What is an AI Agent?
An AI Agent is a sophisticated entity designed to understand its environment, make decisions, and perform actions, primarily driven by Large Language Models (LLM). It operates autonomously and is highly adaptable, enabling it to handle complex tasks and demonstrate intelligent collaboration. Unlike traditional large models that need specific instructions for interaction, an AI Agent can interpret goal directives, independently break down tasks, plan actions, and utilize tools during execution to accomplish objectives. Its standout feature is the capacity for independent thought and action. Compared to early voice assistants like Siri and Microsoft’s Copilot, an AI Agent functions more like a “driver,” constantly enhancing task completion efficiency and precision through self-learning, feedback adjustments, and long-term optimization.
The operation of an AI Agent can be distilled into four fundamental abilities: perception, analysis, decision-making, and execution. Initially, the AI Agent perceives its environment via sensors or data interfaces to gather external information. It then uses analytical tools such as large language models to identify valuable features and patterns. Based on this analysis, the AI Agent creates a suitable action plan and executes decisions to achieve its objectives. Throughout this process, short-term and long-term memory modules provide information storage and retrieval, bolstering its capacity to manage complex tasks. Additionally, the AI Agent dynamically accesses external tools (like calendars, search engines, APIs, etc.) as needed, overcoming the static limitations of traditional large models and significantly enhancing scalability.
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 undergoing rapid development. The industry chain is segmented into upstream, midstream, and downstream. Upstream includes computing power and hardware providers, data suppliers, and developers of algorithms and large models, such as tech giants like NVIDIA. Midstream focuses on the integration and platform services of AI Agents. Downstream centers on the development and promotion of industry-specific applications and general intelligent agents, showing a trend towards diversification. In terms of applications, both consumer (C-end) and business (B-end) markets exhibit significant potential: C-end applications enhance user experience with more convenient interaction methods, while B-end aims to drive enterprise intelligent transformation, boosting business decisions and operations through cost reduction and efficiency gains.
Leading companies in the industry are competing fiercely in the practical application of AI Agents. Google has launched Gemini 2.0 and three AI Agent products: Project Astra (general), Project Mariner (browser operations), and Jules (programming). OpenAI’s Sam Altman stated that 2025 would be a pivotal year for AI Agents, announcing upcoming innovations, including AGI, an enhanced GPT-4o, and personalized features. NVIDIA CEO Jensen Huang predicts that AI Agents could become the next major industry, potentially generating trillions in market value.
Concept of AI Agent in Blockchain
The emergence of AI Agents in blockchain results from the ongoing integration and evolution of blockchain and AI technologies. Blockchain, as a decentralized infrastructure, offers reliable data records and transparent behavior verification for AI Agent operations. Concurrently, AI advancements enable agents to perform complex judgments and tasks autonomously, functioning like a self-operating virtual economy. Within this framework, AI Agents can participate in existing blockchain ecosystems and drive innovation in various scenarios, such as conducting market analysis, planning, and task execution in DeFi through smart contracts, or creating and managing digital assets as “residents” in virtual worlds.
Furthermore, the application of AI Agents in blockchain enhances user experience and productivity, particularly in areas with complex on-chain operations. One significant obstacle to blockchain adoption is the complexity and high entry barriers of operations. AI Agents’ natural language interaction models allow users to manage wallets, identify optimal DeFi investment options, conduct cross-chain transactions, or automatically execute plans based on market conditions using simple commands, drastically reducing the learning curve for new users and improving efficiency and convenience.
The potential of AI Agents in the blockchain ecosystem extends beyond optimizing user operations to broader applications. These include the creator economy, market sentiment monitoring, smart contract auditing, DAO governance voting, and even MEME coin issuance. AI Agents’ performance in de-emotionalized and precise execution makes them more reliable than most people under predefined conditions. Meanwhile, blockchain’s immutability provides reliable data sources for AI, mitigating risks from data quality issues. Leveraging on-chain data and computing power, AI Agents could disrupt existing incentive models and drive significant changes in the blockchain ecosystem.
