1. Background Introduction

What is an AI Agent?

An AI Agent is an intelligent entity capable of perceiving its environment, making decisions, and executing actions, primarily based on LLM (Large Language Model). It possesses autonomy and adaptability, allowing it to independently complete complex tasks and demonstrate highly intelligent collaborative capabilities. Unlike traditional large models that require explicit instructions for interaction, an AI Agent can accept goal instructions, autonomously decompose tasks, plan action steps, and call tools during execution to complete tasks. Its core advantage lies in its ability to think and act independently. Compared to early voice assistants like Siri and Microsoft's Copilot, the AI Agent resembles a primary "driver," continuously improving task completion efficiency and accuracy through self-learning, feedback adjustments, and long-term optimization.

The working principle of an AI Agent can be summarized into four core capabilities: perception, analysis, decision-making, and execution. First, the AI Agent perceives the environment through sensors or data interfaces to gather external information. Then, it utilizes analysis 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, ultimately translating decisions into specific actions to achieve target tasks. In this process, short-term and long-term memory modules provide information storage and retrieval functions, enhancing its ability to handle complex tasks. Additionally, the AI Agent dynamically calls external tools (such as calendars, search engines, APIs, etc.) based on task requirements, overcoming the limitations of traditional large models constrained by static training data and tool dependencies, significantly enhancing the scalability of model capabilities.

Image source: Former OpenAI Chief Safety Researcher Lilian Weng "LLM Powered Autonomous Agents"

Overview of AI Agent Development in Web2

By 2025, the AI Agent industry is at a critical stage 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 tech giants like NVIDIA; the midstream focuses on the integration and platform services of AI Agents; and the downstream revolves around the development and promotion of industry vertical applications and general intelligent agents, gradually showing a trend of diversified development. In terms of applications, both C-end and B-end markets exhibit tremendous potential: C-end applications focus on enhancing user experience, providing more convenient interaction methods, while B-end aims to promote enterprise intelligent transformation, empowering business decisions and operations through cost reduction and efficiency improvement.

Leading companies in the industry have begun to engage in fierce competition over the practical applications of AI Agents. Google has released Gemini 2.0 and launched three AI Agent products: Project Astra (general), Project Mariner (browser operations), and Jules (programming). OpenAI's Sam Altman stated that 2025 will be the year AI Agents become mainstream and announced the upcoming launch of several innovative technologies, including AGI, an upgraded version of GPT-4o, and personalized features. NVIDIA CEO Jensen Huang predicts that AI Agents are expected to become the next robotics industry, creating trillions of dollars in market value.

Concept of AI Agent in Blockchain

The rise of AI Agents in blockchain is a product of the continuous integration and development of blockchain technology and AI. As a decentralized infrastructure, blockchain provides trustworthy data records and transparent behavior verification mechanisms for the operation of AI Agents, while the development of AI technology enables agents to possess complex judgment and execution capabilities, allowing them to autonomously complete a series of economic activities, resembling a self-operating virtual economy. Within this framework, AI Agents can not only participate in the existing blockchain ecosystem but also drive innovation in more scenarios, such as automatically completing market analysis, planning, and task execution in DeFi through smart contracts, or creating and managing digital assets as "residents" in virtual worlds.

Moreover, the application of AI Agents in blockchain directly enhances user experience and productivity, especially in areas with high complexity in on-chain operations. One of the biggest obstacles to blockchain adoption is the complexity and high entry barriers of operations, while the natural language interaction model of AI Agents allows users to manage wallets, filter the best DeFi investment options, conduct cross-chain transactions, or automatically execute plans based on market conditions with simple commands, significantly reducing the learning costs for new users while greatly improving efficiency and convenience.

The potential of AI Agents in the blockchain ecosystem is reflected not only in optimizing user operations but also in broader application scenarios. Creator economy, market sentiment monitoring, smart contract auditing, decentralized autonomous organization (DAO) governance voting, and even the issuance of MEME coins can achieve higher efficiency and fairness through AI Agents. The performance of AI Agents in de-emotionalization and precise execution makes them more reliable than most people under established conditions. At the same time, the immutability of blockchain provides trustworthy data sources for AI, mitigating 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 existing incentive models and drive profound changes in the blockchain ecosystem.

