What Are AI Agents? How Do They Differ from Telegram Trading Bots?

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.

AI Agents, a hot topic in the Web3 community, have sparked discussions and debates. But what exactly are AI Agents? How do they differ from Telegram trading bots? And why do they face skepticism despite their potential benefits?

AI Agents are intelligent agent systems powered by large language models (LLMs) that can perceive their surroundings, make logical decisions, and complete complex tasks by utilizing tools or executing actions. Their workflow involves:

* Perception module (acquiring input)

* LLM (understanding, reasoning, and planning)

* Tool invocation (task execution)

* Feedback and optimization (validating and adjusting)

For example, in the context of Web3 applications, AI Agents differ from Telegram trading bots or automation scripts in the following way:

Suppose users want to execute arbitrage trades when profits exceed 1%. In a Telegram trading bot that supports arbitrage, users can set a trading strategy for profits greater than 1%, and the bot will begin executing trades that meet this condition. However, these bots lack the ability to assess risk and will continue executing arbitrage trades as long as the profit condition is met. In contrast, AI Agents can automatically adjust their strategies. For instance, if a trade's profit exceeds 1%, but data analysis reveals that the risk is too high due to potential sudden market changes that could lead to losses, the AI Agent will decide not to execute the arbitrage trade.

Thus, AI Agents possess self-adaptability, with their core advantage being the ability to self-learn and make autonomous decisions. Through interaction with the environment (such as market conditions, user behavior, etc.), they adjust their behavioral strategies based on feedback signals, continuously improving the effectiveness of task execution. They can also make real-time decisions based on external data and continuously optimize decision-making strategies through reinforcement learning.

While AI Agents sound advanced and capable of enhancing user experiences, they also face skepticism in the community. This is mainly because AI Agents are still just tools and cannot complete entire workflows independently. They 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 MeMes and manage social media accounts. As a result, the community戲稱" , poking fun at the fact that assets ultimately belong to the developer, while liabilities are assigned to the AI.

However, just this week, a new application of AI Agents emerged with the launch of an AI Agent for token presale by aiPool. This AI Agent leverages 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 according to 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, and even simplifying part of the asset issuance process is meaningful. However, from a macro Web3 perspective, AI Agents, as off-chain products, currently serve merely 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 trading processes and provide tangible value, they may develop into a common infra tool.