Author: 0XNATALIE
Since the second half of this year, the topic of AI Agent has continued to rise. At first, the AI chatbot terminal of truths 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 posts, 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 a vague concept of AI Agent: What is the core of AI Agent? How is it different from the Telegram trading robot?
How it works: Perception, reasoning, and autonomous decision-making
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).
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.
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 the Telegram trading robot that supports arbitrage, the user sets a trading strategy with a profit greater than 1%, and the Bot starts to execute. However, when the market fluctuates frequently and arbitrage opportunities are constantly changing, these Bots lack the ability to assess risks 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 the risk is too high through data analysis, and the market may suddenly change and cause losses, it will decide not to execute the arbitrage.
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.
Doesn’t 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.
AI Agent Mainstream Framework
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.
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.
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 environmental status, and determining the priority of tasks. Low-level planning focuses on the execution level, converting the decisions of high-level planning into specific operational steps, and selecting appropriate functions and operation methods.
ZerePy is an open source Python framework for deploying AI Agents on X. The framework integrates the LLM provided by OpenAI and Anthropic, enabling 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. At the same time, the framework also provides a Replit (an online code editing and execution platform) template, through which developers can quickly start using ZerePy without complex local environment configuration.
Why do AI Agents face FUD?
AI Agent seems to be smart, can lower the entry threshold and improve user experience, why is there FUD in the community? The reason is that AI Agent is essentially still just a tool, and it cannot complete the entire workflow at present. It can only improve efficiency and save time at certain nodes. Moreover, at the current stage of development, the role of AI Agent is mostly focused on helping users to issue MeMe and operate social media accounts with one click. The community jokingly calls it "assests belong to Dev, liabilities belong to AI".
However, just this week, aiPool was released as an AI Agent for token pre-sale, using TEE technology to achieve trustlessness. The AI Agent's wallet private key is dynamically generated in the TEE environment to ensure security. Users can send funds (such as SOL) to a wallet controlled by the AI Agent, which then creates tokens according to set rules and launches a liquidity pool on the DEX while distributing tokens to qualified investors. The entire process does not need to rely on any third-party intermediary, and is completely completed independently by the AI Agent in the TEE environment, avoiding the common rug pull risk in DeFi. It can be seen that AI Agent is gradually developing. I believe that AI Agent can help users lower the threshold and improve their experience, even if it only simplifies part of the asset issuance process, which is meaningful. However, from a macro Web3 perspective, AI Agent, as an off-chain product, currently only serves as a tool to assist smart contracts, so there is no need to over brag about its capabilities. Since there is no significant wealth effect narrative other than MeMe in the second half of this year, it is normal for the AI Agent hype to revolve around MeMe. MeMe alone cannot maintain long-term value, so if AI Agent can bring more innovative ways to the transaction process and provide tangible implementation value, it may develop into a universal infra tool.
