Decoding AI Agent: The Intelligent Force Shaping the New Economic Ecology of the Future

1. Background Overview

1.1 Introduction: "New Partners" in the Intelligent Era

Each cryptocurrency cycle brings new infrastructure that drives the entire industry forward.

• In 2017, the rise of smart contracts led to the booming development of ICOs.
• In 2020, the liquidity pools of DEX brought about the summer boom of DeFi.
• In 2021, the emergence of a large number of NFT series marked the arrival of the era of digital collectibles.
• In 2024, the outstanding performance of a certain launch platform led the trend of memecoins and launch platforms.

It is important to emphasize that the emergence of these vertical fields is not solely due to technological innovation, but also the result of a perfect combination of financing models and bull market cycles. When opportunity meets the right timing, it can lead to significant transformations. Looking ahead to 2025, it is clear that the emerging field of the 2025 cycle will be AI agents. This trend peaked last October, when a certain token was launched on October 11, 2024, and reached a market value of $150 million by October 15. Shortly thereafter, on October 16, a certain protocol launched Luna, making its debut with the live streaming image of a neighbor girl IP, igniting the entire industry.

So, what exactly is an AI Agent?

Everyone must be familiar with the classic movie "Resident Evil"; the AI system Red Queen leaves a deep impression. The Red Queen is a powerful AI system that controls complex facilities and security systems, capable of autonomously perceiving the environment, analyzing data, and taking swift action.

In fact, AI Agents and the core functions of the Red Queen have many similarities. In reality, AI Agents play a similar role to some extent; they are the "smart guardians" in the modern technology field, helping businesses and individuals tackle complex tasks through autonomous perception, analysis, and execution. From self-driving cars to intelligent customer service, AI Agents have penetrated various industries and become a key force in enhancing efficiency and innovation. These autonomous intelligent entities, like invisible team members, possess comprehensive capabilities ranging from environmental perception to decision execution, gradually infiltrating various sectors to promote dual improvements in efficiency and innovation.

For example, an AI AGENT can be used for automated trading, managing portfolios and executing trades in real-time based on data collected from a data platform or social platform, continuously optimizing its performance through iterations. The AI AGENT is not a single form but is categorized into different types based on specific needs within the crypto ecosystem.

1. Executable AI Agent: Focused on completing specific tasks such as trading, portfolio management, or arbitrage, aimed at increasing operational accuracy and reducing the time required.

2. Creative AI Agent: used for content generation, including text, design, and even music creation.

3. Social AI Agent: As an opinion leader on social media, interact with users, build communities, and participate in marketing activities.

4. Coordinating AI Agent: Coordinates complex interactions between systems or participants, particularly suitable for multi-chain integration.

In this report, we will delve into the origins, current status, and broad application prospects of AI Agents, analyzing how they are reshaping industry patterns and looking ahead to their future development trends.

1.1.1 Development History

The development history of AI AGENT shows the evolution of AI from basic research to widespread application. The term "AI" was first proposed at the Dartmouth Conference in 1956, laying the foundation for AI as an independent field. During this period, AI research was mainly focused on symbolic methods, giving rise to the first AI programs, such as ELIZA(, a chatbot), and Dendral(, an expert system in the field of organic chemistry). This stage also witnessed the initial proposal of neural networks and the preliminary exploration of machine learning concepts. However, AI research during this period was severely constrained by the limitations of computing power at the time. Researchers encountered significant difficulties in natural language processing and the development of algorithms that mimic human cognitive functions. In addition, in 1972, mathematician James Lighthill submitted a report published in 1973 on the status of ongoing AI research in the UK. The Lighthill report basically expressed comprehensive pessimism about AI research after the early excitement phase, leading to a significant loss of confidence in AI among UK academic institutions(, including funding agencies). After 1973, funding for AI research was drastically reduced, and the AI field experienced its first "AI winter," with increasing skepticism about AI's potential.

In the 1980s, the development and commercialization of expert systems led global enterprises to begin adopting AI technologies. This period saw significant advancements in machine learning, neural networks, and natural language processing, driving the emergence of more complex AI applications. The introduction of autonomous vehicles and the deployment of AI across various industries, such as finance and healthcare, also marked the expansion of AI technologies. However, from the late 1980s to the early 1990s, the AI field experienced a second "AI winter" as the demand for specialized AI hardware collapsed. Additionally, how to scale AI systems and successfully integrate them into practical applications remains an ongoing challenge. Meanwhile, in 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov, marking a milestone event in AI's ability to solve complex problems. The revival of neural networks and deep learning laid the groundwork for the development of AI in the late 1990s, making AI an integral part of the technological landscape and beginning to influence everyday life.

By the beginning of this century, advances in computing power propelled the rise of deep learning, with virtual assistants like Siri demonstrating the practicality of AI in consumer applications. In the 2010s, breakthroughs in reinforcement learning agents and generative models like GPT-2 further elevated conversational AI to new heights. During this process, the emergence of large language models (Large Language Model,LLM) became a significant milestone in AI development, especially with the release of GPT-4, which is seen as a turning point in the field of AI agents. Since OpenAI launched the GPT series, large-scale pre-trained models with hundreds of billions or even trillions of parameters have demonstrated language generation and understanding capabilities that surpass traditional models. Their outstanding performance in natural language processing enables AI agents to exhibit clear logic and coherent interactions through language generation. This allows AI agents to be applied in scenarios such as chat assistants and virtual customer service, gradually extending to more complex tasks ( like business analysis and creative writing ).