2. Applications of AI Agents in Blockchain

1. AI Agent Framework
The AI Agent framework is a foundational tool for developing, training, and deploying agents, providing developers with efficient technical support for building intelligent agents. These frameworks reduce development complexity through standardized development environments and common components, allowing developers to focus on implementing innovative features. Currently, AI Agent frameworks are gradually integrating DeFi protocols, NFT projects, etc., exploring cross-platform collaboration and interoperability. For example, by combining DeFi to optimize investment strategies or developing intelligent tools for NFTs, AI Agent frameworks are building a more open and interconnected ecosystem, becoming a focal point 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 agents and their related tokens, functioning similarly to meme coin issuance platforms like Pump.fun, etc. Users can easily create and deploy AI Agents on these platforms while seamlessly integrating them with social media platforms such as Twitter, Telegram, and Discord for automated user interactions. This model lowers the barriers to 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 fields of AI Agents encompass investment, entertainment, data analysis, etc., demonstrating immense growth potential.

• Fund Management
  AI Agents have transitioned from auxiliary tools to the core of value creation in fund management, capable of formulating investment strategies, adjusting asset allocations, and predicting market trends in real-time. These agents enhance the efficiency of tasks such as arbitrage and risk hedging through automation, meeting the demands for scaling and specialization in the crypto market, injecting new competitiveness into fund management. Representative projects: AIXBT, Ai16z, etc.

• DeFAI: The Combination of AI and DeFi
  DeFAI simplifies operational processes and lowers entry barriers by introducing AI technology into DeFi. Users can issue simple commands through natural language, such as "one-click cross-chain transaction" or "set up a regular investment plan," 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, which have been realized 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, Ai16Z DAO utilizes agents for fundraising and investment management, showcasing the potential of AI in decentralized governance. Such applications not only enhance governance efficiency but also significantly reduce the time and effort required from members.

• Gaming

• AI Agents can also be used in game design. By simulating player behavior, AI Agents can assist game developers in optimizing game design, enhancing the fun and playability of games. Additionally, AI Agents can serve as game assistance tools, helping players improve their gaming skills. For instance, AI Agents can analyze players' operational habits and provide targeted suggestions and guidance, thereby helping players enhance their gaming skills. Representative projects: HYPER, etc.

• Automated Quantitative Trading
  In the field of quantitative trading, AI Agents can devise diverse strategies based on market conditions, such as executing arbitrage trades in highly volatile markets or employing trend-following strategies in trending markets. With the support of exchanges for automated trading tools, AI Agents have vast application potential in future trading.

4. AI MEME Projects
AI MEME refers to meme coin projects derived from the concept of AI Agents, which typically lack deep technical or product support. These projects rely on meme culture to attract attention with high volatility and speculation. Although the technical content is limited, their market popularity and community sentiment drive explosive short-term growth, becoming a unique phenomenon in the crypto market. Representative projects: GOAT, ACT, etc.

3. Future Development Trends

By 2025, the development of AI Agents in the crypto and Web3 fields is expected to reach a significant inflection point. The technology of AI Agents is shifting from a tool attribute of single applications to the construction of an ecosystem of multi-agent collaboration, continuously expanding its boundaries. In the DeFi sector, AI Agents have achieved fund management and smart contract execution, and are expected to evolve into intelligent agents with autonomous economic capabilities, 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. 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, propelling the crypto ecosystem into a new phase of intelligence and efficiency. Meanwhile, the development of AI Agents in Web3 also faces challenges and opportunities.

Privacy and security have become key issues, especially against the backdrop of AI's increasing reliance on personal data. Web3 offers unique advantages in ensuring data privacy and security through blockchain, enabling AI Agents to gain broader applications in industries with high privacy demands, such as healthcare and finance. Additionally, computing power and data costs pose bottlenecks for multi-agent collaboration, but through blockchain and token economies, idle computing power and data resources can be effectively integrated, lowering development and operational barriers. Looking ahead, AI Agents have the potential to serve as a new type of infrastructure for Web3, deeply integrating with other core elements to create new application models, upgrading from a tool role to an indispensable ecological pillar, injecting more innovation and value into the crypto industry.

Risk Warning:

All cryptocurrency investments, including yield products, are highly speculative and involve significant risks. Past performance of products does not guarantee future results. The cryptocurrency market is highly volatile, and before making any investment decisions, you should carefully assess whether trading or holding digital currencies is suitable based on your personal investment goals, financial situation, and risk tolerance, and consult a professional financial advisor. The information in this article is for reference only and does not constitute any investment, legal, or tax advice. The author and publisher are not responsible for any losses resulting from the use of the information in this article.