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

1. Background Overview

1.1 Introduction: "New Partners" in the Intelligent Era

Each cryptocurrency cycle promotes the development of the entire industry, bringing about brand new infrastructure:

• In 2017, smart contracts gave rise 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, memecoins and launch platforms will experience a boom.

The rise of these fields is not only due to technological innovation but also the perfect combination of financing models and bull market cycles. Looking ahead to 2025, AI agents will become a new emerging area. This trend will peak in October 2024, when the $GOAT token is launched and achieves a market value of $150 million. Subsequently, Virtuals Protocol launched Luna, debuting with the image of a "girl next door," igniting the entire industry.

The AI Agent has many similarities with the Red Queen AI system from the classic movie "Resident Evil." In reality, the AI Agent is the "intelligent guardian" 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.

For example, the 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 iterations. The AI AGENT 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.
2. Creative AI Agent: Used for content generation, including text, design, and even music creation.
3. Social AI Agent: Interacting with users as an opinion leader on social media, building communities, and participating in marketing activities.
4. Coordinating AI Agent: Coordinates complex interactions between systems or participants, especially suitable for multi-chain integration.

1.1.1 Development History

The development of AI AGENT showcases the evolution of AI from basic research to widespread application:

• In 1956, the term "AI" was first introduced at the Dartmouth Conference.
• In the 1980s, the development and commercialization of expert systems led companies to begin adopting AI technology.
• In 1997, IBM's Deep Blue computer defeated the world chess champion.
• The progress in computing power at the beginning of this century has driven the rise of deep learning.
• In the 2010s, breakthroughs were achieved in reinforcement learning agents and generative models like GPT-2.
• The release of GPT-4 is seen as a turning point in the field of AI agents.

The emergence of large language models has become an important milestone in AI development. Their outstanding performance in natural language processing allows AI agents to demonstrate clear logical reasoning and well-organized interaction 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.

1.2 Working Principle

The difference between AI AGENT and traditional robots is that they can learn and adapt over time, making nuanced decisions to achieve goals. The workflow of an 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 its perception module, collecting environmental information. This part functions similarly to human senses, using sensors, cameras, microphones, and other devices to capture external data. 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 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. This module typically utilizes 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: Allow AI AGENT to continuously optimize decision-making strategies through trial and error, adapting to changing environments.

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. 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 web service access.
• Automated process management: In a corporate environment, repetitive tasks are executed through RPA (Robotic Process Automation)

1.2.4 Learning Module

The learning module is the core competitive advantage of the AI AGENT, allowing the agent to become smarter over time. The learning module is typically improved in the following ways:

• Supervised Learning: Using labeled data for model training, enabling the AI AGENT to perform tasks more accurately.
• Unsupervised Learning: Discovering underlying patterns from unlabeled data to help agents adapt to new environments.
• Continuous Learning: Keep the agent's performance in a dynamic environment by updating the model with real-time data.

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. According to reports, 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 of up to 44.8%.

Large companies have also significantly increased their investment in open-source proxy frameworks. From the perspective of deploying public chains, Solana is the main battleground, while other public chains like Base Chain also have huge potential.

From the market awareness perspective, FARTCOIN and AIXBT are far ahead. Fartcoin was proposed for issuance by an AI model and achieved a temporary valuation of over $1 billion in December 2024. AIXBT, on the other hand, is an AI Agent based on the Base chain launched by Virtuals Protocol, providing users with powerful market analysis capabilities.

From a technical perspective, AI Agent technology is developing towards multimodal interaction and high autonomous decision-making capabilities. In 2024, the introduction of cross-modal learning and generative pre-training models will enable AI Agents to better understand and process various forms of data, such as text, images, and speech.

1.3.2 Reasons for the Combination of AI Agent and Token Economic Model

The combination of AI agents and token economic models is not only an inevitable trend in technological development but also provides an internal driving mechanism for building an efficient, transparent, and sustainable ecosystem. The main reasons include:

1. Build a more efficient incentive system
2. The Assetization of the AI Agent itself
3. Support interaction and trading between AI Agents
4. Enhance the transparency and security of the system.
5. Accelerate the formation of a global, borderless AI economic ecosystem.

