Author: 0XNATALIE

Since the second half of this year, the topic of AI Agent has continued to increase in popularity. 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 what he posted, someone created the GOAT token, which gained over 10,000% in just 24 hours. The topic of AI Agent immediately attracted the attention of the Web3 community. Afterwards, the first decentralized AI trading fund ai16z based on Solana came out, launched the AI ​​Agent development framework Eliza, and triggered a dispute between upper and lower case tokens. However, the community’s concept of AI Agent is still unclear: What is the core of AI Agent? How is it different from Telegram trading bots?

AI Agent is an intelligent agent system based on a large language model (LLM). It can perceive the environment, make reasoning decisions, and complete it by calling tools or performing operations. Complex tasks. Workflow: Perception module (obtaining input) → LLM (understanding, reasoning and planning) → Tool invocation (executing tasks) → Feedback and optimization (verification and adjustment).

Specifically, the 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 and generates possible solutions or develops action plans. Subsequently, the 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 Web3 application scenarios, what is the difference between AI Agent and Telegram trading robots or automation scripts? Taking arbitrage as an example, users hope to conduct arbitrage transactions under the condition that the profit is greater than 1%. In the Telegram trading bot that supports arbitrage, the user sets a trading strategy with a profit greater than 1%, and the Bot starts executing. However, when the market fluctuates frequently and arbitrage opportunities are constantly changing, these Bots lack risk assessment capabilities and will execute arbitrage as long as the profit is greater than 1%. In contrast, AI Agents can automatically adjust policies. For example, when the profit of a certain transaction exceeds 1%, but the risk is assessed to be too high through data analysis, and the market may suddenly change and lead to losses, it will decide not to execute the arbitrage.

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

Does this sound a bit like a solver (slover) under the intent framework? AI Agent itself is also a product based on intent. The biggest difference from the solver under 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 needs to be passed during the training process. Continuous trial and error to get closer to the optimal solution.

The AI ​​Agent framework is the infrastructure for creating and managing intelligent agents. Currently in Web3, the more popular frameworks include ai16z's Eliza, zerebro's ZerePy and Virtuals' GAME.

Eliza is a multifunctional AI Agent framework built using TypeScript. It supports running on multiple platforms (such as Discord, Twitter, Telegram, etc.) and can remember previous actions through complex memory management. Conversations and context, maintaining stable and consistent personality traits and knowledgeable responses. Eliza uses the RAG (Retrieval Augmented Generation) system, which can access external databases or resources to generate more accurate answers. Additionally, Eliza integrates a TEE plug-in that allows deployment in TEE, ensuring data security and privacy.

GAME is a framework that empowers and drives AI Agents to make autonomous decisions and actions. Developers can customize the agent's behavior according to their own needs, extend its functionality, and provide customized operations (such as social media posting, replies, etc.). Different functions in the framework, such as the agent's environment 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 Agent into two levels: high-level planning (HLP) and low-level planning (LLP), which are respectively responsible for different levels of tasks and decisions. 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 task priorities. Low-level planning focuses on the execution level, converting high-level planning decisions into specific operational steps and selecting appropriate functions and operating methods.

ZerePy is an open source Python framework for deploying AI Agents on X. The framework integrates LLM provided by OpenAI and Anthropic, which enables developers to build and manage social media agents and automate operations such as tweeting, replying to tweets, and liking. Each task can be assigned a different weight based on its importance. ZerePy provides a simple command line interface (CLI) to facilitate developers to quickly start and manage agents. At the same time, the framework also provides Replit (an online code editing and execution platform) template through which developers can quickly start using ZerePy without complex local environment configuration.

AI Agent seems intelligent and can lower the entry barrier and improve user experience. Why is there still FUD in the community? The reason is that AI Agent is still essentially just a tool. It cannot yet complete the entire workflow and can only improve efficiency and save time on certain nodes. And at the current development stage, the role of AI Agent is mostly focused on helping users issue MeMe and operate social media accounts with one click. The community jokingly calls it "assests belong Dev, abilities belong 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.