Decoding 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 entire industry forward.

• In 2017, the rise of smart contracts spurred the thriving development of ICOs.
• In 2020, the liquidity pools of DEX brought about the summer boom of DeFi.
• In 2021, the emergence of numerous NFT series marked the arrival of the era of digital collectibles.
• In 2024, the outstanding performance of a certain launch platform led the wave 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 rather the perfect combination of financing models and bull market cycles. When opportunities meet the right timing, they can lead to tremendous changes. 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 USD by October 15. Shortly thereafter, on October 16, a certain protocol launched Luna, making its debut with the image of the girl next door in a live broadcast, igniting the entire industry.

So, what exactly is an AI Agent?

Everyone is surely familiar with the classic movie "Resident Evil", and the AI system Red Queen is particularly impressive. The Red Queen is a powerful AI system that controls complex facilities and security systems, capable of autonomously sensing the environment, analyzing data, and taking swift action.

In fact, AI Agents share many similarities with the core functions of the Red Queen. In reality, AI Agents play a somewhat similar role; they are the "guardians of wisdom" 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 permeated various industries, becoming a key force for enhancing 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 a dual enhancement of 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, constantly optimizing its performance through iterations. The AI AGENT is not a single form, but is categorized into different types according to 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, especially suitable for multi-chain integration.

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

1.1.1 Development History

The development of AI AGENT showcases the evolution of AI from basic research to widespread applications. 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, leading to the creation of the first AI programs, such as ELIZA(, a chatbot), and Dendral(, an expert system in the field of organic chemistry). This phase also witnessed the initial introduction of neural networks and the early exploration of machine learning concepts. However, AI research during this time was severely constrained by the limitations of contemporary computing power. Researchers faced significant challenges in natural language processing and the development of algorithms that mimic human cognitive functions. Additionally, in 1972, mathematician James Lighthill submitted a report published in 1973 regarding the status of ongoing AI research in the UK. The Lighthill report fundamentally expressed a comprehensive pessimism towards AI research after the initial excitement period, leading to a significant loss of confidence in AI among UK academic institutions(, including funding bodies). After 1973, funding for AI research was drastically 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. Significant advancements were made in machine learning, neural networks, and natural language processing during this period, paving the way for the emergence of more complex AI applications. The introduction of autonomous vehicles for the first time and the deployment of AI across various industries such as finance and healthcare also marked the expansion of AI technology. However, from the late 1980s to the early 1990s, the AI field experienced a second "AI winter" as the market demand for specialized AI hardware collapsed. Additionally, how to scale AI systems and successfully integrate them into practical applications remained a persistent challenge. At the same time, 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 foundation for AI development in the late 1990s, making AI an indispensable part of the technological landscape and beginning to influence daily life.

By the beginning of this century, advancements in computing power drove 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 pushed 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 viewed as a turning point in the field of AI agents. Since the release of the GPT series by a certain company, 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 coherent 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 ( such as business analysis and creative writing ).

The learning capability 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 interaction.

From the early rule-based systems to the large language models represented by GPT-4, the development history of AI agents is an evolutionary history of continuously breaking technological boundaries. The emergence of GPT-4 is undoubtedly a significant turning point in this journey. With further technological advancements, 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 driving the implementation and development of AI agent technology, leading us into a new era of AI-driven experiences.

Working Principle 1.2

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 viewed as highly skilled and continually evolving participants in the cryptocurrency space, 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 intelligent behaviors through algorithms to automate the solution 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 environment through its perception module, collecting environmental information. This part of the function is similar to human senses, using 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 convert 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 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 as orchestrators or reasoning engines to understand tasks, generate solutions, and coordinate 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: Allow AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process usually includes several steps: first is the assessment of the environment, second is calculating multiple possible action plans based on the goal, 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 call: Interacting with external software systems, such as database queries or network service access.
• Automation Process Management: In a corporate environment, repetitive tasks are performed 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 in 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 perform 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.

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 actor. Just as the potential of L1 block space was difficult to estimate in the previous cycle, AI AGENT has also shown the same prospects in this cycle.

According to the latest report from a certain agency, 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 are also significantly increasing 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 beyond the cryptocurrency space, and the TAM is also