Forward the Original Title: A Beginner’s Guide to AI Agents: An In-depth Analysis of AI+Crypto Narrative Evolution
The train of the times is roaring by, please get on board quickly!
The development of AI is moving at an incredible speed. The future is undoubtedly an AI-driven world, and if we were to add one more key element, it would certainly be an AI + Crypto world.
Today, AI has evolved to a new stage: AI Agents.
AI Agents hold immense potential, both in terms of imaginative possibilities and practical applications.
I’ve been diving deep into learning about AI Agents recently, and in this article, I’ve documented my learning path in the hope of helping others get started in the AI Agent field.
This article is the first introductory guide to the AI Agent space. It also helps readers to establish an overall understanding and establish a structured overview. Later, we will continue to delve into the space, aiming to refine skills and seize the opportunities of the AI wave.
Let’s put aside various complex concepts and directly compare the differences between AI Agent and existing large models (such as ChatGPT).
The current large model is more like a powerful “natural language search engine” that can answer questions and provide suggestions, but cannot truly proactively make decisions and execute them.
The capabilities of AI Agent go beyond the scope of existing large models and are no longer limited to “data processing”, but can complete a complete closed loop from “perception” to “action”.
Let’s use an intuitive example: Now if you ask ChatGPT how to invest in Crypto, ChatGPT will give you a bunch of suggestions, but AI Agent can help you track global market information in real time and dynamically adjust your investment portfolio to maximize returns.
From this, we can abstract the concept of AI Agent: 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. interactive.
The core difference here is: Act autonomously.
How does an AI Agent achieve autonomous action?
Through AI, complex logic can be converted into precise conditions (returning True or False according to the context), and then it can be seamlessly integrated into the business scenario.
The first is intention discovery: 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 said, but also considers the user’s previous usage records and specific circumstances, and then converts these needs into specific program instructions.
The second is to assist in decision-making: AI is like a smart assistant that can analyze some complex problems that are difficult for humans to handle and turn them into simple yes or no answers, or a few fixed options. 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 Agent can be divided into two types:
One is that an AI Agent is equivalent to a personal assistant, which can assist users in handling some business.
The other goes a step further. The AI Agent itself is an independent individual, has its own identity or brand, and provides services to many users.
In short, AI Agent can be said to be the next development stage and new product form of large models. An AI Agent has a very large room for imagination.
AI and Crypto are not distinct, they can be integrated.
More importantly, the AI Agent of Web2 is different from the AI Agent of Web3.
Web3’s AI Agent is a higher-level, more complete AI Agent. It may be called another name: Crypto AI Agent.
With the help of Crypto’s capabilities, AI Agent has more features:
After combining Crypto, the operation, data storage and decision-making process of AI Agent are more transparent and not controlled by a single entity.
Web2 AI Agent These agents are usually controlled by a centralized company or platform, and the data and decision-making process are concentrated in the hands of one or a few entities.
Once an AI Agent provides services to the outside world, there will be trust issues, so the AI Agent needs the running or verification environment provided by the blockchain.
AI Agent also requires barrier-free usage, open and transparent data, interconnection and decentralization.
This is the strongest empowerment of Crypto. Through the token economic model, it provides a mechanism that directly encourages developers and users to participate and contribute.
Web2 AI Agent mainly relies on traditional business models such as advertising revenue or subscription services to maintain operations.
The entrepreneurial team or company of Web2 cannot make a profit for a long time, and it is difficult to raise funds; but with Web3, through the issuance of coins, you can directly obtain cash flow to support project development. For example, the use of AI Agent requires Crypto payment.
A free market economy can generate more innovation.
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 can theoretically run indefinitely.
Smart contracts can ensure that the AI Agent’s code and decision-making mechanism exist permanently on the blockchain unless there is clear logic to stop or change its behavior.
But the data it relies on may require ongoing updating or maintenance. Without continuous input of data or interaction with the outside world, the AI Agent’s “immortality” may be limited to its program logic and is not dynamic.
In short, AI Agent needs Crypto more than Crypto needs AI Agent.
AI has two stages from large model to AI Agent. The combination of AI and Crypto can also be divided into two stages:
AI projects mainly have three evaluation dimensions, computing power, algorithm and data.
In fact, the role of Web3 is to add an incentive system to AI and tokenize computing power, algorithms and data.
