Author: 0XNATALIE
Since the second half of this year, the topic of AI Agent has been continuously rising in popularity. Initially, the AI chatbot terminal of truths gained widespread attention for its humorous posts and replies (similar to "Robert" on Weibo) on X, and received a $50,000 grant from Andreessen Horowitz (a16z) founder Marc Andreessen. Inspired by the content it released, someone created the GOAT token, which surged over 10,000% in just 24 hours. The topic of AI Agent then caught the attention of the Web3 community. Subsequently, the first decentralized AI trading fund ai16z based on Solana was launched, introducing the AI Agent development framework Eliza, and triggering a debate over capitalization of tokens. However, the community still lacks a clear understanding of the concept of AI Agent: what is the core of AI Agent? How is it different from Telegram trading bots?
Working Principle: Perception, Reasoning, and Autonomous Decision-making
AI Agent is an intelligent agent system based on large language models (LLMs), capable of perceiving the environment, making reasoning and decisions, and completing complex tasks by calling tools or executing operations. The workflow is: Perception module (obtaining input) → LLM (understanding, reasoning, and planning) → Tool invocation (task execution) → Feedback and optimization (verification and adjustment).
Specifically, the AI Agent first acquires data (such as text, audio, images, etc.) from the external environment through the perception module, and converts it into structured information that can be processed. The LLM, as the core component, provides powerful natural language understanding and generation capabilities, acting as the "brain" of the system. Based on the input data and existing knowledge, the LLM performs logical reasoning, generating possible solutions or formulating action plans. Subsequently, the AI Agent completes specific tasks by calling external tools, plugins, or APIs, and verifies and adjusts the results based on feedback, forming a closed-loop optimization.
In the Web3 application scenarios, how does the AI Agent differ from Telegram trading bots or automated scripts? Taking arbitrage as an example, the user wants to perform arbitrage transactions with a profit margin greater than 1%. In Telegram trading bots that support arbitrage, the user sets the trading strategy with a profit margin greater than 1%, and the bot will start executing. However, when the market fluctuates frequently and arbitrage opportunities keep changing, these bots lack the ability to assess risks, and will execute arbitrage as long as the profit margin exceeds 1%. In contrast, the AI Agent can automatically adjust its strategy. For example, when the profit of a trade exceeds 1%, but through data analysis it assesses the risk as too high and the market may suddenly change, causing a loss, it will decide not to execute the arbitrage.
Therefore, the core advantage of the AI Agent lies in its self-adaptive ability, the capacity for self-learning and autonomous decision-making. Through interaction with the environment (such as the market, user behavior, etc.), it can adjust its behavioral strategies based on feedback signals and continuously improve the execution of tasks. It can also make real-time decisions based on external data and optimize its decision-making strategies through reinforcement learning.
Does this sound a bit like a solver under the intent framework? The AI Agent is also a product based on the intent framework, but the main difference from the solver under the intent framework is that the solver relies on precise algorithms and has mathematical rigor, while the AI Agent's decision-making depends on data training, often requiring constant trial and error in the training process to approach the optimal solution.
Mainstream AI Agent Frameworks
AI Agent frameworks are the infrastructure for creating and managing intelligent agents. Currently, the popular frameworks in Web3 include Eliza from ai16z, ZerePy from zerebro, and GAME from Virtuals.
Eliza is a multi-functional AI Agent framework built with TypeScript, supporting deployment on multiple platforms (such as Discord, Twitter, Telegram, etc.) and maintaining stable and consistent personality traits and knowledge responses through complex memory management. Eliza adopts a Retrieval Augmented Generation (RAG) system, which can access external databases or resources to generate more accurate responses. In addition, Eliza integrates the TEE plugin, allowing deployment in the TEE to ensure data security and privacy.
GAME is a framework that empowers and drives AI Agents to make autonomous decisions and take actions. Developers can customize the agent's behavior and expand its functionality according to their needs, and provide customized operations (such as social media posting, replying, etc.). The different functions of the framework, such as the agent's environmental location and tasks, are divided into multiple modules, making it convenient for developers to configure and manage. The GAME framework divides the AI Agent's decision-making process into two levels: High-Level Planning (HLP) and Low-Level Planning (LLP), responsible for different levels of tasks and decisions. High-level planning is responsible for setting the agent's overall goals and task planning, formulating decisions based on goals, personality, background information, and environmental status, and determining the priority of tasks. Low-level planning focuses on the execution level, translating the decisions of high-level planning into specific operational steps, and selecting appropriate functions and methods of operation.
ZerePy is an open-source Python framework for deploying AI Agents on X. The framework integrates the LLMs provided by OpenAI and Anthropic, allowing developers to build and manage social media agents and automate operations such as posting tweets, replying to tweets, and liking. Each task can be assigned different weights based on its importance. ZerePy provides a concise command-line interface (CLI) for developers to quickly start and manage agents. The framework also provides a Replit (an online code editing and execution platform) template, allowing developers to quickly get started with ZerePy without complex local environment configuration.
Why Does AI Agent Face FUD?
AI Agent seems intelligent and can reduce the entry barrier and improve the user experience, so why does the community still have FUD? The reason is that the AI Agent is essentially still a tool, and it cannot currently complete the entire workflow. It can only improve efficiency and save time at certain nodes. Moreover, in the current development stage, the role of the AI Agent is mainly focused on helping users issue MeMe and operate social media accounts. The community jokingly says "assets belong to Dev, liabilities belong to AI".
However, this week, aiPool, as the token presale of the AI Agent, was released, utilizing TEE technology to achieve decentralization. The wallet private key of this AI Agent is dynamically generated in the TEE environment, ensuring security. Users can send funds (such as SOL) to the wallet controlled by the AI Agent, and the AI Agent will then create tokens according to the set rules and launch a liquidity pool on the DEX, while distributing tokens to qualified investors. The entire process does not rely on any third-party intermediaries and is completed autonomously by the AI Agent in the TEE environment, avoiding the common rug pull risk in DeFi. It can be seen that the AI Agent is gradually developing. I believe that the AI Agent can help users reduce the threshold and improve the experience, even if it only simplifies part of the asset issuance process, it is meaningful. But from the macro perspective of Web3, the AI Agent, as an off-chain product, is currently only playing an auxiliary role to smart contracts, so there is no need to overhype its capabilities. Due to the lack of significant wealth effect narratives other than MeMe in the second half of this year, the hype around AI Agent is centered on MeMe, which is normal. Relying solely on MeMe cannot sustain long-term value, so if the AI Agent can bring more innovative gameplay to the transaction process and provide practical landing value, it may develop into a common infra tool.
