The Development and Application of AI Agent Technology: The Future is Here

As time goes by, we have arrived at the year 2025. Although artificial intelligence has become deeply ingrained in people's hearts, many still have misconceptions about the concept of AI agents.

In fact, to be referred to as an "AI agent", a system must follow the following workflow:

1. Perceive the environment
2. Analyze the situation
3. Take Action

Taking a common AI agent project as an example, such as automated cryptocurrency investment, its operational principle is as follows:

1. Perception: Get the latest cryptocurrency news
2. Analysis: Assess the impact of news on specific cryptocurrencies
3. Action: Buy or sell based on the analysis results.

This process is quite intuitive.

So, does the ChatGPT we use daily count as an AI agent?

Although ChatGPT considers itself an AI agent, it admits that this is only a broad definition. If we broaden the concept of AI agents to include only the need for perception, decision-making, and action loops, then ChatGPT can be seen as an AI agent. However, if the requirements for AI agents include richer perceptual capabilities and execution abilities at the physical or system level, then ChatGPT is more like an intelligent dialogue component rather than a complete multimodal or physical environment operating system.

Why is there such a distinction? There is a subtlety here:

When we want to use ChatGPT, we usually say "I want to use AI" and do not specifically emphasize "I want to use an AI agent."

Therefore, when we mention the term "AI agent", we often want to emphasize certain specific characteristics:

• It focuses more on solving specific problems
• It needs to acquire more data from the real world.

Focus on solving specific problems

Although GPT is just an AI limited to generating dialogues for the future era of General Artificial Intelligence (AGI), it is a typical vertical application.

But in terms of the technological level of 2025, GPT (as well as its competitors such as Gemini, Grok, etc.) is already an all-round player of the current era.

Therefore, from a definitional perspective, current AI agents need to be more specialized than GPT.

Get More Real-World Data

AI agents need to have the ability to actively obtain information from the real world.

For example, another well-known AI agent AIXBT (ranked first by Mindshare) has undoubtedly acquired a large amount of data from the real world, such as various cryptocurrency news, which enables this purple frog to make various comments and predictions.

The time that impressed me the most was when AIXBT predicted that a certain cryptocurrency would be listed on a trading platform within 6 hours, and it actually happened that evening.

In summary, we can provide a complete definition of AI agents:

1. Adopt the "Perception-Analysis-Action" processing flow.
2. Focus on solving specific problems
3. Utilize more data from the real world

Understanding the definition allows us to begin a deeper discussion.

So, what exactly can AI agents do?

This question is actually similar to "What can cryptocurrencies do?", which is a question that many outside commentators have been questioning.

I have tried dozens of AI agents from several mainstream frameworks, which can be roughly categorized into the following types:

1. AI Investment

Such applications are relatively common, where users can deposit funds into the AI agent's wallet, and then the AI agent will analyze market trends based on real-time news and autonomously make buy or sell decisions.

Personal opinion: This concept is interesting, but there are still questions about wallet security.

2. AI Virtual Companion

This is actually what people often refer to as GPT shell applications. However, unlike in the past, the current applications have incorporated some personality restrictions.

For example, Eliza repeatedly emphasizes that she is a real girl, not AI.

There is also AVA, a white-haired beautiful girl who is good at creating videos and analyzing market trends.

Personal opinion: Political correctness in real life is too strong, and it has been proven that people still prefer certain specific images.

3. Opinion Output Category

Users can have the AI agent comment on specific topics.

For example, AIXBT comments on cryptocurrencies and even conducts some research (such as comparing multiple projects in the same field).

For example, Zerebro, although the concept is rather abstract, gives an overall feeling of a mad artist who expresses bizarre theories every day.

There is also a famous E/ACC (Effective Acceleration) critic, and a developer has created an AI agent that imitates his speaking style, posting some peculiar statements.

Personal opinion: Although many people question the existence of such applications, it is important to understand that in the current era, as long as there is traffic, there is significance.

4. Virtual Idol Category

Create AI-based virtual idols that can release music works, such as Luna.

5. Predictive Category

A certain project is specifically designed to provide services for prediction markets, which can also be seen as a variant of opinion types.

6. AI Resource Sharing Category

For example, FXN based on the Eliza framework aims to provide a packaged integrated service for users who are unwilling to pay various AI membership fees, allowing for pay-per-use to save costs.

