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

1. Background Overview

1.1 Introduction: "New Partner" of 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 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 launch platform led the wave of memecoins and launch platforms.

It should be emphasized that the emergence of these vertical fields is not merely due to technological innovation, but rather a perfect combination of financing models and bull market cycles. When opportunities meet the right timing, they can lead to significant transformations. Looking ahead to 2025, it is evident that the emerging field in 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 cap of 150 million USD by October 15. Following that, on October 16, a certain protocol launched Luna, making its debut with the live streaming image of the girl-next-door 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 is particularly impressive. The Red Queen is a powerful AI system that controls complex facilities and security systems, able to autonomously sense the environment, analyze data, and take swift action.

In fact, AI Agents have many similarities with the core functions of the Red Queen. In reality, AI Agents play a similar role to some extent; they are the "intelligent 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 in enhancing efficiency and innovation. These autonomous intelligent entities, like invisible team members, possess comprehensive capabilities from environmental perception to decision execution, gradually infiltrating various sectors and driving a dual increase 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 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, 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 the industry landscape and looking ahead to their future development trends.

1.1.1 Development History

The development history of AI AGENT demonstrates 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 mainly focused on symbolic methods, leading to the emergence of 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 the concept of machine learning. However, AI research during this period was severely constrained by the limitations of computing power at the time. Researchers encountered significant difficulties in the development of algorithms for natural language processing and mimicking human cognitive functions. Additionally, in 1972, mathematician James Lighthill submitted a report published in 1973 on the state of ongoing AI research in the UK. The Lighthill report basically expressed a comprehensive pessimism about AI research after the early excitement phase, leading to a significant loss of confidence in AI from 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 increased skepticism about AI's potential.

In the 1980s, the development and commercialization of expert systems led global enterprises to start adopting AI technologies. Significant progress was made during this period 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 market demand for specialized AI hardware collapsed. Additionally, scaling AI systems and successfully integrating them into practical applications remained an ongoing challenge. Nevertheless, in 1997, IBM's Deep Blue computer 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 foundation for AI development in the late 1990s, making AI an indispensable part of the technological landscape and starting to influence daily life.

By the beginning of this century, advancements in computing power propelled the rise of deep learning, with virtual assistants like Siri showcasing the practicality of AI in consumer applications. In the 2010s, reinforcement learning agents and generative models like GPT-2 achieved further breakthroughs, pushing conversational AI to new heights. In this process, the emergence of large language models (Large Language Model, LLM ) became an important 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 released 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 and organized interaction capabilities through language generation. This allows AI agents to be applied in scenarios such as chat assistants and virtual customer service, gradually expanding to more complex tasks ( such as 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 behavioral strategies based on player input, truly achieving dynamic interactions.

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 that breaks through technological boundaries. The emergence of GPT-4 is undoubtedly a significant turning point in this process. With further technological advancements, AI agents will become more intelligent, contextual, and diverse. Large language models not only inject the "wisdom" of the 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 and leading to 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 realm of cryptocurrency, capable of acting independently within the digital economy.

The core of the AI AGENT lies in its "intelligence"------that is, simulating human or other biological intelligent behaviors through algorithms to automate the resolution of complex problems. The workflow of the 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, using sensors, cameras, microphones, and other devices to capture external data, which includes extracting meaningful features, recognizing objects, or determining 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 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. Utilizing large language models to act as orchestrators or reasoning engines, it understands tasks, generates solutions, and coordinates 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: Allowing the AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process typically involves several steps: first, there is an assessment of the environment; second, multiple possible action plans are calculated based on the goals; finally, the optimal plan is chosen 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 designated 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 network service access.
• Automated Process Management: In a corporate environment, repetitive tasks are performed through RPA( robot process automation).

1.2.4 Learning Module

The learning module is the core competency 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.

The learning module is usually 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 dynamic environments 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.

1.3 Market Status

1.3.1 Industry Status

AI AGENT is becoming the focal point of the market, bringing transformation to multiple industries with its immense potential as a consumer interface and autonomous economic agent. Just as the potential of L1 block space was hard to quantify in the previous 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 by 2030, with a compound annual growth rate (CAGR) of 44.8%. This rapid growth reflects the penetration of AI Agents across various industries and the market demand driven by technological innovations.

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