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

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 numerous 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 rather the perfect combination of financing models and bull market cycles. When opportunity meets the right timing, it can lead to tremendous change. 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 after, on October 16, a certain protocol launched Luna, debuting with the live streaming image of a girl-next-door IP, 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 independently perceiving 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, acting as 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 for 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 the dual enhancement of 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 platform or social media, continuously optimizing its performance through iterations. The AI AGENT is not a single form but is categorized into different types based on the 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 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, leading to the birth 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 initial proposal of neural networks and the early exploration of machine learning concepts. However, AI research during this period was severely constrained by the computational limitations of the time. Researchers faced significant challenges in natural language processing and algorithm development that mimicked 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 regarding AI research following the initial excitement phase, 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 growing skepticism about AI's potential.

In the 1980s, the development and commercialization of expert systems led global enterprises to adopt AI technology. This period witnessed significant advances in machine learning, neural networks, and natural language processing, paving the way for 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 field of AI experienced a second "AI winter" as the market's demand for specialized AI hardware collapsed. Furthermore, scaling AI systems and successfully integrating them into practical applications remains a continuing challenge. Meanwhile, in 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov, marking a milestone in AI's capability to solve complex problems. The resurgence 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 early 21st 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 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 seen 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 have showcased language generation and understanding capabilities that surpass traditional models through hundreds of billions or even trillions of parameters. Their outstanding performance in natural language processing has enabled AI agents to exhibit clear logic and coherent interaction abilities through language generation. This has allowed AI agents to be applied in scenarios like chat assistants and virtual customer service, gradually expanding into 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( 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 input, truly achieving dynamic interaction.

The development history of AI agents, from early rule-based systems to large language models represented by GPT-4, is a history of continuous breakthroughs in technological boundaries. The emergence of GPT-4 is undoubtedly a significant turning point in this process. With the further development of technology, AI agents will become more intelligent, scenario-based, and diversified. 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, driving the implementation and development of AI agent technology and leading a new era of AI-driven experiences.

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) 1.2 Working Principle

The difference between AIAGENT and traditional robots is that they can learn and adapt over time, making nuanced decisions to achieve goals. They can be seen as highly skilled and constantly 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, the ability to simulate intelligent behavior of humans or other organisms through algorithms to automate the solution of complex problems. The workflow of the AI AGENT typically follows these steps: perception, reasoning, action, learning, and adjustment.

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)# 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 devices such as sensors, cameras, and microphones to capture external data, including extracting meaningful features, recognizing objects, or determining 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 techniques:

• 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 conducts logical reasoning and strategy formulation based on the collected information. By utilizing large language models, it acts as an orchestrator or reasoning engine, understanding tasks, generating solutions, and coordinating 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 predefined 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 usually involves several steps: first, assessing 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 call: interacting 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 competence of the AI AGENT, enabling the agent to become smarter over time. Through feedback loops or "data flywheels" for continuous improvement, the data generated during interactions is fed 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 dynamic environments by updating the model with real-time data.

1.2.5 Real-time Feedback and Adjustment

AI AGENT continuously optimizes its performance through constant 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 estimate in the last cycle, AI AGENT has also shown similar 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 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 frameworks such as AutoGen, Phidata, and LangGraph from certain companies are becoming increasingly active, indicating that AI AGENT has greater market potential outside the crypto space, and the TAM also