AI AGENT: The New Companion of the Intelligent Era

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 gave birth 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 a large number of NFT series marked the arrival of the era of digital collectibles.
• In 2024, the outstanding performance of pump.fun led the surge of memecoins and launch platforms.

It is important to emphasize 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 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 last October, with the $GOAT token launched on October 11, 2024, reaching a market value of $150 million on October 15. Shortly after, on October 16, Virtuals Protocol launched Luna, making its debut with the IP live streaming image of the girl next door, 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 share many core functionalities with the Red Queen. In the real world, AI Agents play a similar role to some extent; they are 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 penetrated various industries, becoming a key force for enhancing efficiency and innovation. These autonomous intelligent agents, like invisible team members, possess comprehensive abilities ranging from environmental perception to decision execution, gradually permeating various sectors and driving dual improvements in 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 Dexscreener or social platform X, constantly optimizing its performance through iterations. The AI AGENT is not a single form, but is categorized into different types based on specific needs in the cryptocurrency ecosystem:

1. Execution-type 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 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 mainly focused on symbolic methods, leading to the creation of 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 period was severely constrained by the limitations of computing power at the time. 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 on the state of ongoing AI research in the UK, published in 1973. The Lighthill report fundamentally expressed a comprehensive pessimism regarding AI research after the early excitement period, leading to a significant loss of confidence among UK academic institutions(, including funding agencies), in AI. 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 adopt AI technology. 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, how to scale AI systems and successfully integrate them into practical applications remains an ongoing challenge. Meanwhile, in 1997, IBM's Deep Blue 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 daily life.

By the beginning of this 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, 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 (Large Language Model, LLM ) became an important 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 thousands of billions 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 abilities through language generation. This allows AI agents to be applied in scenarios such as chat assistants and virtual customer service, gradually expanding to more complex tasks ( such as business analysis and creative writing ).

The learning capabilities of large language models provide 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 behavior strategies based on player input, truly achieving dynamic interaction.

From the early rule-based systems to large language models represented by GPT-4, the evolution of AI agents is a history of continuously breaking through technological boundaries. The emergence of GPT-4 is undoubtedly a significant turning point in this process. With further advancements in technology, AI agents will become more intelligent, scenario-based, and diverse. Large language models have not only injected the "wisdom" of the soul into AI agents but also provided 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 lies in their ability to learn and adapt over time, making nuanced decisions to achieve their goals. They can be viewed as highly skilled and continually 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 behavior 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, 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 to process and understand 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 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 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: 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, an assessment of the environment, second, calculating multiple possible courses of action based on the goal, 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, putting the decisions of the reasoning module into action. 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 call: Interacting with external software systems, such as database queries or web service access.
• Automation Process Management: In a corporate environment, execute 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 during 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.

The learning module is 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, helping 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

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 immense 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 has also 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 (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 frameworks such as Microsoft's AutoGen, Phidata, and LangGraph are becoming increasingly active, indicating that AI AGENT has greater market potential beyond the cryptocurrency field, and the TAM is also.