AI Agent：Web3智能辅助新时代

今年下半年以来，AI Agent的话题持续升温。起初，AI聊天机器人终端的真相吸引了广泛关注

With the continuous heating up of the AI Agent topic in the second half of this year, at first, the truths AI chatbot terminal attracted widespread attention for its humorous posts and replies on X (similar to "Robert" on Weibo), and received a $50,000 grant from a16z founder Marc Andreessen. Inspired by its published content, someone created the GOAT token, which rose by more than 10,000% in just 24 hours. The topic of AI Agent immediately attracted the attention of the Web3 community. Later, the first decentralized AI trading fund based on Solana, ai16z, came out, launched the AI Agent development framework Eliza, and triggered a dispute over uppercase and lowercase tokens. However, the community still has an unclear concept of AI Agent: What is the core of AI Agent? How is it different from the Telegram trading robot?

随着今年下半年AI代理话题的持续升温，起初，真相AI聊天机器人终端因其在X上幽默的发帖和回复（类似微博上的“罗伯特”）而引起广泛关注，并获得了广泛关注。 a16z 创始人 Marc Andreessen 提供 50,000 美元资助。受到其发布内容的启发，有人创建了 GOAT 代币，该代币在短短 24 小时内上涨了 10,000% 以上。 AI Agent的话题立刻引起了Web3社区的关注。后来，第一个基于Solana的去中心化AI交易基金ai16z问世，推出了AI Agent开发框架Eliza，并引发了大小写代币之争。然而，业界对AI Agent的概念仍然不明确：AI Agent的核心是什么？它与 Telegram 交易机器人有何不同？

How it works: Perception, reasoning, and autonomous decision-making

运作原理：感知、推理和自主决策

AI Agent is an intelligent agent system based on a large language model (LLM) that can perceive the environment, make reasoning decisions, and complete complex tasks by calling tools or performing operations. Workflow: Perception module (obtaining input) → LLM (understanding, reasoning and planning) → tool calling (task execution) → feedback and optimization (verification and adjustment).

AI Agent是基于大语言模型（LLM）的智能代理系统，可以通过调用工具或执行操作来感知环境、做出推理决策并完成复杂的任务。工作流程：感知模块（获取输入）→LLM（理解、推理和规划）→工具调用（任务执行）→反馈和优化（验证和调整）。

Specifically, AI Agent first obtains 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. As a core component, LLM provides powerful natural language understanding and generation capabilities, acting as the "brain" of the system. Based on the input data and existing knowledge, LLM performs logical reasoning to generate possible solutions or formulate action plans. Subsequently, AI Agent completes specific tasks by calling external tools, plug-ins or APIs, and verifies and adjusts the results based on feedback to form a closed-loop optimization.

具体来说，AI Agent首先通过感知模块从外部环境获取数据（如文本、音频、图像等），并将其转换为可处理的结构化信息。 LLM作为核心组件，提供强大的自然语言理解和生成能力，充当系统的“大脑”。基于输入数据和现有知识，LLM进行逻辑推理以生成可能的解决方案或制定行动计划。随后，AI Agent通过调用外部工具、插件或API完成特定任务，并根据反馈对结果进行验证和调整，形成闭环优化。

In the application scenarios of Web3, what is the difference between AI Agent and Telegram trading robots or automated scripts? Take arbitrage as an example. Users want to conduct arbitrage transactions under the condition that the profit is greater than 1%. In Telegram trading robots that support arbitrage, users set up trading strategies with profits greater than 1%, and the Bot begins to execute. However, when the market fluctuates frequently and arbitrage opportunities are constantly changing, these Bots lack risk assessment capabilities and execute arbitrage as long as the profit is greater than 1%. In contrast, AI Agent can automatically adjust its strategy. For example, when the profit of a transaction exceeds 1%, but through data analysis, the risk is too high, and the market may suddenly change and cause losses, it will decide not to execute the arbitrage.

