AI Agent: The Next Stage Revolution of Artificial Intelligence

The speed of development of artificial intelligence is astonishing. The future will undoubtedly be dominated by AI, and if there is one core element added, it must be the combination of AI and encryption technology.

Currently, AI has entered a new stage: AI Agent. Whether from the perspective of imaginative space or actual application scenarios, AI Agent is full of exciting potential.

The tide of the times is surging, and we must keep pace with it. Recently, I have been studying knowledge related to AI Agents, and this article records my learning journey, hoping to help everyone better understand this emerging field.

This is the first article in the AI Agent introductory guide series, aimed at helping readers build a comprehensive understanding and framework. We will continue to explore this field in depth, continuously refining our knowledge and seizing the opportunities brought by the wave of AI.

The Essence of AI Agents

Putting aside complex concepts, we can directly compare the differences between AI Agents and existing large language models like ChatGPT(.

Currently, large language models are more like powerful "natural language search engines" that can answer questions and provide suggestions, but they cannot truly make autonomous decisions and execute actions.

The capabilities of the AI Agent surpass the existing large model framework, no longer limited to "data processing," but able to complete the full loop from "perception" to "action."

For a straightforward example: if you ask ChatGPT how to invest in cryptocurrencies, it will give you a bunch of suggestions. In contrast, an AI Agent can help you track global market information in real-time and dynamically adjust your investment portfolio to maximize returns.

Therefore, we can define an AI Agent as a software entity based on artificial intelligence technology that can autonomously or semi-autonomously perform tasks, make decisions, and interact with humans or other systems.

The core difference lies in the ability to act independently.

How does the AI Agent achieve autonomous action?

By using AI technology, complex logic can be converted into precise conditional judgments ) that return True or False based on the context (, and then seamlessly integrated into specific business scenarios.

First is intention analysis: AI will understand the user's needs by analyzing the user's prompts and context. It not only considers what the user has said, but also references the user's previous usage records and specific situations, and then translates these needs into specific program instructions.

Secondly, it assists in judgment: AI is like a smart assistant that can transform complex problems, which are difficult for humans to handle, into simple yes or no answers, or a few fixed options, through analysis. This not only makes decision-making more accurate and efficient but also works well with existing business systems.

According to the degree of autonomous action, AI Agents can be divided into two types:

One type is the AI Agent that serves as a personal assistant, capable of helping users with various tasks.

Another type goes further, where the AI Agent itself is an independent entity, possessing its own identity or brand, providing services to multiple users.

Overall, AI Agents can be seen as the next development stage and new product form of large language models, possessing immense imaginative space and development potential.

The Integration of AI Agents and Cryptography

Artificial intelligence and cryptography are not completely separate fields; the two can be deeply integrated.

More importantly, AI Agents in the Web2 environment and AI Agents in the Web3 environment have essential differences.

The Web3 AI Agent is a more advanced and complete form, which we can call the "Crypto AI Agent."

With the capabilities of cryptographic technology, the AI Agent has acquired more features:

1. Decentralization

After integrating with encryption technology, the operations, data storage, and decision-making processes of AI Agents have become more transparent and are not controlled by a single entity.

In contrast, AI Agents in a Web2 environment are typically controlled by centralized companies or platforms, with data and decision-making processes concentrated in the hands of a few entities.

Once the AI Agent provides services externally, it will face trust issues. Therefore, the AI Agent needs the operating or verification environment provided by the blockchain.

AI agents also need a barrier-free usage method, data transparency, interoperability, and decentralized features.

2. Incentive Mechanism

This is one of the most powerful enablers of cryptographic technology. The token economic model provides direct incentives for developers and users to participate and contribute.

AI Agents in the Web2 environment mainly rely on traditional business models, such as advertising revenue or subscription services, to maintain operations.

Web2 startups or companies may struggle to achieve profitability for a long time and find it difficult to secure funding. However, in a Web3 environment, cash flow can be directly obtained through the issuance of tokens, providing support for project development. For example, the use of AI Agents may require payment in cryptocurrency.

A free market economy can foster more innovation.

3. True perpetual operation

With smart contracts, AI Agents have truly achieved "immortality."

As long as the smart contract is deployed on the blockchain, the AI Agent can operate automatically according to its rules and can theoretically run indefinitely.

Smart contracts can ensure that the code and decision-making mechanisms of AI Agents exist permanently on the blockchain, unless there is a clear logic to stop or change their behavior.

However, it is important to note that the data on which the AI Agent relies may need to be continuously updated or maintained. Without a continuous input of data or interaction from the outside world, the "eternity" of the AI Agent may be limited to its program logic, lacking dynamism.

