Author: 0XNATALIE

Since the second half of this year, the topic of AI Agents has been gaining traction. Initially, 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 content, someone created the GOAT token, which surged over 10,000% within just 24 hours. The topic of AI Agents quickly caught the attention of the Web3 community. Subsequently, the first decentralized AI trading fund based on Solana, ai16z, was launched, introducing the AI Agent development framework Eliza, which sparked a token battle. However, the community still lacks clarity on the concept of AI Agents: what is the core of AI Agents? How do they differ from Telegram trading bots?

How It Works: Perception, Reasoning, and Autonomous Decision-Making

An AI Agent is an intelligent agent system based on large language models (LLMs) that can perceive the environment, make reasoning decisions, and complete complex tasks by calling tools or executing operations. The workflow is as follows: perception module (input acquisition) → LLM (understanding, reasoning, and planning) → tool invocation (task execution) → feedback and optimization (validation and adjustment).

Specifically, the AI Agent first acquires data from the external environment (such as text, audio, images, etc.) through the perception module and transforms 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 invoking external tools, plugins, or APIs, and validates and adjusts the results based on feedback, forming a closed-loop optimization.

In the context of Web3 applications, how does an AI Agent differ from Telegram trading bots or automation scripts? Taking arbitrage as an example, a user wants to conduct arbitrage trading under the condition that profits exceed 1%. In a Telegram trading bot that supports arbitrage, the user sets a trading strategy for profits greater than 1%, and the bot begins execution. However, when market fluctuations are frequent and arbitrage opportunities constantly change, these bots lack risk assessment capabilities and will execute arbitrage as long as the profit condition is met. In contrast, an AI Agent can automatically adjust its strategy. For instance, if a trade's profit exceeds 1%, but data analysis assesses its risk as too high, with the market potentially changing suddenly leading to losses, it will decide not to execute that arbitrage.

Therefore, AI Agents possess self-adaptability, with their core advantage being the ability to self-learn and make autonomous decisions. By interacting with the environment (such as market conditions, user behavior, etc.) and adjusting behavioral strategies based on feedback signals, they continuously improve task execution effectiveness. They 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 under an intent framework? The AI Agent itself is also a product based on intent, with the biggest difference from solvers under the intent framework being that solvers rely on precise algorithms, possessing mathematical rigor, while AI Agent decisions depend on data training, often requiring trial and error during the training process to approach optimal solutions.

Mainstream AI Agent Frameworks

The AI Agent framework is the infrastructure used to create and manage intelligent agents. Currently, in Web3, popular frameworks include ai16z's Eliza, zerebro's ZerePy, and Virtuals' GAME.

Eliza is a multifunctional AI Agent framework built using TypeScript, supporting operation across multiple platforms (such as Discord, Twitter, Telegram, etc.) and capable of complex memory management, allowing it to remember previous conversations and contexts, maintaining stable and consistent personality traits and knowledge responses. Eliza employs a RAG (Retrieval Augmented Generation) system, enabling access to external databases or resources to generate more accurate responses. Additionally, Eliza integrates TEE plugins, allowing deployment within TEE to ensure 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 needs, expand its functionality, and provide tailored operations (such as social media posting, replying, etc.). Different functionalities within the framework, such as the agent's environmental location and tasks, are divided into multiple modules for easy configuration and management by developers. The GAME framework divides the decision-making process of AI Agents into two levels: High-Level Planning (HLP) and Low-Level Planning (LLP), each responsible for different levels of tasks and decisions. High-Level Planning sets the overall goals and task planning for the agent, making decisions based on objectives, personality, background information, and environmental status, determining task priorities. Low-Level Planning focuses on execution, translating high-level planning decisions into specific operational steps and selecting appropriate functions and methods.

ZerePy is an open-source Python framework for deploying AI Agents on X. This framework integrates LLMs provided by OpenAI and Anthropic, enabling developers to build and manage social media agents that automate tasks such as posting tweets, replying to tweets, and liking posts. Each task can be assigned different weights based on its importance. ZerePy provides a simple command-line interface (CLI) for developers to quickly start and manage agents. Additionally, the framework offers Replit (an online code editing and execution platform) templates, allowing developers to quickly get started with ZerePy without complex local environment configurations.

Why Do AI Agents Face FUD?

AI Agents seem intelligent, capable of lowering the entry barrier and enhancing user experience, so why is there FUD in the community? The reason is that AI Agents are essentially still just tools; they cannot complete the entire workflow at present and can only enhance efficiency and save time at certain nodes. Moreover, at the current stage of development, the role of AI Agents is mostly concentrated on helping users issue MeMe and manage social media accounts. The community jokingly states, "assets belong to Dev, liabilities belong to AI."

However, just this week, aiPool released an AI Agent for token presale, utilizing TEE technology to achieve trustlessness. The wallet private key of this AI Agent is dynamically generated in a TEE environment, ensuring security. Users can send funds (such as SOL) to the wallet controlled by the AI Agent, which then creates tokens based on set rules and launches a liquidity pool on a DEX, while distributing tokens to eligible investors. The entire process does not rely on any third-party intermediaries and is fully completed autonomously by the AI Agent in a TEE environment, avoiding the common rug pull risks in DeFi. It is evident that AI Agents are gradually evolving. I believe that AI Agents can help users lower barriers and enhance experiences, even if it is just simplifying part of the asset issuance process, it is meaningful. However, from a macro Web3 perspective, AI Agents, as off-chain products, currently serve only as auxiliary tools for smart contracts, so there is no need to overstate their capabilities. Given that there has been a lack of significant wealth effect narratives aside from MeMe in the second half of this year, it is normal for the hype around AI Agents to revolve around MeMe. Relying solely on MeMe cannot sustain long-term value, so if AI Agents can bring more innovative gameplay to the trading process and provide tangible landing value, they may develop into a common infrastructure tool.

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