Decoding AI Agents: 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 spurred the vigorous development of ICOs.
• In 2020, the liquidity pools of DEX brought about the summer boom of DeFi.
• In 2021, a large number of NFT series were launched, marking 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 solely due to technological innovation, but rather the result of a perfect combination of financing models and bull market cycles. When opportunity meets the right timing, it can lead to significant transformation. Looking ahead to 2025, it is clear that the new emerging field of the 2025 cycle will be AI agents. This trend peaked in October last year, when a certain token was launched on October 11, 2024, and reached a market value of 150 million USD on October 15. Shortly after, on October 16, a certain protocol launched Luna, debuting with the image of a neighbor girl live streaming, 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 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 Red Queen. In reality, AI Agents play a somewhat similar role; they are the "intelligent guardians" of 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 agents, 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 and executing trades in real-time based on data collected from a data platform or social platform, continuously optimizing its performance through iterations. The AI AGENT does not exist in 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: Interacts with users as an opinion leader on social media, 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 industry dynamics 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 period was severely constrained by the limitations of computing power at the time. Researchers faced significant challenges 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 regarding the state of ongoing AI research 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 British academic institutions including funding agencies (. After 1973, funding for AI research was drastically reduced, and the field experienced the 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 technology. This period saw significant advances in machine learning, neural networks, and natural language processing, paving the way for 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 market demand for specialized AI hardware collapsed. Additionally, scaling AI systems and successfully integrating them into practical applications remained ongoing challenges. At the same time, in 1997, IBM's Deep Blue computer 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's development in the late 1990s, making it 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. In 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 a certain company launched the GPT series, large-scale pre-trained models, with hundreds of billions or even trillions of parameters, have displayed language generation and understanding capabilities that surpass traditional models. Their outstanding performance in natural language processing allows AI agents to demonstrate clear logic and coherent interaction abilities 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 behaviors 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 the large language models represented by GPT-4, the development history of AI agents is a continuous evolution that breaks through 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 diverse. Large language models not only inject "wisdom" into AI agents but also provide them with the ability for cross-domain collaboration. In the future, innovative project platforms will continue to emerge, driving the implementation and development of AI agent technology, leading to a new era of AI-driven experiences.

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) Working Principle 1.2

The difference between AIAGENT and traditional robots is that they can learn and adapt over time, making meticulous decisions to achieve their goals. They can be seen as highly skilled and continuously evolving participants in the field of cryptocurrencies, capable of operating 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 the 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 identifying relevant entities in the environment. The core task of the perception module is to convert 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 to understand tasks, generate solutions, and coordinate 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: 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, assessing the environment, then 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 calls: Interaction with external software systems, such as database queries or network service access.
• Automated Process Management: In a corporate environment, repetitive tasks are performed 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 more intelligent over time. Continuous improvement through feedback loops or "data flywheels" feeds the data generated during interactions back into the system to enhance the model. This ability to adapt gradually over time and become more effective provides businesses with a powerful tool to enhance decision-making and operational efficiency.

Learning modules are usually improved in the following ways:

• Supervised Learning: Using labeled data for model training so that the AI AGENT can complete tasks more accurately.
• Unsupervised Learning: Discovering underlying patterns from unlabelled data to help agents adapt to new environments.
• Continuous Learning: Update the model with real-time data to maintain the agent's performance in a dynamic environment.

1.2.5 Real-time Feedback and Adjustment

The AI AGENT optimizes its performance through a continuous feedback loop. 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.

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) Market Status 1.3

1.3.1 Industry Status

AI AGENT is becoming the focus of the market, bringing transformation to multiple industries with its enormous potential as a consumer interface and autonomous economic agent. Just as the potential of L1 blockchain space in the previous cycle was hard to estimate, AI AGENT has also shown the same prospects in this cycle.

According to a recent report from a research company, 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 innovation.

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 a larger market beyond the cryptocurrency field.