The learning ability of large language models provides AI agents with greater autonomy. Through reinforcement learning (Reinforcement Learning) technology, AI agents can continuously optimize their behavior and adapt to dynamic environments. For example, in certain AI-driven platforms, AI agents can adjust their behavioral strategies based on player input, truly achieving dynamic interaction.

From the early rule-based systems to the large language models represented by GPT-4, the development history of AI agents is a continuous evolution of breaking through technological boundaries. The emergence of GPT-4 is undoubtedly a significant turning point in this journey. With further advancements in technology, AI agents will become more intelligent, contextual, and diverse. Large language models not only inject the "wisdom" soul into AI agents but also provide them with the capability for cross-domain collaboration. In the future, innovative project platforms will continue to emerge, driving the implementation and development of AI agent technology, leading to a new era of AI-driven experiences.

Working Principle 1.2

The difference between AIAGENT and traditional robots is that they can learn and adapt over time, making detailed decisions to achieve goals. They can be seen as highly skilled and continuously evolving participants in the cryptocurrency field, capable of acting independently in the digital economy.

The core of the AI AGENT lies in its "intelligence" ------ that is, simulating human or other biological intelligence behaviors through algorithms to automate the resolution of complex problems. The workflow of the AI AGENT typically follows these steps: perception, reasoning, action, learning, and adjustment.

1.2.1 Perception Module

The AI AGENT interacts with the external world through the perception module, collecting environmental information. This part of the function is similar to human senses, utilizing devices such as sensors, cameras, and microphones to capture external data, which includes extracting meaningful features, recognizing objects, or identifying relevant entities in the environment. The core task of the perception module is to transform raw data into meaningful information, which typically involves the following technologies:

• Computer Vision: Used for processing and understanding image and video data.
• Natural Language Processing ( NLP ): Helps AI AGENT understand and generate human language.
• Sensor Fusion: Integrating data from multiple sensors into a unified view.

1.2.2 Reasoning and Decision-Making Module

After perceiving the environment, the AI AGENT needs to make decisions based on the data. The reasoning and decision-making module is the "brain" of the entire system, which performs logical reasoning and strategy formulation based on the collected information. It utilizes large language models to act as orchestrators or reasoning engines, understanding tasks, generating solutions, and coordinating specialized models for specific functions such as content creation, visual processing, or recommendation systems.

This module typically uses the following technologies:

• Rule Engine: Simple decision-making based on preset rules.
• Machine learning models: including decision trees, neural networks, etc., used for complex pattern recognition and prediction.
• Reinforcement Learning: Allowing AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process typically involves several steps: first, an assessment of the environment; second, calculating multiple possible action plans based on the objective; and finally, selecting the optimal plan for execution.

1.2.3 Execution Module

The execution module is the "hands and feet" of the AI AGENT, implementing the decisions made by the reasoning module. This part interacts with external systems or devices to complete designated tasks. This may involve physical operations ( such as robotic actions ) or digital operations ( such as data processing ). The execution module relies on:

• Robotic Control System: Used for physical operations, such as the movement of robotic arms.
• API call: Interacting with external software systems, such as database queries or network service access.
• Automated Process Management: In a corporate environment, repetitive tasks are executed through RPA( Robotic Process Automation).

1.2.4 Learning Module

The learning module is the core competitive advantage of the AI AGENT, enabling the agent to become smarter over time. Continuous improvement through feedback loops or "data flywheels" feeds the data generated during interactions back into the system to enhance the model. This ability to gradually adapt and become more effective over time provides businesses with a powerful tool to enhance decision-making and operational efficiency.

Learning modules are typically improved in the following ways:

• Supervised Learning: Utilizing labeled data for model training, enabling the AI AGENT to complete tasks more accurately.
• Unsupervised Learning: Discovering underlying patterns from unlabeled data to help agents adapt to new environments.
• Continuous Learning: Update models with real-time data to maintain agent performance in dynamic environments.

1.2.5 Real-time Feedback and Adjustment

The AI AGENT optimizes its performance through continuous feedback loops. The results of each action are recorded and used to adjust future decisions. This closed-loop system ensures the adaptability and flexibility of the AI AGENT.

Market Status 1.3

1.3.1 Industry Status

AI AGENT is becoming the focus of the market, bringing transformative changes to multiple industries with its enormous potential as a consumer interface and autonomous economic agent. Just as the potential of L1 block space was difficult to measure in the last cycle, AI AGENT has also demonstrated the same prospects in this cycle.

According to the latest report from Markets and Markets, the AI Agent market is expected to grow from $5.1 billion in 2024 to $47.1 billion in 2030, with a compound annual growth rate of 44.8%. This rapid growth reflects the penetration of AI Agents across various industries and the market demand driven by technological innovation.

The investment of large companies in open-source proxy frameworks has also significantly increased. The development activities of frameworks such as AutoGen, Phidata, and LangGraph from a certain company are becoming increasingly active, which indicates that AI AGENT has greater market potential outside the cryptocurrency field, and the TAM is also