AI Agent: Opening a New Era of Artificial Intelligence

The development speed of artificial intelligence is constantly accelerating, and it will undoubtedly be dominated by AI in the future. If there is one more core element to add, it is undoubtedly the combination of AI and encryption technology.

Currently, AI has entered a new stage: AI Agent. This new type of AI has great potential both in terms of imaginative space and practical application scenarios.

The tide of the times is sweeping in, and we need to seize opportunities in a timely manner. Recently, I have also been continuously learning about AI Agent-related knowledge. This article will share my learning insights, hoping to help everyone better understand this field.

This article is the first in a series of introductory guides on AI Agents, aimed at helping readers establish a comprehensive understanding and framework. We will continue to delve deeper into this field, continuously improving and collectively seizing the opportunities of AI development.

The Essence of AI Agent

Putting aside complex concepts, we can directly compare the AI Agent with existing large language models like ChatGPT(.

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

The capabilities of the AI Agent go beyond the scope of existing large models, no longer limited to "data processing", but able to complete a full loop from "perception" to "action".

A straightforward example: If you ask ChatGPT how to invest in cryptocurrencies, it will provide you with a series of suggestions. In contrast, an AI Agent can track global market information in real-time and dynamically adjust the investment portfolio to maximize returns.

Thus, 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 most core feature is: autonomous action.

How does the AI Agent achieve autonomous actions?

It uses AI to convert complex logic into precise conditional judgments ) returning True or False based on context (, and then seamlessly integrates into specific business scenarios.

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

Secondly, there is assistance in judgment: AI acts like a smart assistant that can simplify complex issues that are difficult for humans to handle into simple yes/no choices or a few fixed options through analysis. This not only improves the accuracy and efficiency of decision-making but also works well with existing business systems.

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

One type is as a personal assistant, helping users handle various tasks.

Another type goes further; 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 a new product form of large models, with immense development potential.

The Integration of AI Agents and Cryptography

AI and cryptographic technology are not entirely independent; 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 AI Agent of Web3 is a more advanced and complete form, which we can refer to as "Crypto AI Agent."

With the power of encryption technology, AI Agent has gained more features:

1. Decentralization

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

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

When AI Agents provide services externally, trust issues become particularly important, so AI Agents need the operating or verification environment provided by blockchain.

AI Agent also requires a no-threshold usage method, data openness and transparency, interconnectivity, and decentralized characteristics.

2. Incentive Mechanism

This is the most powerful empowerment of cryptographic technology, providing a direct incentive mechanism for developers and users to participate and contribute through the token economic model.

Web2 AI agents mainly rely on traditional business models, such as advertising revenue or subscription services, to sustain operations.

Web2 startups or companies may struggle to achieve profitability for a long time and find it difficult to obtain financing; however, in a Web3 environment, issuing tokens can directly generate cash flow to support project development, such as the use of AI Agents which may require cryptocurrency payments.

A free market economy can foster more innovation.

3. True Permanence

With smart contracts, the AI Agent has truly achieved "immortality".

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

Smart contracts ensure that the code and decision-making mechanisms of the AI Agent exist permanently on the blockchain, unless there is explicit logic to stop or change its behavior.

However, the data it relies on may require continuous updates or maintenance. Without ongoing data input or external interaction, the "immortality" of the AI Agent may be limited to its program logic, lacking dynamism.

Overall, compared to the fact that encryption technology requires AI Agents, AI Agents need the support of encryption technology even more.

The Evolution of the Integration of AI and Cryptocurrency Technology

AI has gone through two stages from large models to AI agents, and the combination of AI and cryptography can also be divided into two stages:

) Large Model Stage: Infrastructure

There are three main evaluation dimensions for AI projects: computing power, algorithms, and data.

The role of Web3 is mainly to add 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. By distributing AI's computational tasks across various nodes in the Web3 network, it is possible to achieve more powerful parallel computing capabilities, which is especially useful for training large AI models.

• Incentive Mechanism: Web3 introduces economic incentive mechanisms, such as token economics, which can encourage network participants to contribute computing resources. Such mechanisms can create a market where AI developers can purchase computing power for machine learning tasks, while providers receive token rewards.

2. Algorithm

• Smart Contracts: Smart contracts in Web3 can automatically execute AI algorithms. AI can design algorithms to run on the blockchain in the form of smart contracts, which not only increases transparency and trust but also enables automated decision-making processes, such as automated market forecasting or content moderation.

• Decentralized algorithm execution: In a Web3 environment, AI algorithms can operate without relying on a single central server, but instead verify and execute through multiple nodes together. This increases the algorithm's resilience to interference and security, preventing single points of failure.

