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

1. Background Overview

1.1 Introduction: The "New Partners" of the Intelligent 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, 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 craze for memecoins and launch platforms will rise.

It should be emphasized that the emergence of these vertical fields is not only due to technological innovation, but also the perfect combination of financing models and bull market cycles. When opportunity meets the right timing, it can lead to tremendous changes. Looking ahead to 2025, it is clear that the emerging field of the 2025 cycle will be AI agents. This trend peaked last October, and on October 11, 2024, the $GOAT token was launched, reaching a market value of $150 million by October 15. Shortly after, on October 16, Virtuals Protocol launched Luna, debuting with the image of the girl next door in a live broadcast, 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 perceiving the environment, analyzing data, and taking swift action.

In fact, AI Agents and the core functions of the Red Queen have many similarities. In reality, AI Agents play a somewhat similar role; they are the "smart 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 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 Dexscreener or the social platform X, 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: 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, 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 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 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 mainly focused on symbolic methods, giving rise to 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 preliminary exploration of the concept of machine learning. However, AI research during this time was severely constrained by the limitations of computing power at that time. Researchers faced significant difficulties in natural language processing and in the development of algorithms that mimic human cognitive functions. Additionally, in 1972, mathematician James Lighthill submitted a report published in 1973 on the status of ongoing AI research in the UK. The Lighthill report essentially expressed a comprehensive pessimism about AI research after the initial excitement phase, leading to a significant loss of confidence in AI from UK academic institutions(, including funding agencies). After 1973, funding for AI research was drastically reduced, and the field of AI 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 advancements were made in machine learning, neural networks, and natural language processing during this period, promoting the emergence of more complex AI applications. The introduction of autonomous vehicles 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 market demand for specialized AI hardware collapsed. Additionally, scaling AI systems and successfully integrating them into practical applications remained a persistent 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.

At 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 such as GPT-2 further elevated conversational AI to new heights. In this process, the emergence of large language models (Large Language Model, LLM) became a significant 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 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 logic 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 ( Reinforcement Learning ) technology, AI agents can continuously optimize their behavior and adapt to dynamic environments. For example, in AI-driven platforms like Digimon Engine, AI agents can adjust their behavioral strategies based on player input, truly achieving dynamic interaction.

From the early rule-based systems to large language models represented by GPT-4, the development history of AI agents 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 advancements in technology, AI agents will become more intelligent, contextual, and diversified. 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 to a new era of AI-driven experiences.

Working Principle 1.2

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 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 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 its perception module, collecting environmental information. This part of its 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 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 ): Helping 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, conducting 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: Make simple decisions based on predefined rules.
• Machine Learning Models: including decision trees, neural networks, etc., used for complex pattern recognition and prediction.
• Reinforcement Learning: Allow AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

The reasoning process usually involves several steps: first, evaluating the environment; second, calculating multiple possible action plans based on the objectives; 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 made by the reasoning module into action. This part interacts with external systems or devices to complete assigned 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: Interact with external software systems, such as database queries or web service access.
• Automated Process Management: In a business 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 data generated from interactions back into the system to enhance the model. This capability 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, allowing 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

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 adaptability and flexibility of the AI AGENT.

Market Status 1.3

1.3.1 Industry Status

AI AGENT is becoming the focus of the market, with its tremendous potential as a consumer interface and an autonomous economic actor, bringing transformation to multiple industries. Just as the potential of L1 block space was hard to measure 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 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 frameworks such as Microsoft's AutoGen, Phidata, and LangGraph are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the cryptocurrency sector, and the TAM is also expanding.