AI Agent Track Entry Guide: In-Depth Analysis of the Integration and Development of AI and Encryption Technologies

The speed of AI development is beyond imagination, and the future will undoubtedly belong to AI. If we add a core element, it must be a world where AI and encryption technology are combined.

Currently, AI has entered a new stage: AI Agent. Whether from the perspective of imagination or practical scenarios, AI Agent is worth looking forward to.

The train of the era is whistling past, and we need to hurry up and catch this ride.

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

This is the first article in the AI Agent introductory guide series, aimed at helping readers establish a holistic understanding and framework comprehension. In the future, we will continue to delve deeper into this field, continuously refine our knowledge, and seize the opportunities brought by the wave of AI.

What exactly is an AI Agent?

Let's first set aside the complex concepts and directly compare the differences between AI Agents and existing large language models (such as ChatGPT).

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

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

A straightforward example: if you ask ChatGPT how to invest in cryptocurrency now, it will give you a bunch of suggestions, but the AI Agent can help you track global market information in real time and dynamically adjust your portfolio to maximize profits.

From this, we can abstract the concept of AI Agent: an AI Agent (Artificial Intelligence 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 specifically achieve autonomous action?

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 prompts and context. It not only looks at what the user said but also takes into account the user's previous usage history and specific circumstances, then translates these needs into specific program instructions.

Secondly, there is assistance in judgment: AI is like a smart assistant that can transform complex problems that 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 is equivalent to a personal assistant that can help users handle some business.

Another type takes it a step further, where the AI Agent itself is an independent entity with its own identity or brand, providing services for many users.

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

The Relationship Between AI Agents and Encryption Technology

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

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

The Web3 AI Agent is a more advanced and complete AI Agent, perhaps it could be renamed as: Crypto AI Agent.

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

Decentralization

After integrating encryption technology, the operations, data storage, and decision-making processes of the AI Agent are more transparent and 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 to the outside, there will be trust issues; therefore, the AI Agent needs a runtime or verification environment provided by the blockchain.

AI Agent also requires 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 maintain operations.

Web2 startups or companies struggle to become profitable after a long time and find it difficult to raise funds; however, in Web3, by issuing tokens, they can directly obtain cash flow to support project development, such as the use of AI Agents which requires payment in cryptocurrency.

A free market economy can foster more innovation.

True Eternal Life

With smart contracts, AI agents truly achieve "immortality."

As long as the smart contract is deployed on the blockchain, the AI Agent can automatically operate according to its rules and 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 explicit logic to stop or change their behavior.

However, the data it relies on may need to be continuously updated or maintained. Without continuous input of data or interaction from the outside, the "immortality" of the AI Agent 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 more.

The Narrative Evolution of AI + Encryption Technology

The transition from large language models to AI agents represents two stages, and the integration of AI with encryption technology can also be divided into two stages:

Large Language Model Stage: Infrastructure

AI projects are mainly evaluated on three 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:

Computational Power:

Distributed Computing Network: Blockchain inherently possesses distributed characteristics. AI can leverage the distributed network of Web3 to acquire 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 especially useful for training large AI models.

Incentive Mechanism: Web3 introduces economic incentive mechanisms, such as token economics, which can encourage 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.

Algorithms:

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 utilize blockchain technology to manage data permissions, ensuring data privacy, while users can selectively share data in exchange for rewards, providing AI with richer yet controlled data sources.

Data Validation and Quality: Blockchain technology can be used for data validation to ensure the authenticity and integrity of data, which is critical for training 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 data through market mechanisms.

Through these junctions, AI and Web3 can develop synergistically.

• AI can acquire distributed computing power and high-quality data through Web3, while utilizing smart contracts to enhance 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:

Computational 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 platform that supports AI inference and training.

Algorithm (Algorithms) class 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, has 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-type projects:

• Vana: Vana is building a DAO for personal genetic data, allowing users to control and potentially benefit 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 up a platform where anyone can create, share, and monetize their AI-native applications.

Overall, during the stage of large language 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 Stage: Application Landing

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

AI Agents can also be subdivided into three development stages: the Meme coin stage, the single AI application stage, and the 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 Depth, causing ordinary people to feel very anxious. AI Meme coin has provided ordinary people with the opportunity to participate.

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

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

Think from another perspective, why does the AI Agent need 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-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 the top assets:

• $GOAT: The first popular AI Agent Meme coin;
• $Fartcoin: Attracting user attention by generating humorous content (such as "fart jokes");
• $ACT: Aiming 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 nervous system of biological worms.

Monolithic AI Applications

AI Agent is integrating with various sub-sectors of encryption technology, presenting a blooming situation.

With the development of AI Agents, the tokens issued by AI Agents are no longer just simple Meme coins. Supported by actual use cases, they gradually possess the attributes of value coins.

Genesis Project

• ai16z: The first AI Agent to break into the mainstream and established the first framework standard Eliza.

Agent Gaming

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

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 and manage DeFi transactions more intuitively.

Code Audit

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