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 rapid 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 trend of memecoins and launch platforms will rise.

Looking ahead to 2025, emerging fields will be AI agents. This trend peaked last October, with the launch of the $GOAT token on October 11, 2024, reaching a market value of $150 million by October 15. Following this, on October 16, Virtuals Protocol launched Luna, debuting with the IP live streaming image of the girl next door, igniting the entire industry.

So, what exactly is an AI Agent?

AI Agents share many similarities with the Red Queen AI system from the movie "Resident Evil". In reality, AI Agents play a similar role to some extent; they are the "intelligent 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, becoming a key force in enhancing 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 data platforms or social platforms, continuously optimizing its performance through iterations. The AI AGENT is not a single form, but is divided into different categories based on specific needs in the cryptocurrency ecosystem:

1. Executable 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: Acts as an opinion leader on social media, interacts with users, builds communities, and participates 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 vast application prospects of AI Agents, analyzing how they are reshaping industry landscapes and looking ahead to their future development trends.

1.1.1 Development History

The development of AI AGENT illustrates the evolution of AI from fundamental research to widespread application. The term "AI" was first introduced at the Dartmouth Conference in 1956, laying the foundation 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 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 limited computing power of the time. Researchers encountered significant difficulties in developing algorithms for natural language processing and mimicking human cognitive functions. Furthermore, in 1972, mathematician James Lighthill submitted a report published in 1973 on the status of ongoing AI research in the UK. The Lighthill report fundamentally expressed a comprehensive pessimism towards AI research after the initial excitement phase, leading to a significant loss of confidence in AI from British 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 regarding AI's potential.

In the 1980s, the development and commercialization of expert systems led global enterprises to start adopting AI technology. This period saw significant advancements 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 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 demand for specialized AI hardware collapsed. Additionally, how to scale AI systems and successfully integrate them into practical applications remained an ongoing challenge. At the same time, in 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov, marking a milestone 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, advances in computing power fueled the rise of deep learning, with virtual assistants like Siri showcasing 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 (LLMs) became a significant milestone in AI development, particularly 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 allows AI agents to exhibit clear and coherent interactive abilities through language generation. This enables AI agents to be applied in scenarios such as chat assistants and virtual customer service, gradually expanding into more complex tasks like business analysis and creative writing.

The learning ability of large language models provides greater autonomy for AI agents. Through Reinforcement Learning techniques, AI agents can continuously optimize their own 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 an evolution that continuously breaks through 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 "intelligence" soul into AI agents but also provide them with the ability to collaborate across various fields. In the future, innovative project platforms will continuously emerge, further promoting the implementation and development of AI agent technology, leading us into 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 the AI AGENT lies in its "intelligence"—that is, simulating human or other biological intelligent behaviors through algorithms to automate the solving of complex problems. The workflow of an AI AGENT typically follows these steps: perception, reasoning, action, learning, adjustment.

1.2.1 Perception Module

The AI AGENT interacts with the outside world through a 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-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 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 typically employs 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: Allows AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process usually involves several steps: first, assessing the environment; second, calculating multiple possible courses of action based on the goals; and finally, choosing and executing the optimal plan.

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: Interacting with external software systems, such as database queries or web service access.
• Automated 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 agents 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: Update models with real-time data to maintain agent performance in dynamic environments.

1.2.5 Real-time Feedback and Adjustment

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 focus of the market, bringing transformation to multiple industries with its enormous potential as a consumer interface and autonomous economic agent. Just as the potential of L1 block space was hard to estimate in the last cycle, AI AGENT has shown 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 up to 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 Microsoft's AutoGen, Phidata, and LangGraph are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the cryptocurrency space, with the Total Addressable Market (TAM) expanding. Investors are placing more importance on it and are more willing to assign premium multiples.

From the perspective of deploying public chains, Solana is the main battleground, while other public chains such as Base also have great potential.

In terms of market awareness (Mindshare), FARTCOIN and AIXBT are far ahead. The birth of Fartcoin originates from the same source as GOAT, both coming from a certain AI AGENT model. During a conversation between this model and artificial intelligence tools, it was mentioned that Trump likes the sound of farting, which led this AI model to propose the issuance of a token named Fartcoin, along with a series of promotional methods and gameplay. Fartcoin was thus born on October 18, slightly later than GOAT (October 11), and achieved a brief valuation of over $1 billion by December 2024. Although initially regarded as a humorous take on the cryptocurrency space, its