This article is a translation of an industry in-depth analysis article "Why Will AI Agents Become the Next Hot Narrative in Web3?" published by Sonny Young on Medium, with some deletions that do not affect the original intention. The article delves into the functions of AI agents, analyzes their advantages and development directions, and introduces their wide application in Web3.0. Let's take a look with Xiao Wan!
What are AI Agents?
Imagine you've created a game similar to 'TRON' Racing. 'TRON' as an AI agent in the game has a significant impact on the game's outcome. He needs to make decisions continuously throughout the game process, such as when to accelerate, turn, and jump, to skillfully avoid obstacles, overtake other racers, and ultimately win the race. In this example, the entire game environment and the behavior of the other racers constitute the environment of the AI agent. The AI agent will make the optimal decisions based on this information. For example, if the road ahead turns left, the intelligent agent will turn left.
The essence of an AI agent is a system that can perceive its environment, make decisions based on given goals, and take actions. These decisions are based on the AI agent's understanding of the environment, such as its destination, current position, and surrounding obstacles. One of the most impressive features of AI agents is their adaptability. They can "learn," which means they can continuously improve their decision-making process through interaction with the environment. For example, after multiple games, it may learn how to better avoid obstacles or find the optimal racing route, demonstrating its ability to evolve and adapt.
In summary, an AI agent is an AI system that can perceive and understand its environment, make decisions, take actions, and continuously learn and evolve.
AI Agents - The Next Hot Topic
In the development of large language models, the next step of evolution will proceed in two directions. On the one hand, it is "downward" to focus on updating and improving the underlying system. On the other hand, it is "upward" to expand by increasing complexity, intelligence, and automation, essentially the development of agent models. A typical agent module unit integrates components such as memory, planning, execution, and reflection, making it more intelligent than the traditional large language model interaction mode.
Furthermore, people are continuously adding new and anticipated features to these systems to meet various needs, thereby constantly expanding the boundaries of imagination. Some outstanding innovative ideas, such as integrating search engine and compiler functions, have been gradually realized in recent months of research.
Although a single agent is powerful and easy to use, 'OP' GPTs represent the future of AI business models based on independent agents. In this model, developers and users collaborate to customize and purchase 'OP' GPTs that meet their needs. 'OP' then shares the resulting profits with the developers. This collaborative business model not only promotes community cohesion but also tightly connects the community, developers, platform, and users, collectively advancing the grand vision of AI progress.
However, our vision should not be limited by the current possibilities. The future of 'AR' is boundless, and our imagination should extend accordingly. The potential for 'AR' evolution is enormous, and this infinite potential can inspire us and ignite our passion. For example, can the agents within 'OP' GPTs have the ability to learn and evolve independently?
AI Challenges in Web3
In short, the core challenge of Web3 is the lack of truly customized projects for users. "Users" here refer to the vast majority who do not have complex trading strategy knowledge, are not familiar with 'RON' programming, and do not know how to obtain accurate data from platforms such as 'ONG', 'Massari', 'Dune', or 'DeBank'. In the early stages of Web3, these users are particularly crucial, as they constitute the broadest user base in the field and must be effectively attracted and retained.
The Migration from Web2
Transitioning Web2 users to Web3 has profound implications in multiple aspects. First, Web3 marks the advent of the decentralized era, granting users unprecedented control over their data, identity, and digital assets. By migrating Web2 users to Web3, we not only achieve democratization at the technological level but also strengthen the sovereignty of users, allowing them to truly own and manage their online identity and data flow. This transformation addresses public concerns about data privacy, censorship, and surveillance in centralized platforms, providing users with a new choice centered on transparency, security, and empowerment.
Secondly, the introduction of Web2 users is crucial for driving the widespread adoption and mainstream acceptance of Web3. Web3 is not limited to the financial sector; it has the potential to revolutionize various fields, such as healthcare, supply chain management, voting systems, and content creation. With the addition of Web2 users, we will further expand the user base and ecosystem of decentralized applications, and this network effect will accelerate the development and maturity of decentralized networks. This widespread adoption not only validates the practical value and vitality of Web3 but will also stimulate more investment, research, and innovation in decentralized technologies, building a more robust, inclusive, and vibrant digital future.
