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

1. Background Overview

1.1 Introduction: "New Partner" 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 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 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 tremendous transformation. Looking ahead to 2025, it is clear that the emerging field of 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 thereafter, on October 16, a certain protocol launched Luna, making its debut with the IP live streaming image of a girl next door, 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 independently sensing 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 similar role to some extent; 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 intelligent customer service, AI Agents have penetrated various industries, becoming a key force for enhancing efficiency and innovation. These autonomous intelligent entities, much like invisible team members, possess comprehensive capabilities 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 investment 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 is not a single form but is divided into different categories 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: Act 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, especially 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 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 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 proposal of neural networks and the preliminary exploration of machine learning concepts. However, AI research during this time was severely constrained by the limited computing power of the era. Researchers faced significant difficulties in developing 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 about AI research following the early excitement, 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 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 advancements 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 across 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 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 event in AI's ability to solve complex problems. The revival of neural networks and deep learning laid the foundation for AI development in the late 1990s, making AI an indispensable part of the technological landscape and beginning to influence everyday life.

By the beginning of this century, advances in computing power drove the rise of deep learning, with virtual assistants like Siri showcasing the practicality of AI in consumer applications. In the 2010s, reinforcement learning agents and generative models like GPT-2 made further breakthroughs, elevating conversational AI to new heights. In this process, the emergence of Large Language Models (LLM) became an important milestone in AI development, especially with the release of GPT-4, which was regarded as a turning point in the field of AI agents. Since a certain company released 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 has enabled AI agents to exhibit clear and coherent interaction capabilities through language generation. This has allowed AI agents to be applied in scenarios like chat assistants and virtual customer service, gradually expanding to more complex tasks such as business analysis and creative writing.

The learning capability of large language models provides AI agents with greater autonomy. Through Reinforcement Learning techniques, 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 realizing 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 continuously breaking 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 the capability 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 us into 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 seen as highly skilled and ever-evolving participants in the field of cryptocurrency, 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, adjustment.

1.2.1 Perception Module

The AI AGENT interacts with the external 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 identifying 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): 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 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, it acts as an orchestrator or reasoning engine 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 predefined 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 consists of several steps: first, an assessment of the environment; second, calculating multiple possible action plans based on the objectives; 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, implementing the decisions made by the reasoning module. 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:

• Robotic Control System: Used for physical operations, such as the movement of robotic arms.
• API calls: Interacting with external software systems, such as database queries or network service access.
• Automated Process Management: In an enterprise environment, executing repetitive tasks through RPA (Robotic Process Automation).

1.2.4 Learning Module

The learning module is the core competence 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 to enable AI AGENT to perform tasks more accurately.
• Unsupervised Learning: Discovering latent patterns from unlabeled data to help the agent adapt to new environments.
• Continuous Learning: Update models with real-time data to maintain agent performance in a dynamic environment.

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 hard to estimate in the previous cycle, AI AGENT has also shown similar 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) 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.

Large companies have also significantly increased their investment in open-source proxy frameworks. The development activities of certain company's frameworks like AutoGen, Phidata, and LangGraph are becoming increasingly active, indicating that AI AGENT has a larger market beyond the cryptocurrency field.