AI Agent Track Beginner's Guide: Analyzing the Integration of AI and encryption Technology

The speed of AI development is accelerating, and the future is likely to be a world dominated by AI. If we add a core element, it is undoubtedly a world where AI is combined with encryption technology.

Currently, AI has entered a new stage: AI Agent. Whether from the perspective of imaginative space or practical application scenarios, AI Agent is worth our期待.

The train of the era is moving forward at high speed, and we need to hurry to catch this train.

Recently, I have also been continuously learning about AI Agent-related knowledge. This article records my learning path, hoping to help everyone better enter the field of AI Agent.

This is the first article in the AI Agent track introductory guide series, aimed at helping everyone build a comprehensive understanding and framework. In the future, we will continue to explore this field in depth, constantly improving ourselves and seizing the opportunities brought by the AI wave.

What is AI Agent?

Let's set aside the complex concepts for now and directly compare the differences between AI Agents and existing large language models (like ChatGPT).

Current large language models are more like powerful "natural language search engines" that can answer questions and provide suggestions but are unable to truly make decisions and take actions proactively.

The capabilities of the AI Agent surpass the existing large model framework, 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 encryption currency right now, it will give you a bunch of suggestions. However, the AI Agent can track global market information in real-time and dynamically adjust the investment portfolio to maximize returns.

From this, we can give a definition of an AI Agent: An AI Agent is 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 here is: autonomous action.

How does the AI Agent achieve autonomous actions?

AI can convert complex logic into precise conditions (returning True or False based on context), which can then be seamlessly integrated into business scenarios.

First is intent analysis: AI will understand what the user wants to do by analyzing the user's prompt words and context. It not only looks at what the user has said, but also considers the user's previous usage records and specific situations, and then converts these needs into specific program instructions.

Secondly, there is assistance in judgment: AI is like a smart assistant that can transform complex issues, 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, which serves as a personal assistant and can help users handle some business tasks.

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

In conclusion, AI Agent can be considered the next development stage and a new product form of large models, with a very large space for imagination.

The Relationship Between AI Agents and Encryption Technology

AI and encryption technology are not completely separate; the two can be integrated.

More importantly, the AI Agent of Web2 is not the same as the AI Agent of Web3.

Web3's AI Agent is a more advanced and complete AI Agent, perhaps it can be renamed as: Crypto AI Agent.

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

decentralized

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

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

Once an AI Agent provides services externally, there will be trust issues; therefore, the AI Agent needs a running or verification environment provided by the blockchain.

AI agents also require a barrier-free usage method, data openness and transparency, interoperability, and decentralization.

incentive mechanism

This is the strongest empowerment of encryption technology, providing a mechanism through the token economic model that directly incentivizes developers and users to participate and contribute.

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

Web2 startup teams or companies often struggle to become profitable and find funding over time; however, in Web3, by issuing tokens, they can directly obtain cash flow to support project development, such as the use of AI Agents requiring encryption currency payments.

A free market economy can foster more innovation.

true immortality

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

As long as the smart contract is deployed on the blockchain, the AI Agent can automatically operate 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, the data it relies on may need to be continuously updated or maintained. Without ongoing data input or external interaction, the AI Agent's "immortality" may be limited to its program logic and lack dynamism.

In summary, compared to the fact that encryption technology requires AI Agents, AI Agents need encryption technology even more.

The Narrative Evolution of AI + Encryption Technology

The transition from large models to AI Agents consists of two phases, and the combination of AI and encryption technology can also be divided into two phases:

Large Model Stage: Infrastructure

AI projects mainly have three evaluation dimensions: computing power, algorithms, and data.

In fact, the role of Web3 is to provide 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.

computing power

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, more powerful parallel computing capabilities can be achieved, which is particularly useful for training large AI models.

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

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 predictions or content moderation.

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

Data

Data Privacy and Ownership: Web3 emphasizes the decentralization of data and user ownership of data. AI combined with Web3 can leverage blockchain technology to manage data permissions, ensuring data privacy, while users can selectively share 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 validation, 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, improving the output quality and credibility of AI algorithms.

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 the data through market mechanisms.

Through these convergence points, AI and Web3 can develop synergistically.

AI can obtain distributed computing power and high-quality data through Web3, while leveraging smart contracts to improve the execution efficiency and transparency of algorithms;

Web3 can enhance the intelligence of its systems through AI, such as intelligent resource management and automated contract execution.

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

Hashrate 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: Focuses on decentralized cloud computing, possibly involving the provision of AI computing power.
• ionet: A decentralized computing power platform that supports AI inference and training.

Algorithm-based projects:

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

Data projects:

• Vana: Vana is building a DAO for personal genetic data, allowing users to control and potentially profit from a data market.
• RSS3: Launched an open-source AI architecture that enables any large language model to become a Web3 AI agent, involving data utilization and management.

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, in the large model phase, the combination of encryption technology and AI primarily occurs at the infrastructure level, laying the foundation for the long-term development of AI.

AI Agent Stage: Application Implementation

The emergence of AI Agents marks the application layer landing phase of AI.

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

AI Agent Meme Coin

AI Agent Meme coin is a very special existence, and Meme coin itself is a product of community sentiment.

AI is developing too quickly, and this technology seems very profound, making ordinary people very anxious. AI Meme coins have given ordinary people the opportunity to participate.

Therefore, AI Meme Coin brings emotional value to holders by allowing ordinary people to participate in the AI revolution.

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

Let's think from another perspective, why does the AI Agent want to issue tokens?

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

We can take a look at the top assets:

• $GOAT: The first AI Agent Meme coin to gain popularity;
• $Fartcoin: Attracting user attention by generating humorous content (such as "fart 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 the neural system of biological worms.

Monolithic AI Application

The AI Agent is integrating with various segments of encryption technology, presenting a flourishing situation.

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

Genesis Project

• ai16z: The first AI Agent to break out of the mold, establishing the first framework standard Eliza.

Agent Gaming

• ARC: Developed an AI framework called RIG based on the Rust language, supporting decentralized applications (dApp) and smart contracts.
• FARM: Focused on using AI to enhance the realism and strategic depth of agricultural games.
• GAME: $GAME empowers AI agents with autonomous operations and intelligence, deeply integrating AI with games.

Agent DeFi

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

code audit

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

Agent data analysis

• REI: Conduct large-scale data analysis through AI technology, providing