2. AI Agent in crypto application analysis

2.1 AI AGENT LAUNCHPAD

AI Agent Launchpad is a platform focused on intelligent agents and their related token issuance, allowing users to easily create and deploy AI AGENTs, seamlessly integrating with social media platforms to achieve automated user interaction.

2.1.1 Virtuals Protocol

Virtuals Protocol launched on Base, allowing users to easily deploy their own AI AGENT using the VIRTUAL token. Its features include:

• Creation and Deployment: Each agent requires 100 VIRTUAL tokens to launch, ensuring initial liquidity through a bonding curve mechanism.
• Capitalization mechanism: After reaching a specific capitalization threshold, the agent enters a new stage and automatically deploys a liquidity pool.
• Autonomous Interaction: Agents can automate tasks such as trading and participate in community activities.

The success of Virtuals Protocol stems from a series of key transformations and innovative initiatives. The team transitioned from PathDAO to the AI AGENT protocol and quickly became a leading project with a market value of $1.7 billion.

2.1.2 Holoworld

Holoworld is a complete AI + game technology framework designed to democratize AI character creation through this platform, fundamentally transforming digital interaction models. Its core modules include:

1. Brain Development
2. Role Personalization Customization
3. Personalized Behavior Integration
4. Knowledge Base Integration
5. 3D Avatar Creation

Holoworld also launched the Agent Market, allowing anyone to create and deploy multimodal AI agents.

2.2 AIAGENT Framework

ai16z is a key project driving AI AGENT narratives, with its open-source framework ElizaOS becoming the market focus.

2.2.1 Eliza OS

ElizaOS is a tool that supports the creation of customized AI AGENTS, featuring strong network effects and unlimited scalability. Its architecture is divided into five main components:

1. Agent
2. Actions
3. Evaluators
4. Providers
5. Memory System

2.3 DEFAI

DeFAI (DeFi + AI) is an upgraded version of DeFi, allowing people to use DeFi more conveniently. The main application areas include:

2.3.1 Abstract Layer

The abstraction layer hides the complexity of DeFi through an intuitive interface, allowing users to interact with DeFi protocols using natural language commands. Key projects include:

• GRIFFAIN: Allows users to perform a variety of operations from simple to complex.
• ORBIT / GRIFT: Focused on on-chain DeFi experience, emphasizing cross-chain functionality.
• HEYANON: An AI DeFi protocol that simplifies DeFi interactions and aggregates project information.

2.3.2 Autonomous Trading Agent

Automated trading agents can adapt to their environment, learn, and make smarter decisions over time. Key projects include:

• ai16z: The first AI version of VC, combining decentralized governance and the powerful potential of AI.
• ALMANAK: Provides users with institutional-level quantitative AI AGENT
• COD3XORG / BIGTONYXBT: An DeFAI ecosystem aimed at simplifying the creation of trading agents.

2.3.3 AI-driven dApp

AI-driven dApps represent a field full of potential but still in its early stages. Some key projects include:

• ARMA: Autonomous Stablecoin Agriculture Protocol
• Modius: Autonomous Agent Balancer LP Farming
• Amplifi Lending Agents: Automated Lending Agents

2.4 AI AGENT+Game

The application of AI AGENT in the gaming industry is transforming various aspects of gameplay and development, with key applications including:

1. NPC Behavior Optimization
2. Programmatic Content Generation
3. Adaptive Difficulty Adjustment
4. Path Planning and Navigation
5. Graphics Enhancement
6. Player Sentiment Analysis

Main projects include:

2.4.1 Digimon

Digimon is a complete AI+game technology framework that deeply integrates AI technology into game development, enabling creators to build more immersive, dynamic, and engaging games.

2.4.2 Illuvium

Illuvium is an RPG and NFT game built on Ethereum, in collaboration with Virtuals Protocol, utilizing AI technology to provide dynamic and intelligent behavior for NPCs.

2.4.3 Smolverse

Smolverse is a game and NFT project on Treasure DAO, currently developing an on-chain AI Tomogatchi game called "Smolworld," which combines Eliza's Agent framework.

![Decoding AI AGENT: The Intelligent Force Shaping the New Economic Ecosystem of the Future](