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

1. Background Overview

1.1 Introduction: "New Partners" in the Smart Era

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

• In 2017, the rise of smart contracts spurred the booming development of ICOs.
• In 2020, DEX liquidity pools 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 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 only due to technological innovation but also a perfect combination of financing models and bull market cycles. When opportunities meet the right timing, it can give rise to massive transformations. 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, reaching a market value of $150 million by October 15. Immediately after, on October 16, a certain protocol launched Luna, making its debut with the live 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, capable of autonomously perceiving the environment, analyzing data, and taking swift action.

In fact, AI Agents share many core functions with the Red Heart Queen. In reality, AI Agents play a similar role to some extent, acting as the "intelligent guardians" in the modern technology field. Through autonomous perception, analysis, and execution, they help businesses and individuals tackle complex tasks. 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 agents, like invisible team members, possess comprehensive capabilities ranging from environmental perception to decision-making execution, gradually infiltrating different sectors and driving dual improvements in 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 data platform or social platform, continuously optimizing its performance through iterations. The AI AGENT is not a single form, but is divided into different categories according to the specific needs in 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: Facilitates 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 the industry landscape and looking ahead to their 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 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 proposal of neural networks and preliminary exploration of the concept of machine learning. However, AI research during this time was severely constrained by the limitations of computing power. Researchers faced 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 status of AI research being conducted in the UK. The Lighthill report fundamentally expressed a comprehensive pessimism regarding AI research after the initial excitement phase, leading to a significant loss of confidence in AI from UK academic institutions(, including funding agencies). Following 1973, funding for AI research was drastically reduced, 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 enterprises to begin adopting AI technologies. Significant progress was made during this period in machine learning, neural networks, and natural language processing, promoting the emergence of more complex AI applications. The introduction of autonomous vehicles for the first time and the deployment of AI in 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 the demand for specialized AI hardware collapsed. Additionally, scaling AI systems and successfully integrating them into practical applications remained an ongoing challenge. Meanwhile, in 1997, a company's Deep Blue computer defeated world chess champion Garry Kasparov, marking a milestone event for AI's ability to solve complex problems. The revival of neural networks and deep learning laid the foundation for the development of AI 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 drove the rise of deep learning, with certain virtual assistants demonstrating the practicality of AI in consumer applications. In the 2010s, breakthroughs in reinforcement learning agents and certain generative models 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 the release of a certain model, which was 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, with hundreds of billions or even trillions of parameters, have showcased language generation and understanding capabilities that surpass traditional models. Their outstanding performance in natural language processing has enabled AI agents to demonstrate clear and organized interaction abilities through language generation. This allows AI agents to be applied in scenarios such as 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 ( Reinforcement Learning ) techniques, AI agents can continuously optimize their behavior and adapt to dynamic environments. For example, in certain AI-driven platforms, AI agents can adjust their behavioral strategies based on player input, achieving true dynamic interaction.

From the early rule-based systems to the large language models represented by a certain model, the development history of AI agents is a story of continuous breakthroughs in technological boundaries. The emergence of a certain model is undoubtedly a significant 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" of the 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 promoting the landing and development of AI agent technology, 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 cryptocurrency space, capable of acting independently in the digital economy.

The core of the AI AGENT lies in its "intelligence" ------ which simulates human or other biological intelligent behavior 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 world through a perception module, collecting environmental information. This part of the function is similar to human senses, using devices such as sensors, cameras, and microphones 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 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 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 and other tools as orchestrators or reasoning engines, it understands tasks, generates solutions, and coordinates specialized models used 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 typically involves several steps: first, assessing the environment; second, calculating multiple possible action plans based on the goals; and finally, selecting 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 Call: Interacting with external software systems, such as database queries or web service access.
• Automated Process Management: In a corporate environment, repetitive tasks are executed through RPA( robotic process automation).

1.2.4 Learning Module

The learning module is the core competitiveness of the AI AGENT, enabling the agent to become smarter over time. Continuous improvement through feedback loops or the "data flywheel" 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.

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 continuously optimizes its performance through a constant feedback loop. 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 last cycle, AI AGENT shows the same prospects in this cycle.

According to the latest report from a market research firm, 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 innovations.

The investment of large companies in open-source proxy frameworks has also significantly increased. The development activities of certain companies' frameworks such as AutoGen, Phidata, and LangGraph are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the crypto space, and the TAM is also expanding.