Agent in DAO includes voting decision optimization and governance automation. For example, Ai16Z DAO uses intelligent agents for fund-raising and investment management, demonstrating the potential of AI in decentralized autonomy. Such applications not only improve governance efficiency, but also significantly reduce members’ time and energy investment.

Game

AI Agent can also be used in game design. By simulating player behavior, AI Agent can help game developers optimize game design and improve game fun and playability. In addition, AI Agent can be used as a game auxiliary tool to help players improve their game level. For example, AI Agent can analyze players' operating habits and provide targeted suggestions and guidance to help players improve their gaming skills. Representative projects: HYPER, etc.

Automated quantitative trading

In the field of quantitative trading, AI Agent can formulate various strategies, such as executing arbitrage trades in high-volatility markets or employing trend-following strategies in trending markets. Combined with the exchange's support for automated trading tools, AI Agent has broad application potential in future transactions.

4. AI MEME project

AI MEME is a Meme coin project derived from the concept of AI Agent. Its core is usually not profound. technical or product support. Such projects rely on Meme culture to attract attention with high volatility and speculation. Although the technical content is limited, its market popularity and community sentiment have promoted short-term explosive growth, becoming a special phenomenon in the encryption market. Representative projects: GOAT, ACT, etc.

3. Future Development Trends

In 2025, AI Agent’s development in the fields of encryption and Web3 Development is expected to usher in an important flashpoint. From the tool attributes of a single application to the ecological construction of multi-agent collaboration, the boundaries of AI Agent technology are constantly expanding. In the field of DeFi, AI Agent has realized fund management and smart contract execution. In the future, it is expected to become an intelligent agent with independent economic capabilities, participate in more complex economic activities, and achieve economic autonomy. In DAO, AI Agent can optimize governance efficiency and decision-making process, while in quantitative trading, it can execute efficient arbitrage and risk management strategies through real-time data analysis. With the improvement of frameworks and standards, collaboration between AI Agents will give rise to new application scenarios, such as Agent social networks, economic settlement gateways and governance DAOs, pushing the encryption ecosystem to a new stage of intelligence and efficiency. At the same time, the development of AI Agent in Web3 also faces challenges and opportunities. Privacy and security are key issues, especially as AI increasingly relies on personal data. Web3 provides the unique advantage of ensuring data privacy and security through blockchain, allowing AI Agents to be more widely used in industries with high privacy requirements such as medical care and finance. In addition, computing power and data costs are bottlenecks faced by multi-agent collaboration, but through blockchain and token economy, idle computing power and data resources can be effectively integrated to lower the development and operation threshold. Looking to the future, AI Agent has the potential to serve as a new infrastructure for Web3, deeply integrated with other core elements, creating new application models, upgrading from a tool role to an indispensable ecological pillar, and injecting more innovation and value into the encryption industry.