AI Agent: A Comprehensive Exploration from Concept to Application

As time progresses to 2025, AI has become an indispensable part of our lives. However, many people still have misunderstandings about the definition of Agent.

To be called an "AI Agent", a system must follow the following steps to solve problems:

1. Perception
2. Think
3. Action

Taking the AI Agent for automatic cryptocurrency investment as an example, its working principle is as follows:

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

So, does the widely popular ChatGPT belong to the category of AI Agents? While ChatGPT considers itself an AI Agent, it is more like a smart conversational component, lacking complete multimodal or physical environment operation capabilities.

The main features of the AI Agent include:

• Focus on solving specific problems
• Able to obtain more data from the real world

Compared to all-purpose AIs like ChatGPT, today's AI Agents are more vertical, focusing on specific areas. For example, some AI Agents are specialized in cryptocurrency investment analysis and prediction.

After understanding the definition of AI Agent, let's explore its application areas:

1. AI Investment: Automatically analyze market dynamics and execute trading operations.

2. AI Virtual Companion: A dialogue system with specific personality settings

3. Opinion Output: Provide professional commentary and analysis for specific fields.

4. Virtual Idol: Digital artists created based on AI technology

5. Market Forecast: Focus on the trend predictions of specific markets

6. AI Resource Sharing: Integrating various AI services to provide convenient usage methods.

7. Other applications: including multimedia content creation such as image generation, music composition, etc.

Currently, the AI Agent industry is flourishing, reminiscent of the early ICO craze and the DeFi summer. Various AI frameworks such as AI16Z, Virtual, AVA, etc., provide developers with platforms for rapid deployment of AI Agents.

These frameworks not only provide technical support but also assist developers in issuing tokens, becoming a bridge connecting traditional AI technology with the cryptocurrency market. However, we must also recognize that many so-called AI frameworks are actually just adding token issuance features on top of existing AI technologies.

Despite criticism pointing out that certain AI Agent projects lack technological innovation, the characteristics of the cryptocurrency market make it an ideal platform for rapidly validating ideas. In this competitive environment, high-quality projects have the opportunity to quickly gain attention and support, while low-quality projects will be swiftly eliminated by the market.

Looking to the future, the development of the AI Agent field may bring unexpected innovations. We look forward to seeing more groundbreaking products emerge, promoting the deep integration of AI technology and cryptocurrency.