Therefore, the combination of AI and Web3 can also be discussed from the three dimensions of computing power, algorithm, and data:
(1) Computational Power:
Distributed computing network: Blockchain is naturally distributed. AI can use Web3’s distributed network to obtain more computing resources. By distributing AI computing tasks to 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 an economic incentive mechanism, such as the token economy, which can motivate participants in the network to contribute their computing resources. Such a mechanism can be used to create a market where AI developers can purchase computing power to perform machine learning tasks, and providers are rewarded with tokens.
(2) Algorithms:
Smart contracts: Smart contracts in Web3 can automatically execute AI algorithms. AI can design algorithms to run in the form of smart contracts on the blockchain, 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 the Web3 environment, AI algorithms can not rely on a single central server, but can be verified and executed through multiple nodes. This increases the interference resistance and security of the algorithm and prevents single points of failure.
(3) Data:
Data privacy and ownership: Web3 emphasizes the decentralization of data and user ownership of data. AI combined with Web3 can use blockchain technology to manage data permissions and ensure data privacy. At the same time, users can selectively share data in exchange for rewards, which provides AI with a richer but controlled data source.
Data verification and quality: Blockchain technology can be used for data verification to ensure the authenticity and integrity of the data, which is very critical 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 marketplace: Web3 can promote the development of the data marketplace, and users can directly sell or share data to the AI systems in need. This not only provides diversified data sets for AI, but also ensures the liquidity and value of data through market mechanisms.
Through these combination points, AI and Web3 can mutually reinforce one another:
AI can leverage Web3 for distributed computational power, high-quality data, and increased algorithm efficiency and transparency through smart contracts.
Web3 can enhance its ecosystem with AI for smarter resource management and automated contract execution.
Focusing on these three dimensions, many well-known projects have appeared on the market:
Computational Power:
-Render Network: Although it mainly focuses on rendering, it can also provide AI computing power.
-Akash Network: Provides decentralized cloud computing resources that can be used for AI needs.
-Aethir: Focus on decentralized cloud computing, which may involve the provision of AI computing power.
-ionet: A decentralized computing platform that supports AI reasoning and training.
Algorithms:
-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, it launched the code-free management service Agentverse to simplify the deployment of AI agents for Web3 projects.
-iExec RLC: Provides a blockchain-based AI model market that supports confidential computing and decentralized oracles.
Data:
-Vana: Vana is building a DAO for personal genetic data, a data market that allows users to control and potentially benefit from it.
-RSS3: Launched an open source AI architecture that enables any large-scale language model to become an AI agent for Web3, involving data utilization and management.
Comprehensive projects:
-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.
Generally speaking, in the large model stage, the integration of Crypto and AI primarily focuses on the infrastructure layer, laying the groundwork for AI’s long-term development.
The emergence of AI Agent marks the implementation stage of AI in the application layer.
AI Agent can also be subdivided into three development stages: Meme coin stage, single AI application stage and AI Agent framework standard stage.
AI Agent Meme Coin is a very special existence. Meme Coin itself is the product of community sentiment.
AI is developing too fast, and this technology seems to be very profound, making ordinary people very anxious. AI Meme coins give ordinary people the opportunity to participate.
Therefore, AI Meme coins bring an emotional value to holders to participate in the AI revolution, allowing ordinary people to participate in the AI wave.
The final result is: AI + MEME uses the wealth effect to accelerate the market education and dissemination of AI.
Thinking from another perspective, why does the AI Agent issue tokens?
On the one hand, it attracts funds and users through the wealth effect, injecting momentum into the subsequent development of the industry; on the other hand, the MEME issuance method itself is a means of community financing, providing cash flow for the development of the project itself.
We can take a look at the relevant leading projects:
$GOAT: The first popular AI Agent Meme coin;
$Fartcoin: Attract user attention by generating humorous content (such as “fart jokes”);
$ACT: aims to create a digital ecosystem where users and AI interact equally;
$WORM: Aims to combine digital biology and blockchain technology to create a unique digital asset that simulates the nervous system of a biological worm;
AI Agent is integrating with various subdivisions of Crypto, showing a situation where a hundred flowers are blooming.
With the development of AI Agent, the tokens issued by AI Agent are no longer pure Meme coins. With the support of actual use scenarios, they gradually have the attributes of value coins.
(1) Pioneering Project
$ai16z: The first AI Agent to emerge from the industry, and established the first framework standard Eliza.
(2)Agent Gaming
$ARC: An AI framework called RIG was developed based on the Rust language to support decentralized applications (dApp) and smart contracts.