Large-scale Adoption and Market Transformation
In the current market landscape, many teams are quietly investing in the development of Web3 AI agent projects. As competition in this field intensifies, a forward-looking strategic vision becomes crucial for navigating this turbulent market. Clearly, in this environment, the key to efficiency lies in precise market transformation, focusing on cultivating a unique and outstanding AI agent project, rather than dispersing resources in an attempt to become a comprehensive center for multiple 'AR' projects.
This approach deeply recognizes the complexity and multifaceted nature of the competitive environment, where differentiation and innovation are prerequisites for achieving market leadership. By focusing resources and efforts on developing an exceptional 'AR' agent project, enterprises can take a leading position in the constantly evolving Web3 transformation. Furthermore, this strategy is not limited to meeting the needs of the Web3 community; it also has a grand vision of driving the transformation of global Web2 users and providing services for them.
By adopting this comprehensive and forward-looking strategy, enterprises can capture market share in the emerging Web3 ecosystem and facilitate the seamless integration and adoption of decentralized technologies by a global audience. This will establish the enterprises as industry leaders in the transformative digital domain, ensuring their continued prosperity in this market of opportunities and challenges.
Endless Possibilities
Combining 'AR' agents with Web3 technologies not only creates a powerful synergy but also significantly enhances the functionality of decentralized applications (dApps), improves user experience, and drives innovation. Here are the specific ways in which 'AR' agents and Web3 technologies can be integrated, and the benefits they bring:
01 Decentralized Autonomous Organizations (DAOs)
Intelligent Decision Support: 'AR' agents, through in-depth data analysis, provide key insights to help DAOs make wiser and more efficient decisions.
Automated Governance Processes: 'AR' agents automate governance processes, including voting and proposal evaluation, allowing DAOs to respond to various situations more quickly and effectively.
02 Smart Contracts
Advanced Automation Capabilities: 'AR' agents can trigger and manage smart contracts based on complex conditions and data inputs, achieving more sophisticated and granular process automation.
Predictive Analytics and Optimization: Leveraging advanced machine learning models, 'AR' agents can predict contract execution outcomes and optimize the execution process of smart contracts.
03 Personalized dApps Experience
Deep User Customization: 'AR' agents, through analyzing user behavior and preferences, provide highly personalized services and content recommendations for dApps, creating a unique user experience.
Intelligent Chatbots and Assistants: Integrating advanced 'AR' chatbots, dApps can provide real-time, efficient support and interaction, enhancing the usability and convenience of decentralized platforms.
04 Data Privacy and Security Reinforcement
Intelligent anomaly detection: AI agents can monitor blockchain transactions and user activities in real-time, intelligently identify and prevent potential security threats and fraudulent activities.
Data privacy protection: Through advanced data management technologies, AI agents ensure the security and privacy of user data, while maintaining the transparency and credibility of the blockchain system.
05 Improvement of interoperability and scalability
Cross-chain interaction optimization: AI agents achieve seamless interoperability between blockchain networks, facilitating the smooth transfer of cross-chain data and assets.
Transaction processing and resource optimization: They significantly improve the scalability and performance of decentralized networks through smart optimization of transaction processing workflows and resource allocation.
06 Enhancing supply chain transparency and traceability
AI agents can deeply analyze and verify data at each stage of the supply chain, achieving end-to-end transparency and ensuring the authenticity and traceability of goods.
With AI technology, supply chain operations can be optimized, making demand forecasting, smart inventory management, and delay reduction more efficient.
07 Intelligent content creation and planning
AI agents not only can automatically generate and plan content for decentralized platforms (such as social media dApps), but also ensure that this content accurately matches user needs, providing personalized and engaging experiences.
In terms of content management, AI agents can perform decentralized content moderation, effectively identifying and filtering out inappropriate or harmful content, ensuring the purity of platform content.
08 Healthcare and self-sovereign identity management
With the in-depth analysis of health data by AI agents, users can receive more personalized medical recommendations and predictive health management solutions.
Supported by blockchain technology, AI agents help users achieve self-sovereign identity management, allowing users to fully control their personal information and credentials, ensuring the security and privacy of their data.
The integration of AI agents and Web3 technologies not only optimizes the decision-making process and achieves process automation, but also brings personalized experiences to users, significantly enhances security, and ensures cross-chain interoperability. This combination fully leverages the advantages of AI and blockchain technologies, jointly building a more intelligent, efficient, and user-friendly decentralized ecosystem.