Overall, compared to the need for AI Agents in cryptographic technology, AI Agents rely more on the support of cryptographic technology.

The Narrative Evolution of AI + Cryptography Technology

The development from large language models to AI agents represents two different stages, and the combination of AI and cryptographic technology can also be divided into two stages:

Large Language Model Phase: Infrastructure

AI projects are mainly evaluated on three dimensions: computing power, algorithms, and data.

Web3 at this stage mainly adds an incentive system for AI, tokenizing computing power, algorithms, and data.

Therefore, the intersection of AI and Web3 can also be explored from three dimensions: computing power, algorithms, and data.

1. Hash Rate:

   • Distributed Computing Network: Blockchain inherently possesses distributed characteristics. AI can leverage the distributed network of Web3 to access more computing resources.
   • Incentive Mechanism: Web3 introduces economic incentive mechanisms, such as token economics, that can encourage participants in the network to contribute their computing resources.

2. Algorithm:

   • Smart Contract: Smart contracts in Web3 can automatically execute AI algorithms.
   • Decentralized algorithm execution: In a Web3 environment, AI algorithms can operate without relying on a single central server, but instead through multiple nodes that collaboratively verify and execute.

3. Data:

   • Data Privacy and Ownership: Web3 emphasizes the decentralization of data and user ownership of data.
   • Data Validation and Quality: Blockchain technology can be used for data verification, ensuring the authenticity and integrity of the data.
   • Data Market: Web3 can promote the development of data markets, allowing users to directly sell or share data with AI systems in need.

In response to these three dimensions, several well-known projects have emerged in the market:

Hashrate projects:

• Render Network
• Akash Network
• Aethir
• ionet

Algorithm projects:

• Cortex
• Fetchai
• iExec RLC

Data projects:

• Vana
• RSS3

Comprehensive Project:

• Myshell

Overall, during the phase of large language models, the integration of encryption technology and AI primarily focuses on the infrastructure level, laying the foundation for the long-term development of AI.

AI Agent Stage: Application Landing

The emergence of AI Agents marks the stage of AI entering the application layer.

The development of AI Agents can be divided into three stages: the Meme token stage, the standalone AI application stage, and the AI Agent framework standard stage.

1. AI Agent Meme Token

AI Agent Meme token is a special phenomenon that reflects the community's emotional response to the rapid development of AI.

The rapid development of AI technology has made ordinary people feel anxious, while AI Meme tokens have given them an opportunity to participate.

These tokens bring emotional value to holders by allowing ordinary people to feel the impact of the AI wave and participate in the AI revolution.

The result is: AI+MEME has accelerated the market education and dissemination of AI through the wealth effect.

From another perspective, there are two reasons for the issuance of tokens by AI Agent:

• Attract funds and users through the wealth effect, injecting momentum for the subsequent development of the industry.
• The meme-based issuance method itself is a form of community financing, providing cash flow for the project's own development.

Top projects include:

• $GOAT
• $Fartcoin
• $ACT
• $WORM

2. Monolithic AI Applications

AI Agent is integrating with various subfields of cryptocurrency technology, presenting a flourishing situation.

With the development of AI Agents, the tokens they issue are no longer just simple Meme coins, but are supported by actual use cases, gradually possessing the attributes of value coins.

Main categories include:

• Genesis Project: like ai16z
• Agent Gaming: such as ARC, FARM, GAME
• Agent DeFi: such as $NEUR, $BUZZ
• Code Audit: like AgentAUDIT
• Agent data analysis: such as REI
• Autonomous AI Agent: such as LMT, GRIFFAIN

3. AI Agent Framework Standard

The AI Agent framework standards are still in a highly competitive stage.

The AI Agent framework standard simplifies the development and deployment process of AI Agents by providing a unified set of specifications and tools.

It allows developers to create AI Agents that can interact with multiple clients, extend functionality through plugins, and leverage AI technology to enhance their intelligence.

These standards and foundational libraries ensure that the operation of AI Agents is efficient, secure, and user-friendly.

The main AI Agent framework standards include:

• Eliza framework of ai16z
• Virtual GAME framework
• swarms multi-agent AI framework
• ZerePy framework of ZEREBRO

Relevant ecosystems have formed around these frameworks, and it is important to focus on the development of these ecosystems when researching related projects.

Conclusion

The narrative of AI Agent has begun to explode.

Every year in our industry, there is an outbreak of a main narrative, and around this main narrative, many star projects will emerge, naturally bringing numerous opportunities.

For example, the DeFi Summer of 2020, the Inscription Summer of 2023, the Meme Summer of 2024, and the AI Summer emerging in 2025.

Don't miss every rare opportunity to create wealth.

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