$FARM: Focus on using AI to improve the authenticity and strategic depth of farming games.
$GAME: $GAME empowers the autonomous operation and intelligence of AI agents, and deeply integrates AI and games.
(3)Agent DeFi
$NEUR: Focus on token analysis and DeFi interaction, providing intelligent financial decision support.
$BUZZ: Provides a natural language interface to enable users to conduct DeFi transactions and management more intuitively.
(4) Code Auditing
AgentAUDIT: Use AI technology to automate code audits and improve code security and quality.
(5) Data Analysis Agents
$REI: Conduct large-scale data analysis through AI technology to provide insight and prediction services.
(6) Autonomous AI Agent
$LMT: An AI Agent that learns and performs tasks autonomously, aiming to reduce human intervention.
$GRIFFAIN: An AI Agent that can autonomously optimize its own behavior, especially for decision-making and strategy formulation in complex environments.
When there are too many single AI Agents, a set of common framework standards is needed to achieve “one-click AI delivery.”
What is an AI Agent framework standard?
The AI Agent framework standard simplifies the development and deployment process of AI Agent by providing a unified set of specifications and tools.
It allows developers to create an AI Agent that can interact with multiple clients (Twitter, Discord, Telegram, etc.), extend functionality through plug-ins, and leverage AI technology to enhance its intelligence.
These standards and basic libraries (such as memory storage, session isolation, context generation, etc.) ensure that the operation of AI Agent is efficient, secure and user-friendly.
By connecting various AI platform interfaces, the framework standards further enhance the capabilities of AI Agents, enabling them to utilize the latest AI technology to provide better services.
In short, the AI Agent framework standard is infrastructure and platform, and it can form its own ecosystem. The narrative space is naturally higher than that of a single AI application.
The AI Agent framework standards are still in a state of chaos.
AI Agent framework standards mainly include the following:
ai16z: Built the Eliza framework to support multiple platforms such as Discord, Twitter, Telegram, etc., allowing AI Agents to seamlessly integrate with these platforms. @ai16zdao
Virtual: The GAME framework is built, specifically designed for games and virtual environments, allowing AI Agents to operate autonomously or interact with players in these environments. @virtuals_io
Swarms: A multi-agent AI framework. Based on its framework, developers can create and manage multiple AI agents. It is suitable for scenarios that require high-complexity coordination, such as simulating social behavior, complex business process automation, or large-scale data processing. @swarms_corp
ZEREBRO: Built the ZerePy framework, which is equivalent to Optimism’s OP Stack, making it easier and standardized to develop and deploy single AI applications, allowing these agents to independently create and distribute content on social platforms. @0xzerebro
Related ecosystems have emerged around these frameworks, and we need to focus on these ecosystems when studying the AI Agent track.
This article serves as an introduction to spark interest in the AI Agent space, but consistent effort and learning are key.
Here are some highly professional influencers in the AI Agent field who are worth following for long-term insights:
-Haotian: @tmel0211 A hardcore research and investment influencer with deep insights into AI Agents.
-wizard: @0xcryptowizard An ACT diamond-hand veteran with multiple successful trades and a unique perspective on AI meme tokens.
-brightness: @0xNing0x A senior researcher at Web3Caff, a highly professional investment researcher and hands-on practitioner.
-Aunt AI: @ai_9684xtpa True to the name, they offer a systematic methodology for navigating the AI space.
-Poyin: @poyincom A research-focused investment influencer and trader specializing in human behavior, currently studying AI meme tokens.
-Michael: @Michael_Liu93 An expert in market-making strategies with profound insights into the dynamics behind meme tokens.
-Crypto Skanda: @thecryptoskanda Creator of the “Three-Pan Theory” and a legendary trader with deep understanding of various trading setups.
-Star: @starzqeth Head of Web3Brand, recently interviewed several key figures in the AI field. Recommended podcast: Day1Global.
(These are influencers I frequently follow. If you know of other high-quality contributors, please feel free to recommend them!)
In short, the AI Agent narrative is already gaining momentum.
Every year in our industry, a main narrative breaks out. Around this main narrative, many star projects will emerge, and naturally there will be many opportunities.
For example, DeFi Summer in 2020, Inscription Summer in 2023, Meme Summer in 2024. And now AI Summer is emerging in 2025.
Don’t waste every scarce opportunity to make wealth.
