I. Background Introduction

What is an AI Agent?

An AI Agent is an intelligent entity that can perceive the environment, make decisions, and take actions, primarily based on Large Language Models (LLMs). It has autonomy and adaptability, and can independently complete complex tasks, demonstrating highly intelligent collaboration capabilities. Compared to traditional large models, which require explicit instructions for interaction, an AI Agent can independently decompose tasks, plan action steps, and call upon tools to complete tasks during execution, with the core advantage being its ability to think and act independently. Compared to earlier voice assistants like Siri and Microsoft's Copilot, an AI Agent is more like a junior "pilot" that can continuously improve the efficiency and accuracy of task completion through self-learning, feedback adjustment, and long-term optimization.

The working principle of an AI Agent can be summarized as four core capabilities: perception, analysis, decision-making, and execution. First, the AI Agent perceives the environment and obtains external information through sensors or data interfaces. Then, it uses tools such as Large Language Models 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 the AI Agent with information storage and tracing functions, enhancing its ability to cope with complex tasks. Additionally, the AI Agent dynamically calls upon external tools (such as calendars, search engines, and APIs) based on task requirements, solving the limitations of traditional large models that are constrained by static training data and tool dependencies, and significantly enhancing the extensibility of the model's capabilities.

Image source: Lilian Weng, former Chief Security Researcher at Open AI, "LLM Powered Autonomous Agents"

The Development of AI Agents in Web2

In 2025, the AI Agent industry is in a critical period of accelerated development. From the industry chain perspective, the upstream is dominated by computing power and hardware providers, data suppliers, and algorithm and large model developers, such as tech giants like NVIDIA; the midstream focuses on the integration and platformization of AI Agents; and the downstream is around the development and promotion of vertical applications and general intelligent agents, gradually showing a diversified development trend. In terms of applications, both the consumer and business markets have great potential: consumer applications focus on enhancing user experience and providing more convenient interaction methods, while the business market is committed to driving enterprise digitalization and intelligence, empowering business decision-making and operations through cost reduction and efficiency improvement.

Leading companies in the industry have already begun fierce competition in the deployment of AI Agent applications. Google has released Gemini 2.0 and launched three AI Agent products: Project Astra (general), Project Mariner (browser operation), and Jules (programming). Sam Altman of OpenAI has stated that 2025 will be the year when AI Agents become mainstream, and has announced the upcoming release of multiple innovative technologies including AGI, an upgraded GPT-4, and personalized features. NVIDIA CEO Jensen Huang has predicted that AI Agents have the potential to become the next robotics industry, creating a market value of trillions of dollars.

The Concept of AI Agents in Blockchain

The rise of AI Agents in Blockchain is the result of the continuous integration and development of Blockchain technology and AI. Blockchain, as a decentralized infrastructure, provides a trusted data recording and transparent behavior verification mechanism for the operation of AI Agents, while the development of AI technology has endowed intelligent agents with the ability to make complex judgments and execute actions, enabling them to autonomously complete a series of economic activities, akin to a self-operating virtual economy. Within this framework, AI Agents not only can participate in the existing Blockchain ecosystem, but also can drive more innovative scenarios, such as automatically completing market analysis, planning, and task execution through smart contracts in DeFi, or acting as "residents" in virtual worlds to create and manage digital assets.

Furthermore, the application of AI Agents in Blockchain directly improves user experience and production efficiency, especially in complex on-chain operations. One of the current major obstacles to Blockchain adoption is the complexity and high threshold of operations, while the natural language interaction mode of AI Agents can complete functions such as wallet management, screening the best DeFi investment solutions, cross-chain transactions, or automatically executing plans based on market conditions through simple instructions, significantly reducing the learning cost for new users and substantially improving efficiency and convenience.

The potential of AI Agents 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 MEME coin issuance can be achieved with higher efficiency and fairness through AI Agents. The AI Agents' performance in de-emotionalization and precise execution makes them more reliable than most people under certain conditions. At the same time, the immutability of Blockchain provides a trusted data source for AI systems, which compensates for the risks that AI systems may face due to data quality issues. Furthermore, by leveraging on-chain data and computing power, AI Agents have the potential to disrupt the existing incentive models and drive deeper transformations in the Blockchain ecosystem.

II. Applications of AI Agents in Blockchain

1. AI Agent Framework

The AI Agent framework is a fundamental tool for developing, training, and deploying intelligent agents, providing developers with efficient technical support for building intelligent agents. These frameworks, through standardized development environments and common components, reduce the complexity of development, allowing developers to focus on the implementation of innovative functions. Currently, AI Agent frameworks are gradually integrating DeFi protocols, Non-Fungible Token (NFT) projects, and others, exploring cross-platform collaboration and interoperability. For example, by combining with DeFi to optimize investment strategies or with NFTs to develop intelligent tools, AI Agent frameworks are building a more open and interconnected ecosystem, becoming a focus of market attention. Representative projects: Ai16z, ARC, Swarms, Zerebro, etc.