在Web3的应用场景中，AI Agent与Telegram交易机器人或自动化脚本有什么区别？以套利为例。用户希望在利润大于1%的条件下进行套利交易。在支持套利的Telegram交易机器人中，用户设置利润大于1%的交易策略，Bot开始执行。但当市场波动频繁、套利机会不断变化时，这些Bot缺乏风险评估能力，只要利润大于1%就会执行套利。相比之下，AI Agent可以自动调整策略。例如，当一笔交易的利润超过1%，但通过数据分析，风险太高，市场可能突然发生变化而造成损失，就会决定不执行套利。

Therefore, AI Agent is self-adaptive. Its core advantage lies in its ability to self-learn and make decisions autonomously. It can adjust its behavior strategy based on feedback signals through interaction with the environment (such as the market, user behavior, etc.) to continuously improve the performance of task execution. It can also make decisions in real time based on external data and continuously optimize its decision-making strategy through reinforcement learning.

因此，AI Agent具有自适应性。其核心优势在于具有自学习、自主决策的能力。它可以通过与环境（如市场、用户行为等）的交互，根据反馈信号调整自身的行为策略，不断提高任务执行的性能。它还可以根据外部数据实时做出决策，并通过强化学习不断优化其决策策略。

Does this sound a bit like a solver in the intent framework? AI Agent itself is also a product based on intent. The biggest difference between it and the solver in the intent framework is that the solver relies on precise algorithms and is mathematically rigorous, while AI Agent decision-making relies on data training, and often requires continuous trial and error during the training process to approach the optimal solution.

这听起来是不是有点像意图框架中的求解器？ AI Agent本身也是一个基于意图的产品。它与意图框架中的求解器最大的区别在于，求解器依赖于精确的算法，在数学上是严谨的，而AI Agent的决策依赖于数据训练，往往需要在训练过程中不断试错才能逼近目标。最优解。

AI Agent Mainstream Framework

AI Agent主流框架

AI Agent framework is an infrastructure for creating and managing intelligent agents. Currently in Web3, popular frameworks include Eliza from ai16z, ZerePy from zerebro, and GAME from Virtuals.

AI Agent框架是用于创建和管理智能代理的基础设施。目前在Web3中，流行的框架包括ai16z的Eliza、zerebro的ZerePy和Virtuals的GAME。

Eliza is a versatile AI Agent framework built with TypeScript. It supports running on multiple platforms (such as Discord, Twitter, Telegram, etc.), and through complex memory management, it can remember previous conversations and contexts, and maintain stable and consistent personality traits and knowledge answers. Eliza uses the RAG (Retrieval Augmented Generation) system, which can access external databases or resources to generate more accurate answers. In addition, Eliza integrates a TEE plug-in, allowing deployment in TEE to ensure data security and privacy.

Eliza 是一个使用 TypeScript 构建的多功能 AI 代理框架。它支持在多个平台上运行（如Discord、Twitter、Telegram等），并通过复杂的内存管理，可以记住之前的对话和上下文，并保持稳定一致的人格特征和知识答案。 Eliza 使用 RAG（检索增强生成）系统，该系统可以访问外部数据库或资源以生成更准确的答案。此外，Eliza集成了TEE插件，允许部署在TEE中以确保数据安全和隐私。

GAME is a framework that enables and drives AI Agents to make autonomous decisions and actions. Developers can customize the behavior of agents according to their needs, expand their functions, and provide customized operations (such as social media posting, replying, etc.). Different functions in the framework, such as the agent's environmental location and tasks, are divided into multiple modules to facilitate developers to configure and manage. The GAME framework divides the decision-making process of AI Agents into two levels: high-level planning (HLP) and low-level planning (LLP), which are responsible for tasks and decisions at different levels. High-level planning is responsible for setting the overall goals and task planning of the agent, making decisions based on goals, personality, background information and

GAME 是一个框架，可支持并驱动 AI 代理做出自主决策和行动。开发者可以根据需求定制代理的行为，扩展其功能，提供定制化操作（如社交媒体发帖、回复等）。框架中的不同功能，如代理的环境位置、任务等，被划分为多个模块，方便开发者配置和管理。 GAME框架将AI Agent的决策过程分为两个层次：高层规划（HLP）和低层规划（LLP），分别负责不同层级的任务和决策。高层规划负责设定Agent的总体目标和任务规划，根据目标、个性、背景信息和