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

1. Background Overview

1.1 Introduction: "New Partners" in the Intelligent Era

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

• In 2017, the rise of smart contracts spurred the vigorous development of ICOs.
• In 2020, the liquidity pools of DEX brought about the summer craze 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 craze for memecoins and launch platforms.

It is important to emphasize that the emergence of these vertical fields is not solely due to technological innovation, but rather a perfect combination of financing models and bull market cycles. When opportunities meet the right timing, it can lead to significant transformations. Looking ahead to 2025, it is clear that the emerging field for 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 after, on October 16, a certain protocol launched Luna, making its debut with the live streaming image of a girl next door, igniting the entire industry.

So, what exactly is an AI Agent?

Everyone is certainly familiar with the classic movie "Resident Evil", in which 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 share many core functions with the Red Queen. In reality, AI Agents play a similar role to some extent; they are the "wise guardians" of modern technology, 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, becoming a key force for efficiency and innovation. These autonomous intelligences, like invisible team members, possess comprehensive capabilities from environmental perception to decision execution, gradually infiltrating various sectors and driving dual improvements in efficiency and innovation.

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

1. Execution AI Agent: Focused on completing specific tasks, such as trading, portfolio management, or arbitrage, aimed at improving 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, interacts with users, builds community, and engages 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 explore the origins, current status, and broad application prospects of AI Agents, analyze how they are reshaping the industry landscape, and look forward to their future development trends.

1.1.1 Development History

The development of AI AGENT shows the evolution of AI from basic research to widespread application. The term "AI" was first introduced at the Dartmouth Conference in 1956, laying the groundwork for AI as an independent field. During this period, AI research primarily 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 first proposal of neural networks and the initial exploration of machine learning concepts. However, AI research during this time was severely constrained by the limited computing power available. Researchers faced significant difficulties in developing algorithms for natural language processing and mimicking human cognitive functions. Furthermore, in 1972, mathematician James Lighthill submitted a report on the status of ongoing AI research in the UK, published in 1973. The Lighthill report fundamentally expressed a comprehensive pessimism about AI research after the initial excitement period, leading to a significant loss of confidence among UK academic institutions(, including funding agencies), in AI. After 1973, funding for AI research was greatly reduced, and the 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 technology. This period saw significant progress in machine learning, neural networks, and natural language processing, paving the way for the emergence of more complex AI applications. The introduction of autonomous vehicles and the deployment of AI in various industries such as finance and healthcare also marked the expansion of AI technology. However, from the late 1980s to the early 1990s, with the collapse of market demand for specialized AI hardware, the AI field experienced a second "AI winter." Furthermore, scaling AI systems and successfully integrating them into practical applications remain ongoing challenges. At the same time, in 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov, marking a milestone event for AI's capability in solving complex problems. The revival of neural networks and deep learning laid the foundation for the development of AI in the late 1990s, making AI an indispensable part of the technological landscape and beginning to influence everyday life.

By the beginning of this century, advancements 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 were made with reinforcement learning agents and generative models like GPT-2, taking conversational AI to new heights. Throughout 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 a certain company 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 allows AI agents to exhibit clear and logical interaction capabilities through language generation. This enables AI agents to be applied in scenarios such as chat assistants and virtual customer service, gradually expanding 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 a certain AI-driven platform, AI agents can adjust their behavior strategies based on player inputs, 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 story of continuous breakthroughs in 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 ability for cross-domain collaboration. In the future, innovative project platforms will continue to emerge, further promoting the implementation and development of AI agent technology, leading a new era of AI-driven experiences.

1.2 Working Principle

The difference between AIAGENT and traditional robots lies in their ability to learn and adapt over time, making nuanced decisions to achieve goals. They can be seen as highly skilled and continuously evolving participants in the crypto space, capable of acting independently in the digital economy.

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

1.2.1 Perception Module

The AI AGENT interacts with the external world through a perception module, collecting environmental information. This part of the functionality is similar to human senses, utilizing sensors, cameras, microphones, and other devices 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 often involves the following technologies:

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

1.2.2 Inference 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 conducts 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 usually adopts 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: Allow AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process usually involves several steps: first, an assessment of the environment; second, calculating multiple possible action plans based on the objectives; 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, putting the decisions of the reasoning module into action. This part interacts with external systems or devices to complete specified tasks. This may involve physical operations ( such as robotic actions ) or digital operations ( such as data processing ). The execution module relies on:

• Robot Control System: Used for physical operations, such as the movement of robotic arms.
• API calls: Interact with external software systems, such as database queries or web 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 competence of the AI AGENT, enabling the agent to become smarter over time. Continuous improvement through feedback loops or "data flywheels" feeds data generated from 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: Using 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: Keep the agent's performance in a dynamic environment by updating the model with real-time data.

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 transformation to multiple industries with its tremendous potential as a consumer interface and autonomous economic actor. Just as the potential of L1 block space in the last cycle was difficult to quantify, AI AGENT has 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.

Large companies have also significantly increased their investment in open-source proxy frameworks. The development activities of certain companies' frameworks such as AutoGen, Phidata, and LangGraph are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the crypto space, and the TAM is also