Applications of AI Agents in Blockchain
- AI Agent Framework The AI Agent framework is essential for developing, training, and deploying agents, offering developers robust technical support for building intelligent agents. These frameworks simplify development by providing standardized environments and common components, allowing developers to focus on innovative features. Currently, AI Agent frameworks are integrating DeFi protocols, NFT projects, and more, exploring cross-platform collaboration and interoperability. For example, combining DeFi to optimize investment strategies or developing intelligent tools for NFTs, these frameworks are building a more interconnected ecosystem, attracting market attention. Representative projects: Ai16z, ARC, Swarms, Zerebro.
- AI Agent Launchpad The AI Agent Launchpad is a platform for issuing agents and related tokens, similar to meme coin issuance platforms like Pump.fun. Users can create and deploy AI Agents on these platforms and integrate them with social media platforms such as Twitter, Telegram, and Discord for automated interactions. This model lowers the barriers to issuance and promotion, offering users a more accessible creation experience and expanding AI Agent application scenarios. Representative projects: Virtuals, Clanker.
- AI Agent Application Scenarios Direct applications of AI Agents include investment, entertainment, and data analysis, with immense growth potential:
- Fund Management: AI Agents have evolved from auxiliary tools to core value creators in fund management, capable of formulating investment strategies, adjusting asset allocations, and predicting market trends in real-time. They enhance efficiency in tasks like arbitrage and risk hedging, meeting the demands for scaling and specialization in the crypto market. Representative projects: AIXBT, Ai16z.
- DeFAI: AI and DeFi Combination: DeFAI simplifies operations and lowers entry barriers by introducing AI into DeFi. Users can issue simple commands like “one-click cross-chain transaction” or “set up a regular investment plan,” achieving efficient asset management and trading. Main applications include cross-chain operation optimization, autonomous trading agents, and intelligent information analysis. Platforms like Griffain, Orbit, and Neur have realized these applications. Representative projects: GRIFFAIN, BUZZ, NEUR.
- DAO Automated Management: AI Agents optimize DAO voting decisions and automate governance. For instance, Ai16Z DAO utilizes agents for fundraising and investment management, showcasing AI’s potential in decentralized governance. Such applications enhance governance efficiency and significantly reduce member effort.
- Gaming: AI Agents assist in game design by simulating player behavior, helping developers optimize game design, enhancing fun and playability. They can also serve as game assistance tools, helping players improve their skills by analyzing operational habits and providing targeted suggestions. Representative projects: HYPER.
- Automated Quantitative Trading: In quantitative trading, AI Agents devise diverse strategies based on market conditions, executing arbitrage in volatile markets or trend-following in trending markets. Supported by exchanges for automated trading tools, AI Agents hold vast potential in future trading.
- AI MEME Projects AI MEME projects are meme coin initiatives derived from the AI Agent concept, typically lacking deep technical or product support. These projects capitalize on meme culture to attract attention with high volatility and speculation. Despite limited technical content, their market popularity and community sentiment drive explosive short-term growth, becoming a unique crypto market phenomenon. Representative projects: GOAT, ACT.
Future Development Trends
By 2025, AI Agents in crypto and Web3 are expected to reach a significant turning point. The technology is evolving from a tool for single applications to a multi-agent collaboration ecosystem, continuously expanding its boundaries. In DeFi, 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. In quantitative trading, they can execute efficient arbitrage and risk management strategies through real-time data analysis. With improved frameworks and standards, AI Agent collaboration will create 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. The development of AI Agents in Web3 faces both challenges and opportunities.
Privacy and security are critical issues, especially as AI increasingly relies on personal data. Web3 offers unique advantages in ensuring data privacy and security through blockchain, enabling AI Agents to gain broader applications in privacy-sensitive industries like healthcare and finance. Additionally, computing power and data costs pose bottlenecks for multi-agent collaboration. However, through blockchain and token economies, idle computing power and data resources can be effectively integrated, lowering development and operational barriers. Looking ahead, AI Agents could serve as a new infrastructure for Web3, deeply integrating with other core elements, creating new application models, and becoming an indispensable ecological pillar, injecting more innovation and value into the crypto industry.