2. AI Agent Launchpad

The AI Agent Launchpad is a platform for the issuance of intelligent agents and their related tokens, similar to MEME coin issuance platforms like Pump.fun. Users can easily create and deploy AI Agents on these platforms, and seamlessly integrate them with social media platforms such as Twitter, Telegram, and Discord, achieving automated user interaction. This model lowers the threshold for issuance and promotion, providing users with a more convenient creation experience and expanding the application scenarios of AI Agents. Representative projects: Virtuals, Clanker, etc.

3. AI Agent Application Scenarios

The direct application areas of AI Agents cover investment, entertainment, data analysis, and more, showing great growth potential.

• Fund Management

AI Agents in fund management have evolved from auxiliary tools to core value creators, capable of formulating investment strategies, adjusting asset allocations, and real-time forecasting of market trends. These intelligent agents, through automated operations, have improved the efficiency of tasks such as arbitrage and risk hedging, meeting the demand for scale and professionalization in the crypto market, injecting new competitiveness into asset management, representative projects: AIXBT, Ai16z, etc.

• DeFAI: The Combination of AI and DeFi

DeFAI, by introducing AI technology into DeFi, simplifies the operation process and lowers the entry threshold. Users can issue simple instructions in natural language, such as "one-click cross-chain transaction" or "set up a regular investment plan", to achieve more efficient asset management and trading operations. The main applications of DeFAI include cross-chain operation optimization, autonomous trading agents, and intelligent information analysis, which have been implemented on multiple platforms such as Griffain, Orbit, and Neur, representative projects: GRIFFAIN, BUZZ, NEUR, etc.

• DAO Automated Management

The application of AI Agents in DAOs includes optimizing voting decisions and automating governance. For example, the Ai16Z DAO uses intelligent agents for fundraising and investment management, demonstrating the potential of AI in decentralized autonomy. These applications not only improve governance efficiency, but also significantly reduce the time and effort invested by members.

• Games

AI Agents can also be used in game design. By simulating player behavior, AI Agents can help game developers optimize game design and improve the fun and playability of games. In addition, AI Agents can also be used as game auxiliary tools, helping players improve their gaming skills by analyzing their operation habits and providing targeted advice and guidance, representative project: HYPER, etc.

• Automated Quantitative Trading

  Here is the English translation of the text, with the specified terms preserved: In the field of quantitative trading, AI Agents can develop diversified strategies based on market conditions, such as executing arbitrage trades in high-volatility markets or adopting trend-following strategies in trending markets. Combining the support of exchanges for automated trading tools, the potential for AI Agents' application in future trading is vast. 4. AI MEME Project AI MEME is a Meme Coin project derived from the concept of AI Agents, whose core often lacks strong technical or product support. These projects rely on Meme culture to attract attention with high volatility and speculative nature. Although the technical content is limited, their market heat and community sentiment have driven explosive growth in the short term, becoming a special phenomenon in the crypto market. Representative projects: GOAT, ACT, etc. III. Future Development Trends By 2025, the development of AI Agents in the crypto and Web3 fields is expected to reach an important tipping point. Shifting from the tool attribute of standalone applications to the ecosystem construction of multi-agent collaboration, the boundaries of AI Agent technology are constantly expanding. In the DeFi field, AI Agents have realized fund management and smart contract execution, and are expected to become intelligent entities with autonomous economic capabilities in the future, participating in more complex economic activities and achieving economic autonomy. In DAOs, AI Agents can optimize governance efficiency and decision-making processes, while in quantitative trading, they can execute efficient arbitrage and risk management strategies through real-time data analysis. As frameworks and standards are improved, the collaboration between AI Agents will give rise to new application scenarios, such as Agent social networks, economic settlement gateways, and governance DAOs, driving the crypto ecosystem towards a new stage of intelligence and efficiency. At the same time, the development of AI Agents in Web3 also faces challenges and opportunities. Privacy and security have become key issues, especially in the context of AI's increasing dependence on personal data. Web3 provides the unique advantage of ensuring data privacy and security through blockchain, allowing AI Agents to gain wider application in industries with high privacy requirements, such as healthcare and finance. In addition, computing power and data costs are bottlenecks facing multi-agent collaboration, but through blockchain and token economics, idle computing power and data resources can be effectively integrated to reduce development and operational thresholds. Looking to the future, AI Agents have the potential to serve as a new infrastructure for Web3, deeply integrating with other core elements, creating new application models, and upgrading from a tool role to an indispensable ecosystem pillar, injecting more innovation and value into the crypto industry.