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Forward the Original Title: A Beginner’s Guide to AI Agents: An In-depth Analysis of AI+Crypto Narrative Evolution
The train of the times is roaring by, please get on board quickly!
The development of AI is moving at an incredible speed. The future is undoubtedly an AI-driven world, and if we were to add one more key element, it would certainly be an AI + Crypto world.
Today, AI has evolved to a new stage: AI Agents.
AI Agents hold immense potential, both in terms of imaginative possibilities and practical applications.
I’ve been diving deep into learning about AI Agents recently, and in this article, I’ve documented my learning path in the hope of helping others get started in the AI Agent field.
This article is the first introductory guide to the AI Agent space. It also helps readers to establish an overall understanding and establish a structured overview. Later, we will continue to delve into the space, aiming to refine skills and seize the opportunities of the AI wave.
Let’s put aside various complex concepts and directly compare the differences between AI Agent and existing large models (such as ChatGPT).
The current large model is more like a powerful “natural language search engine” that can answer questions and provide suggestions, but cannot truly proactively make decisions and execute them.
The capabilities of AI Agent go beyond the scope of existing large models and are no longer limited to “data processing”, but can complete a complete closed loop from “perception” to “action”.
Let’s use an intuitive example: Now if you ask ChatGPT how to invest in Crypto, ChatGPT will give you a bunch of suggestions, but AI Agent can help you track global market information in real time and dynamically adjust your investment portfolio to maximize returns.
From this, we can abstract the concept of AI Agent: 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. interactive.
The core difference here is: Act autonomously.
How does an AI Agent achieve autonomous action?
Through AI, complex logic can be converted into precise conditions (returning True or False according to the context), and then it can be seamlessly integrated into the business scenario.
The first is intention discovery: 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 said, but also considers the user’s previous usage records and specific circumstances, and then converts these needs into specific program instructions.
The second is to assist in decision-making: AI is like a smart assistant that can analyze some complex problems that are difficult for humans to handle and turn them into simple yes or no answers, or a few fixed options. 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 Agent can be divided into two types:
One is that an AI Agent is equivalent to a personal assistant, which can assist users in handling some business.
The other goes a step further. The AI Agent itself is an independent individual, has its own identity or brand, and provides services to many users.
In short, AI Agent can be said to be the next development stage and new product form of large models. An AI Agent has a very large room for imagination.
AI and Crypto are not distinct, they can be integrated.
More importantly, the AI Agent of Web2 is different from the AI Agent of Web3.
Web3’s AI Agent is a higher-level, more complete AI Agent. It may be called another name: Crypto AI Agent.
With the help of Crypto’s capabilities, AI Agent has more features:
After combining Crypto, the operation, data storage and decision-making process of AI Agent are more transparent and not controlled by a single entity.
Web2 AI Agent These agents are usually controlled by a centralized company or platform, and the data and decision-making process are concentrated in the hands of one or a few entities.
Once an AI Agent provides services to the outside world, there will be trust issues, so the AI Agent needs the running or verification environment provided by the blockchain.
AI Agent also requires barrier-free usage, open and transparent data, interconnection and decentralization.
This is the strongest empowerment of Crypto. Through the token economic model, it provides a mechanism that directly encourages developers and users to participate and contribute.
Web2 AI Agent mainly relies on traditional business models such as advertising revenue or subscription services to maintain operations.
The entrepreneurial team or company of Web2 cannot make a profit for a long time, and it is difficult to raise funds; but with Web3, through the issuance of coins, you can directly obtain cash flow to support project development. For example, the use of AI Agent requires Crypto payment.
A free market economy can generate more innovation.
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 can theoretically run indefinitely.
Smart contracts can ensure that the AI Agent’s code and decision-making mechanism exist permanently on the blockchain unless there is clear logic to stop or change its behavior.
But the data it relies on may require ongoing updating or maintenance. Without continuous input of data or interaction with the outside world, the AI Agent’s “immortality” may be limited to its program logic and is not dynamic.
In short, AI Agent needs Crypto more than Crypto needs AI Agent.
AI has two stages from large model to AI Agent. The combination of AI and Crypto can also be divided into two stages:
AI projects mainly have three evaluation dimensions, computing power, algorithm and data.
In fact, the role of Web3 is to add an incentive system to AI and tokenize computing power, algorithms and data.