Personal opinion: This is somewhat like an AI version of group buying service.

7. Other Categories

This includes painting, GIF generation, music creation, etc., which will not be detailed one by one here. Overall, they do not exceed the scope of the multimodal capabilities of the mainstream large models in the current AI industry.

So, what is the AI framework?

Currently, the AI agent field is showing a blooming state, which is quite similar to the ICO boom and DeFi Summer of previous years.

If we were to make an analogy, platforms like AI16Z, Virtual, and AVA are equivalent to public chains, and their frameworks can be used to quickly deploy personal AI agents.

Of course, these services are chargeable.

For example, if you want to deploy an AI agent using AI16Z/Eliza, you need to allocate a portion of tokens to the AI16Z fund pool and stake the platform tokens of AI16Z, which also explains why the funds in the AI16Z fund pool are constantly increasing.

To launch an AI agent using the Virtual framework, a payment of 100 Virtual coins is required, and you need to pair your own tokens with Virtual to form an LP. This model is similar to SOL in relation to certain projects.

The operating model of AVA and Swarms is generally similar to the previous two, but due to their later start, their ecosystem is relatively less rich, although it is actively developing.

It is worth mentioning that AVA originated from an AI project called Holoworld within a certain trading platform incubator, which decisively transformed into an AI agent specializing in video generation.

The founder of Swarms gives the impression of a genius youth. This framework has been operating in traditional fields for many years, with a strong emphasis on technology, highlighting that tasks can be accomplished through collaboration among multiple agents (which is also the origin of the name "Swarms").

Of course, there are also some fringe news, such as the founder of Swarms being harshly criticized by Shaw from AI16Z for poor technology, and even using some vulgar language.

However, speaking of which.

Apps are apps, tokens are tokens.

The most important feature of these frameworks, or the biggest difference from traditional AI frameworks, is that they can conveniently help developers issue tokens.

Or to put it more bluntly: essentially these AI frameworks are also a transformation of the existing AI industry framework, a product that stands on the shoulders of giants, and their greatest originality lies in the token issuance module.

The imagined AI developer could be a founding team member of OpenAI, a Stanford PhD, earning a million dollars a year.

In reality, AI developers may be more inclined to issue tokens first and then discuss.

Therefore, with the help of these frameworks, if you want to issue an AI agent, your process might be as follows:

1. Determine the theme
2. Choose your preferred framework and make the payment
3. Issue tokens and list them
4. Create social media accounts for AI agents to publish content.
5. Add more features for AI agents (development required)
6. Develop the official website, establish a community, contact exchanges
7. Establish your own AI framework
8. Encourage more people to identify new themes...

This process is somewhat like the realm advancement in cultivation novels.

The corresponding valuation may be (for reference only):

1, 100, 10,000, 100,000, 1,000,000, 10,000,000, 100,000,000, 1,000,000,000

In contrast, the development process of traditional AI companies may be:

1. Determine the topic
2. Financing
3. Burn Money
4. Financing
5. Burn money
6. Financing
7. Listing

You see, the combination of cryptocurrency and AI actually helps you complete this step of going public.

This is a disruptive innovation, in the literal sense of "disruption."

In addition, due to the outstanding performance of these AI frameworks in the secondary market (even achieving a market value of 1 billion or even 2 billion dollars without being listed on any mainstream exchange), they have become the only hot track in the cryptocurrency field at present.

Nowadays, this field has entered a stage where everyone is out in the streets.

It must be acknowledged that there is indeed an overheating phenomenon in the short term. If there were an AI agent measuring fear and greed, the current greed index would be at least 90, and it may even have exceeded the scale.

We often see some real AI developers from outside the circle criticizing these projects for lacking technical content.

However, my point of view is that the best use case for cryptocurrency is 【rapid market discovery】, although this may bring some bubbles.

Excellent products and founders can quickly gain exposure and community support, while poor products and founders will immediately face negative validation from the market.

In this fierce competitive environment, it may indeed give birth to innovative products that traditional AI developers did not expect.

Let us look forward to the arrival of that day.

Can artificial intelligence really have self-awareness? Will they think like the replicants in the movie "Blade Runner"? There are currently no clear answers to these questions. However, it is certain that with the continuous advancement of AI technology, humanity is facing unprecedented challenges and opportunities. What the future will look like in 20, 30... or even further years is something we find hard to imagine now.