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

1. Background Overview

1.1 Introduction: "New Partner" 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 gave birth to the booming development of ICOs.
• In 2020, the liquidity pool of DEX brought about the summer boom 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 merely due to technological innovation, but rather the result of a perfect combination of financing models and bull market cycles. When opportunities meet the right timing, they can lead to significant 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, with a certain token launching on October 11, 2024, and reaching a market value of 150 million USD on October 15. Subsequently, on October 16, a certain protocol launched Luna, debuting with the IP live streaming image of a girl-next-door, 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 Red Queen leaves a deep impression. 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 actions.

In fact, AI Agents share many similarities with the core functions of the Queen of Hearts. In reality, AI Agents play a somewhat similar role, acting as the "intelligent guardians" in the field 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 infiltrated 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 execution, gradually permeating various sectors and driving a 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 iteration. AI AGENT is not a single form but is categorized into different types based on 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: Acts as an opinion leader on social media, interacts with users, builds communities, and participates 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 reshape 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, 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 proposal of neural networks and the preliminary exploration of machine learning concepts. However, AI research during this time was severely constrained by the limitations of computing power. Researchers faced significant difficulties in natural language processing and the development of algorithms mimicking human cognitive functions. In addition, in 1972, mathematician James Lighthill submitted a report on the status of ongoing AI research in the UK, published in 1973. The Lighthill report essentially expressed a comprehensive pessimism about AI research following the early excitement phase, leading to a significant loss of confidence in AI among UK academic institutions (, including funding agencies ). After 1973, funding for AI research was drastically reduced, and the 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. This period saw significant advances in machine learning, neural networks, and natural language processing, driving 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 technologies. However, from the late 1980s to the early 1990s, the AI field experienced a second "AI winter" as the market's demand for dedicated AI hardware collapsed. Furthermore, scaling AI systems and successfully integrating them into practical applications remained a continuous 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 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 early 21st century, advances in computing power had driven the rise of deep learning, with virtual assistants such as 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 (LLMs) 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 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, utilizing hundreds of billions or even trillions of parameters. Their outstanding performance in natural language processing has enabled AI agents to demonstrate clear and organized interaction capabilities through language generation. This has allowed 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 capability 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 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 story of constantly breaking through technological boundaries. The emergence of GPT-4 is undoubtedly a significant turning point in this journey. With the further advancement of technology, AI agents will become more intelligent, contextualized, 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, further promoting the implementation 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 constantly evolving participants in the crypto space, capable of acting independently in the digital economy.

The core of the AI AGENT lies in its "intelligence"------that is, simulating the intelligent behavior of humans or other organisms through algorithms to automate the resolution of complex problems. The workflow of the 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 a perception module, collecting environmental information. This part of the function is similar to human senses, using sensors, cameras, microphones, and other devices 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 convert 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 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, 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 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 movements) 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, executing repetitive tasks through RPA (Robotic Process Automation).

1.2.4 Learning Module

The learning module is the core competitive advantage of the AI AGENT, enabling the agent to become smarter over time. Through continuous improvement via feedback loops or "data flywheels," 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 enhance decision-making and operational efficiency.

The learning module is 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 unlabelled 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 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 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 hard to measure in the last cycle, AI AGENT has also demonstrated the same prospects in this cycle.

According to the latest report by 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 (CAGR) of up to 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 AutoGen, Phidata, and LangGraph from a certain company are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the cryptocurrency space.