Therefore, the combination of AI and Web3 can also be discussed from the three dimensions of computing power, algorithm, and data:
(1) Computational Power:
Distributed computing network: Blockchain is naturally distributed. AI can use Web3’s distributed network to obtain more computing resources. By distributing AI computing tasks to 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 an economic incentive mechanism, such as the token economy, which can motivate participants in the network to contribute their computing resources. Such a mechanism can be used to create a market where AI developers can purchase computing power to perform machine learning tasks, and providers are rewarded with tokens.
(2) Algorithms:
Smart contracts: Smart contracts in Web3 can automatically execute AI algorithms. AI can design algorithms to run in the form of smart contracts on the blockchain, 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 the Web3 environment, AI algorithms can not rely on a single central server, but can be verified and executed through multiple nodes. This increases the interference resistance and security of the algorithm and prevents single points of failure.
(3) Data:
Data privacy and ownership: Web3 emphasizes the decentralization of data and user ownership of data. AI combined with Web3 can use blockchain technology to manage data permissions and ensure data privacy. At the same time, users can selectively share data in exchange for rewards, which provides AI with a richer but controlled data source.
Data verification and quality: Blockchain technology can be used for data verification to ensure the authenticity and integrity of the data, which is very critical 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 marketplace: Web3 can promote the development of the data marketplace, and users can directly sell or share data to the AI systems in need. This not only provides diversified data sets for AI, but also ensures the liquidity and value of data through market mechanisms.
Through these combination points, AI and Web3 can mutually reinforce one another:
AI can leverage Web3 for distributed computational power, high-quality data, and increased algorithm efficiency and transparency through smart contracts.
Web3 can enhance its ecosystem with AI for smarter resource management and automated contract execution.
Focusing on these three dimensions, many well-known projects have appeared on the market:
Computational Power:
-Render Network: Although it mainly focuses on rendering, it can also provide AI computing power.
-Akash Network: Provides decentralized cloud computing resources that can be used for AI needs.
-Aethir: Focus on decentralized cloud computing, which may involve the provision of AI computing power.
-ionet: A decentralized computing platform that supports AI reasoning and training.
Algorithms:
-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, it launched the code-free management service Agentverse to simplify the deployment of AI agents for Web3 projects.
-iExec RLC: Provides a blockchain-based AI model market that supports confidential computing and decentralized oracles.
Data:
-Vana: Vana is building a DAO for personal genetic data, a data market that allows users to control and potentially benefit from it.
-RSS3: Launched an open source AI architecture that enables any large-scale language model to become an AI agent for Web3, involving data utilization and management.
Comprehensive projects:
-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.
Generally speaking, in the large model stage, the integration of Crypto and AI primarily focuses on the infrastructure layer, laying the groundwork for AI’s long-term development.
The emergence of AI Agent marks the implementation stage of AI in the application layer.
AI Agent can also be subdivided into three development stages: Meme coin stage, single AI application stage and AI Agent framework standard stage.
AI Agent Meme Coin is a very special existence. Meme Coin itself is the product of community sentiment.
AI is developing too fast, and this technology seems to be very profound, making ordinary people very anxious. AI Meme coins give ordinary people the opportunity to participate.
Therefore, AI Meme coins bring an emotional value to holders to participate in the AI revolution, allowing ordinary people to participate in the AI wave.
The final result is: AI + MEME uses the wealth effect to accelerate the market education and dissemination of AI.
Thinking from another perspective, why does the AI Agent issue tokens?
On the one hand, it attracts funds and users through the wealth effect, injecting momentum into the subsequent development of the industry; on the other hand, the MEME issuance method itself is a means of community financing, providing cash flow for the development of the project itself.
We can take a look at the relevant leading projects:
$GOAT: The first popular AI Agent Meme coin;
$Fartcoin: Attract user attention by generating humorous content (such as “fart jokes”);
$ACT: aims to create a digital ecosystem where users and AI interact equally;
$WORM: Aims to combine digital biology and blockchain technology to create a unique digital asset that simulates the nervous system of a biological worm;
AI Agent is integrating with various subdivisions of Crypto, showing a situation where a hundred flowers are blooming.
With the development of AI Agent, the tokens issued by AI Agent are no longer pure Meme coins. With the support of actual use scenarios, they gradually have the attributes of value coins.
(1) Pioneering Project
$ai16z: The first AI Agent to emerge from the industry, and established the first framework standard Eliza.
