AI Agent: An on-chain interaction assistant tool—does the product that gained popularity amid meme trends actually have value?
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AI Agent: An on-chain interaction assistant tool—does the product that gained popularity amid meme trends actually have value?
As a supplementary tool for smart contracts, AI Agents might evolve into a common infrastructure tool if they can deliver tangible real-world value.
Navigating Web3 tides with focused insightsAuthor: 0XNATALIE
Since the second half of this year, discussions around AI Agents have continued to gain momentum. Initially, an AI chatbot called terminal of truths attracted widespread attention due to its humorous posts and replies on X (similar to "Luobote" on Weibo), even receiving 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. This sparked interest in AI Agents across the Web3 community. Subsequently, ai16z—the first decentralized AI investment fund built on Solana—launched the Eliza framework for AI Agent development, triggering debates around uppercase versus lowercase tokens. However, the community still lacks clarity on what exactly defines an AI Agent: What is its core function? How does it differ from Telegram trading bots?
Working Principle: Perception, Reasoning, and Autonomous Decision-Making
An AI Agent is an intelligent agent system based on large language models (LLMs) capable of perceiving its environment, performing reasoning and decision-making, and completing complex tasks through tool invocation or action execution. The workflow follows: Perception module (input acquisition) → LLM (understanding, reasoning, planning) → Tool calling (task execution) → Feedback and optimization (verification and adjustment).
Specifically, the AI Agent first acquires data from the external environment via the perception module—such as text, audio, or images—and converts it into structured information for processing. The LLM serves as the core component, providing powerful natural language understanding and generation capabilities, acting as the system’s “brain.” Based on input data and existing knowledge, the LLM performs logical reasoning to generate potential solutions or formulate action plans. Then, the AI Agent completes specific tasks by invoking external tools, plugins, or APIs, and uses feedback to verify and adjust outcomes, forming a closed-loop optimization process.
In Web3 use cases, how do AI Agents differ from Telegram trading bots or automated scripts? Take arbitrage as an example: a user wants to execute arbitrage trades when profits exceed 1%. With a standard Telegram arbitrage bot, once the user sets a strategy requiring >1% profit, the bot begins executing trades. However, when market volatility increases and arbitrage opportunities shift rapidly, these bots lack risk assessment capabilities—they will execute any trade meeting the 1% threshold regardless of context. In contrast, an AI Agent can autonomously adjust strategies. For instance, if a trade offers more than 1% profit but data analysis indicates high risk—such as sudden market shifts likely leading to losses—the AI Agent may decide not to execute that trade.
Thus, AI Agents possess self-adaptability. Their key advantage lies in self-learning and autonomous decision-making: they interact with environments (e.g., markets, user behavior) and refine their behavioral strategies based on feedback signals, continuously improving task performance. They can also make real-time decisions using external data and optimize decision strategies through reinforcement learning.
Does this sound similar to a solver under an intent-based framework? Indeed, AI Agents are also products of intent-driven design. The main difference is that solvers rely on precise algorithms with mathematical rigor, whereas AI Agents base decisions on data training, often approaching optimal solutions through trial and error during training.
Mainstream AI Agent Frameworks
AI Agent frameworks serve as infrastructure for creating and managing intelligent agents. Currently in Web3, popular frameworks include Eliza by ai16z, ZerePy by zerebro, and GAME by Virtuals.
Eliza is a versatile AI Agent framework built with TypeScript, supporting operation across multiple platforms such as Discord, Twitter, and Telegram. Through sophisticated memory management, it remembers past conversations and context, maintaining consistent personality traits and knowledge responses. Eliza employs a RAG (Retrieval-Augmented Generation) system, enabling access to external databases or resources to produce more accurate answers. Additionally, Eliza integrates TEE plugins, allowing deployment within Trusted Execution Environments (TEEs) to ensure data security and privacy.
GAME is a framework designed to empower and drive autonomous decision-making and actions in AI Agents. Developers can customize agent behaviors, extend functionality, and define personalized operations (e.g., social media posting, replying). Different functions—such as agent environment positioning and task assignment—are modularized for easy configuration and management. The GAME framework divides the AI Agent's decision-making process into two layers: High-Level Planning (HLP) and Low-Level Planning (LLP), responsible for different levels of tasks and decisions. HLP handles overall goal setting and task planning, making strategic decisions based on objectives, personality, background information, and environmental states, while determining task priorities. LLP focuses on execution, translating high-level decisions into concrete operational steps and selecting appropriate functions and methods.
ZerePy is an open-source Python framework for deploying AI Agents on X. It integrates LLMs provided by OpenAI and Anthropic, enabling developers to build and manage social media agents that automate actions like posting tweets, replying, and liking content. Each task can be assigned a weight based on importance. ZerePy provides a simple command-line interface (CLI) to help developers quickly launch and manage agents. It also offers Replit templates—an online code editing and execution platform—allowing developers to get started with ZerePy instantly without complex local setup.
Why Does the AI Agent Face FUD?
Despite appearing intelligent and promising lower barriers to entry and improved user experience, why does the AI Agent face skepticism and fear, uncertainty, and doubt (FUD)? The reason is that AI Agents remain tools at their core. They currently cannot handle entire workflows independently but only improve efficiency and save time at certain stages. At this stage of development, most AI Agent applications focus on helping users mint Memes with one click or manage social media accounts. The community jokingly says: "assets belong to devs, liabilities belong to AI."
However, just this week, aiPool launched as an AI Agent for token presales, leveraging TEE technology to achieve trustlessness. The private key of the AI Agent’s wallet is dynamically generated within a TEE environment, ensuring security. Users can send funds (e.g., SOL) to the wallet controlled by the AI Agent, which then automatically creates a token according to predefined rules, launches a liquidity pool on a DEX, and distributes tokens to eligible investors. The entire process requires no third-party intermediaries and is fully executed autonomously by the AI Agent within the TEE, mitigating common DeFi risks like rug pulls. Clearly, AI Agents are evolving. I believe they can meaningfully reduce barriers and enhance user experience—even if only by simplifying parts of asset issuance. Yet from a broader Web3 perspective, AI Agents are currently off-chain tools that assist smart contracts rather than replace them, so their capabilities should not be overhyped. Given the lack of significant wealth-generation narratives beyond Memes since the second half of the year, it's natural that AI Agent hype has centered around Memes. Relying solely on Memes cannot sustain long-term value. If AI Agents can introduce innovative mechanics within transaction processes and deliver tangible, real-world utility, they may eventually evolve into a widely adopted infrastructural tool.
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