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 led to the booming development of ICOs.
• In 2020, the liquidity pools of DEX ushered in 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 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 a perfect combination of financing models and bull market cycles. When opportunity meets the right timing, it can trigger significant changes. Looking ahead to 2025, it is clear that the emerging field for 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 after, on October 16, a certain protocol launched Luna, which debuted with the IP live streaming image of a neighbor girl, igniting the entire industry.

So, what exactly is an AI Agent?

Everyone must be familiar with the classic movie "Resident Evil", in which the AI system known as Red Queen leaves a deep impression. 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 actions.

In fact, AI Agents share many similarities with the core functions of the Red Queen. AI Agents in the real world play a similar role to some extent; they are the "smart 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 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 data platform or social platform, continuously optimizing its performance through iterations. The AI AGENT is not a single form, but is categorized into different types based on 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: Interacting with users as an opinion leader on social media, building communities, and participating 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 broad application prospects of AI Agents, analyzing how they are reshaping industry patterns and looking ahead to their future development trends.

1.1.1 Development History

The development history of AI AGENT showcases 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 introduction of neural networks and the preliminary exploration of machine learning concepts. However, AI research during this period was severely constrained by the computational capabilities of the time. Researchers faced significant difficulties 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 state of ongoing AI research in the UK. The Lighthill report fundamentally expressed a comprehensive pessimism about AI research following the early excitement period, triggering a huge loss of confidence in AI from British academic institutions (, including funding agencies ). After 1973, funding for AI research was significantly reduced, leading the field to experience 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, which propelled 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 technology. However, from the late 1980s to the early 1990s, the AI field experienced a second "AI winter" as the demand for dedicated AI hardware collapsed. Furthermore, how to scale AI systems and successfully integrate them into practical applications remains 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 groundwork 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 drove the rise of deep learning, with virtual assistants like Siri demonstrating the practicality of AI in consumer applications. In the 2010s, breakthroughs were made with reinforcement learning agents and generative models like GPT-2, elevating conversational AI to new heights. During this process, the emergence of Large Language Models (LLM) became a significant 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 the launch 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 allows AI agents to demonstrate 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 like 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 behavioral strategies based on player inputs, truly achieving dynamic interaction.

From the early rule-based systems to large language models represented by GPT-4, the history of AI agent development is a story of continuous breakthroughs in 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, scenario-based, and diverse. Large language models not only inject the "wisdom" soul into AI agents but also provide them with the capability 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.

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 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 an 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's function is similar to human senses, utilizing devices such as sensors, cameras, and microphones 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 transform 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 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: 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, an assessment of the environment; second, 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 call: Interaction 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 competency of the AI AGENT, enabling the agent to become smarter over time. Continuous improvement through feedback loops or "data flywheels" feeds the 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 latent 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

The AI AGENT continuously optimizes its performance through a 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 focus 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 is also showing the same prospects in this cycle.

According to the latest report from a research institution, the AI Agent market is expected to grow from 5.1 billion USD in 2024 to 47.1 billion USD in 2030, with a compound annual growth rate (CAGR) as high as 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. Some