(2)Agent Gaming
$ARC: An AI framework called RIG was developed based on the Rust language to support decentralized applications (dApp) and smart contracts.
$FARM: Focus on using AI to improve the authenticity and strategic depth of farming games.
$GAME: $GAME empowers the autonomous operation and intelligence of AI agents, and deeply integrates AI and games.
(3)Agent DeFi
$NEUR: Focus on token analysis and DeFi interaction, providing intelligent financial decision support.
$BUZZ: Provides a natural language interface to enable users to conduct DeFi transactions and management more intuitively.
(4) Code Auditing
AgentAUDIT: Use AI technology to automate code audits and improve code security and quality.
(5) Data Analysis Agents
$REI: Conduct large-scale data analysis through AI technology to provide insight and prediction services.
(6) Autonomous AI Agent
$LMT: An AI Agent that learns and performs tasks autonomously, aiming to reduce human intervention.
$GRIFFAIN: An AI Agent that can autonomously optimize its own behavior, especially for decision-making and strategy formulation in complex environments.
When there are too many single AI Agents, a set of common framework standards is needed to achieve “one-click AI delivery.”
What is an AI Agent framework standard?
The AI Agent framework standard simplifies the development and deployment process of AI Agent by providing a unified set of specifications and tools.
It allows developers to create an AI Agent that can interact with multiple clients (Twitter, Discord, Telegram, etc.), extend functionality through plug-ins, and leverage AI technology to enhance its intelligence.
These standards and basic libraries (such as memory storage, session isolation, context generation, etc.) ensure that the operation of AI Agent is efficient, secure and user-friendly.
By connecting various AI platform interfaces, the framework standards further enhance the capabilities of AI Agents, enabling them to utilize the latest AI technology to provide better services.
In short, the AI Agent framework standard is infrastructure and platform, and it can form its own ecosystem. The narrative space is naturally higher than that of a single AI application.
The AI Agent framework standards are still in a state of chaos.
AI Agent framework standards mainly include the following:
ai16z: Built the Eliza framework to support multiple platforms such as Discord, Twitter, Telegram, etc., allowing AI Agents to seamlessly integrate with these platforms. @ai16zdao
Virtual: The GAME framework is built, specifically designed for games and virtual environments, allowing AI Agents to operate autonomously or interact with players in these environments. @virtuals_io
Swarms: A multi-agent AI framework. Based on its framework, developers can create and manage multiple AI agents. It is suitable for scenarios that require high-complexity coordination, such as simulating social behavior, complex business process automation, or large-scale data processing. @swarms_corp
ZEREBRO: Built the ZerePy framework, which is equivalent to Optimism’s OP Stack, making it easier and standardized to develop and deploy single AI applications, allowing these agents to independently create and distribute content on social platforms. @0xzerebro
Related ecosystems have emerged around these frameworks, and we need to focus on these ecosystems when studying the AI Agent track.
This article serves as an introduction to spark interest in the AI Agent space, but consistent effort and learning are key.
Here are some highly professional influencers in the AI Agent field who are worth following for long-term insights:
-Haotian: @tmel0211 A hardcore research and investment influencer with deep insights into AI Agents.
-wizard: @0xcryptowizard An ACT diamond-hand veteran with multiple successful trades and a unique perspective on AI meme tokens.
-brightness: @0xNing0x A senior researcher at Web3Caff, a highly professional investment researcher and hands-on practitioner.
-Aunt AI: @ai_9684xtpa True to the name, they offer a systematic methodology for navigating the AI space.
-Poyin: @poyincom A research-focused investment influencer and trader specializing in human behavior, currently studying AI meme tokens.
-Michael: @Michael_Liu93 An expert in market-making strategies with profound insights into the dynamics behind meme tokens.
-Crypto Skanda: @thecryptoskanda Creator of the “Three-Pan Theory” and a legendary trader with deep understanding of various trading setups.
-Star: @starzqeth Head of Web3Brand, recently interviewed several key figures in the AI field. Recommended podcast: Day1Global.
(These are influencers I frequently follow. If you know of other high-quality contributors, please feel free to recommend them!)
In short, the AI Agent narrative is already gaining momentum.
Every year in our industry, a main narrative breaks out. Around this main narrative, many star projects will emerge, and naturally there will be many opportunities.
For example, DeFi Summer in 2020, Inscription Summer in 2023, Meme Summer in 2024. And now AI Summer is emerging in 2025.
Don’t waste every scarce opportunity to make wealth.