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

1. Background Overview

1.1 Introduction: "New Partners" 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 led to the booming development of ICOs.
• In 2020, the liquidity pools of DEX brought about the summer wave 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 should be emphasized that the emergence of these vertical fields is not only due to technological innovation but also the result of a perfect combination of financing models and bull market cycles. When opportunities meet the right timing, it can lead to significant changes. Looking ahead to 2025, it is clear that the 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 cap of $150 million by October 15. Shortly after, on October 16, a certain protocol launched Luna, marking its debut with the image of a neighborhood girl live streaming, igniting the entire industry.

So, what exactly is an AI Agent?

Everyone is certainly familiar with the classic movie "Resident Evil," and 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 action.

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 modern technology, helping businesses and individuals tackle complex tasks through autonomous perception, analysis, and execution. From self-driving cars to smart customer service, AI Agents have penetrated various industries, becoming a key force in enhancing efficiency and innovation. These autonomous intelligences, like invisible team members, possess comprehensive capabilities ranging from environmental perception to decision execution, gradually infiltrating various sectors and driving a 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 crypto ecosystem:

1. Execution AI Agent: Focuses 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 engages 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 are reshaping the industry landscape and looking ahead to their future development trends.

1.1.1 Development History

The development of AI AGENT illustrates 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 the concept of machine learning. However, AI research during this period was severely constrained by the limited computing power of the time. Researchers faced significant difficulties in natural language processing and algorithm development for mimicking 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 fundamentally expressed a comprehensive pessimism regarding AI research after the early 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 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 technologies. Significant progress was made during this period 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 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 its second "AI winter" as the demand for specialized AI hardware collapsed. Additionally, how to scale AI systems and successfully integrate them into real-world applications remains a persistent challenge. Nevertheless, in 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov, marking a milestone event in AI's capacity to solve complex problems. The revival of neural networks and deep learning laid the foundation for AI development at the end of the 1990s, making AI an indispensable part of the technological landscape and beginning to influence everyday 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 in reinforcement learning agents and generative models like GPT-2 further elevated conversational AI to new heights. During 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 regarded 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 demonstrated language generation and understanding capabilities that surpass traditional models, utilizing hundreds of billions or even trillions of parameters. Their exceptional performance in natural language processing allows AI agents to exhibit clear and coherent interactive 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 behavior 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 continuous evolution that breaks through 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, contextual, and diverse. 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, driving the implementation and development of AI agent technology and leading a new era of AI-driven experiences.

1.2 Working Principle

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 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 the intelligent behavior of humans or other organisms 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 outside world through a perception module, collecting environmental information. This part of its functionality is similar to human senses, utilizing 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 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 sensing 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 to act 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 usually employs the following technologies:

• Rule Engine: Makes 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 typically involves several steps: first, assessing 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, implementing the decisions made by the reasoning module. This part interacts with external systems or devices to complete designated 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 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 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 usually 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 the model with real-time data to maintain the agent's 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.

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 tremendous 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 demonstrating 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 (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 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 some major tech companies are becoming increasingly active, indicating that AI AGENT has a greater presence beyond the cryptocurrency field.