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

1. Background Overview

1.1 Introduction: "New Partners" in the Smart Era

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

• In 2017, the rise of smart contracts gave birth to the flourishing 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 digital collectibles era.
• 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 result of the perfect combination of financing models and bull market cycles. When opportunity meets the right timing, it can lead to tremendous transformation. Looking ahead to 2025, it is clear that the emerging field of the 2025 cycle will be AI agents. This trend peaked in October last year, with the launch of a certain token on October 11, 2024, reaching a market value of $150 million by October 15. Following that, on October 16, a certain protocol launched Luna, debuting with the IP live broadcast image of the girl next door, igniting the entire industry.

So, what exactly is an AI Agent?

Everyone must be 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 have many similarities to 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 for enhancing efficiency and innovation. These autonomous intelligences, like invisible team members, possess comprehensive abilities 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 and executing trades in real-time based on data collected from data platforms or social platforms, continuously optimizing its performance through iterations. AI AGENT is not a single form but is categorized into different types according to the specific needs in the cryptocurrency ecosystem:

1. Execution-type 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. Coordinated 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 broad application prospects of AI Agents, analyzing how they are reshaping the industry landscape and forecasting 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 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, which led to the creation of 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 preliminary exploration of machine learning concepts. However, AI research during this period was severely constrained by the limitations of computing power at that time. Researchers faced great difficulties in the development of algorithms for natural language processing and mimicking human cognitive functions. Furthermore, in 1972, mathematician James Lighthill submitted a report published in 1973 regarding the state of 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 drastically decreased, and the AI 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 companies to adopt AI technology. This period saw significant advancements 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 AI field experienced a second "AI winter" as the market for dedicated AI hardware collapsed. Additionally, 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 event in AI's capability 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 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 in reinforcement learning agents and generative models like GPT-2 pushed conversational AI to new heights. During 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 regarded 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 logic and organized interaction abilities through language generation. This enables AI agents to be applied in scenarios such as chat assistants and virtual customer service, gradually extending 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 a history of constantly breaking through technological boundaries. The emergence of GPT-4 is undoubtedly a major turning point in this journey. With further technological advancements, AI agents will become more intelligent, scenario-based, and diverse. Large language models not only inject the "wisdom" soul into AI agents but also provide them with the ability to collaborate across fields. In the future, innovative project platforms will continue to emerge, further promoting the implementation and development of AI agent technology, leading 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 viewed as highly skilled and continuously evolving participants in the crypto space, capable of operating 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 resolution of complex problems. The workflow of an AI AGENT typically follows these steps: perception, reasoning, action, learning, and adjustment.

1.2.1 Perception Module

The AI AGENT interacts with the outside world through the perception module, collecting environmental information. This part of the function is similar to human senses, utilizing sensors, cameras, microphones, and other devices to capture external data, which includes extracting meaningful features, identifying objects, or determining 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 ): 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. By 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 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: Allowing AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process typically involves several steps: first is the assessment of the environment, followed by calculating multiple possible action plans based on the goals, 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: Interacting with external software systems, such as database queries or network service access.
• Automated Process Management: In an enterprise environment, repetitive tasks are executed through RPA( robotic 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. Through a feedback loop or "data flywheel" for continuous improvement, the data generated from 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 improve decision-making and operational efficiency.

Learning modules are 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: Update the model with real-time data to maintain agent performance in dynamic environments.

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 AI AGENT's adaptability and flexibility.

1.3 Market Status

1.3.1 Industry Status

AI AGENT is becoming the focal point of the market, with its immense potential as a consumer interface and autonomous economic agent, bringing transformation to multiple industries. 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 in a certain company are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the crypto space, and the TAM is also.