The Predicament of Web3 AI Agents
The Achilles’ Heel of Web3 AI Agents: Over-Conceptualization
The challenge with Web3 AI Agents lies in their excessive conceptualization, where the narrative outweighs practical utility. While there’s much discussion about the grand vision of decentralized platforms and user data sovereignty, the user experience of actual product applications is often woefully inadequate. Especially after a round of conceptual bubble cleansing, few retail investors are willing to pay for grand and unfulfilled expectations.
The Web3 AI Agent space has been plagued by an overemphasis on theoretical possibilities at the expense of tangible results. The allure of decentralization, data ownership, and novel governance models has captured the imagination of many, but the reality often falls short of the hype. Users are left with clunky interfaces, limited functionality, and a general sense that the technology is not yet ready for prime time. This is not to say that the core tenets of Web3 are flawed, but rather that the application of these tenets to AI agents needs a more pragmatic approach. The focus should be on solving real-world problems and providing demonstrable value to users, rather than simply adhering to abstract ideals.
The initial excitement surrounding Web3 AI agents was fueled by the promise of a decentralized, transparent, and user-centric AI ecosystem. However, the reality has been far more complex. Building truly decentralized AI systems requires overcoming significant technical and logistical challenges. Ensuring data privacy and security while also enabling collaboration and innovation is a delicate balancing act. Furthermore, the lack of standardized protocols and interoperability has hampered the development of a cohesive Web3 AI ecosystem.
The Need for Practical Applications
The Web3 community needs to shift its focus from abstract ideals to concrete applications. The promise of decentralized AI is compelling, but it will only be realized if it translates into real-world benefits for users. This requires a focus on user experience, ease of use, and tangible value creation. It’s not enough to simply build decentralized versions of existing AI tools; the goal should be to create entirely new applications that leverage the unique capabilities of Web3.
This shift in focus also requires a change in mindset. The Web3 community needs to be more open to collaboration and experimentation. Building a successful Web3 AI ecosystem requires the combined expertise of AI researchers, software engineers, and domain experts. Furthermore, it requires a willingness to embrace failure and learn from mistakes. The path to decentralized AI is not a straight line, and there will be many challenges along the way.
Investors are growing weary of projects that promise the moon but fail to deliver. They are looking for projects that can demonstrate a clear path to adoption and revenue generation. This means building products that solve real problems and offer a compelling value proposition. Investors are now demanding tangible evidence of progress, such as working prototypes, user adoption metrics, and clear revenue models. The days of simply raising capital based on a whitepaper and a grand vision are over.
The Pragmatism of Web2 AI: MCP and A2A
The Rise of MCP and A2A in Web2 AI
The rapid rise of MCP, A2A, and other protocol standards in the web2 AI field, and their resulting momentum in the AI space, stems from their ‘visible and tangible’ pragmatism. MCP is like the USB-C interface of the AI world, allowing AI models to seamlessly connect to various data sources and tools. There are already many practical MCP use cases. This is in stark contrast to the often-theoretical and highly abstract discussions surrounding Web3 AI agents.
In stark contrast to the conceptual focus of Web3 AI, Web2 AI has prioritized practicality and real-world impact. The emergence of protocols like MCP (Model-Controller-Pipeline) and A2A (Application-to-Application) has been driven by a desire to solve concrete problems and create tangible value. These protocols are not about lofty ideals or decentralized utopias; they are about making AI more accessible, efficient, and useful.
The Web2 AI ecosystem has benefited from a more mature infrastructure, a larger talent pool, and a greater willingness to adopt proven technologies. This has allowed Web2 companies to rapidly develop and deploy AI-powered applications across a wide range of industries. The focus has been on delivering immediate value to users, rather than on building a fundamentally different AI paradigm.
MCP: The Universal Connector for AI
MCP, often likened to a USB-C interface for AI, enables AI models to connect seamlessly to diverse data sources and tools. This standardized approach simplifies the integration of AI into existing systems, allowing developers to build more complex and powerful applications. It provides a framework for building, deploying, and managing AI models across different platforms and environments.
The beauty of MCP lies in its simplicity and versatility. It provides a common framework for connecting AI models to data sources, tools, and other applications. This eliminates the need for custom integrations, saving developers time and effort. The result is faster development cycles, reduced costs, and greater flexibility. MCP also promotes interoperability, allowing different AI models and tools to work together seamlessly.