3. Data

• Data Privacy and Ownership: Web3 emphasizes the decentralization of data and users' ownership of their data. AI, combined with Web3, can utilize blockchain technology to manage data permissions, ensuring data privacy, while users can selectively share their data in exchange for rewards, providing AI with a richer yet controlled data source.

• Data Validation and Quality: Blockchain technology can be used for data verification, ensuring the authenticity and integrity of data, which is crucial for the training of AI models. Through Web3, data can be verified before being used, which enhances the quality and credibility of AI algorithm outputs.

• Data Market: Web3 can facilitate the development of data markets, allowing users to directly sell or share data with AI systems in need. This not only provides diverse datasets for AI but also ensures the liquidity and value of data through market mechanisms.

Through these junctions, AI and Web3 can develop synergistically.

• AI can obtain distributed computing power and high-quality data through Web3, while using smart contracts to enhance the execution efficiency and transparency of algorithms.
• Web3 can enhance the intelligence of its system through AI, such as smart resource management and automated contract execution.

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

Computing power projects:

• Render Network: Although primarily focused on rendering, it can also provide AI computing power.
• Akash Network: Provides decentralized cloud computing resources that can be used for AI needs.
• Aethir: Focused on decentralized cloud computing, potentially involving the provision of AI computing power.
• ionet: A decentralized computing power platform that supports AI reasoning and training.

Algorithm projects:

• Cortex: A decentralized world computer capable of running AI and AI-driven applications on the blockchain, focusing on integrating AI into smart contracts.
• Fetchai: A blockchain-based machine learning platform that has launched the no-code management service Agentverse, simplifying the AI agent deployment for Web3 projects.
• iExec RLC: Provides a blockchain-based AI model marketplace that supports confidential computing and decentralized oracles.

Data Projects:

• Vana: Building a decentralized organization for personal genetic data, allowing users to control and potentially benefit from a data marketplace.
• RSS3: Launched an open-source AI architecture that allows any large language model to become an AI agent for Web3, involving the utilization and management of data.

Comprehensive Project:

• Myshell: A decentralized AI consumption layer designed to connect consumers, creators, and open-source researchers. It opens a platform where anyone can create, share, and monetize their AI-native applications.

Overall, during the stage of large models, the combination of encryption technology and AI mainly occurs at the infrastructure level, laying the foundation for the long-term development of AI.

AI Agent Phase: Application Implementation

The emergence of AI Agents marks the entry of AI into the application layer landing stage.

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

1. AI Agent Meme Token

AI Agent Meme token is a special existence, and the Meme token itself is a product of community sentiment.

AI is developing rapidly, and this technology seems very profound. Ordinary people feel anxious, but AI Meme tokens provide an opportunity for them to participate.

Therefore, the AI Meme token brings emotional value to holders by allowing ordinary people to join the AI wave and participate in the AI revolution.

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

From a different perspective, why does the AI Agent want to issue tokens?

On one hand, attracting funds and users through the wealth effect injects momentum into the subsequent development of the industry; on the other hand, the MEME-based issuance method itself is a means of community financing, providing cash flow for the project's own development.

We can take a look at some leading projects:

• $GOAT: The first popular AI Agent Meme token;
• $Fartcoin: Attracts user attention by generating humorous content (such as "jokes");
• $ACT: Aims to create a digital ecosystem for equal interaction between users and AI;
• $WORM: Aims to combine digital biology with blockchain technology to create a unique digital asset that simulates a biological nervous system;

2. Monolithic AI Applications

AI Agents are merging with various subfields of cryptocurrency technology, presenting a vibrant and diverse landscape.

With the development of AI Agents, the tokens issued by AI Agents are no longer simply Meme tokens; they are gradually acquiring the attributes of value tokens, supported by actual use cases.

(1) Genesis Project

• ai16z: The first widely recognized AI Agent, and established the first framework standard Eliza.

(2) Agent Game

• ARC: Developed an AI framework called RIG based on the Rust language, supporting decentralized applications and smart contracts.
• FARM: Focused on enhancing the realism and strategic depth of agricultural games using AI.
• GAME: $GAME empowers AI agents for autonomous operation and intelligence, with deep integration of AI and gaming.

(3) Agent Finance

• $NEUR: Focused on token analysis and decentralized finance interactions, providing intelligent financial decision support.
• $BUZZ: Provides a natural language interface that allows users to conduct and manage decentralized finance transactions more intuitively.

(4) Code Audit

• AgentAUDIT: Automates code auditing using AI technology to enhance code security and quality.

(5) Agent Data Analysis

• REI: Large-scale data processing through AI technology