MCP’s standardized approach makes it easier to scale AI deployments. By providing a consistent interface for connecting to different data sources and tools, MCP allows developers to easily add new capabilities and scale their applications as needed. This is particularly important for organizations that are looking to deploy AI across multiple departments or business units.
Real-World Examples of MCP in Action
For example, some users can directly use Claude to control Blender to make 3D models, and some UI/UX practitioners can use natural language to generate complete Figma design files. Some programmers can also directly use Cursor to complete code writing, supplementation, and Git submission in one stop. These examples illustrate the power of MCP to democratize access to AI and empower users to create and innovate in new ways.
AI-Powered 3D Modeling: Imagine using natural language to instruct an AI model to create a 3D model. With MCP, this is becoming a reality. Users can simply describe the desired model, and theAI will generate it automatically, streamlining the design process and opening up new creative possibilities. This eliminates the need for specialized 3D modeling skills, making it easier for anyone to create and visualize complex designs.
Automated UI/UX Design: The tedious task of designing user interfaces can now be automated with AI. UI/UX practitioners can use natural language to describe the desired interface, and the AI will generate a complete Figma design file, saving them countless hours of work. This allows designers to focus on the creative aspects of their work, such as user experience and visual aesthetics, rather than on the repetitive tasks of creating layouts and components.
AI-Assisted Programming: Programmers can leverage AI to automate routine tasks and improve code quality. With tools like Cursor, developers can use natural language to write code, generate documentation, and submit changes to Git, all from a single interface. This increases developer productivity and reduces the risk of errors. AI-assisted programming tools can also help developers learn new languages and frameworks more quickly.
These examples highlight the transformative potential of MCP. By providing a standardized framework for connecting AI models to data sources and tools, MCP is enabling developers to build more powerful and versatile applications. The adoption of MCP is accelerating, and its impact on the AI landscape is only going to grow in the years to come. It is important for Web3 AI agents to recognize the power of MCP and A2A in order to promote the creation of functional applications.
Bridging the Gap: MCP and A2A for Web3
The Limitations of Web3 AI in Vertical Scenarios
Previously, everyone expected web3 AI Agent to have innovative landing applications in the two major vertical scenarios of DeFai and GameFai, but in reality, many similar applications are still stuck at the natural language processing interface ‘show skills’ level, which is not enough to meet the threshold of practicality. The focus has been on demonstrating the technical capabilities of the AI models, rather than on solving real-world problems or providing tangible value to users.
Despite the initial excitement, Web3 AI Agents have struggled to find practical applications in key vertical sectors like DeFi (Decentralized Finance) and GameFi (Decentralized Gaming). Many projects remain stuck at the ‘show skills’ stage, demonstrating impressive natural language processing capabilities but failing to deliver tangible value to users. This lack of practical applications has led to a decline in user interest and investment in Web3 AI projects.
The DeFi and GameFi sectors present unique challenges for AI agents. In DeFi, AI agents need to be able to analyze complexfinancial data, predict market trends, and execute trades in a secure and efficient manner. In GameFi, AI agents need to be able to create engaging and immersive gaming experiences, manage virtual economies, and personalize gameplay. Meeting these challenges requires a combination of technical expertise, domain knowledge, and a deep understanding of user needs.
Moving Beyond ‘Show Skills’
The focus on showcasing technical capabilities has come at the expense of usability and real-world impact. Users are less interested in flashy demonstrations and more concerned with how AI can solve their problems and improve their lives. The Web3 AI community needs to shift its focus from building impressive demos to building useful applications.
To succeed, Web3 AI Agents must move beyond the ‘show skills’ phase and focus on building practical applications that address specific needs. This requires a deep understanding of the target market and a commitment to user-centric design. The goal should be to create AI-powered tools that are easy to use, provide tangible value, and integrate seamlessly into existing workflows.
User feedback is crucial for identifying the pain points and unmet needs of potential users. By actively soliciting and incorporating user feedback, Web3 AI developers can ensure that their products are aligned with user needs and expectations. This iterative approach to product development is essential for building successful Web3 AI applications.
The Power of Multi-Agent Collaboration
Through the combination of MCP and A2A, a more powerful Multi-Agent collaboration system can be constructed, and complex tasks can be broken down for specialized Agents to handle. For example, let the analysis Agent read the on-chain data, analyze market trends, and connect other prediction Agents and risk control Agents to transform the past single Agent’s integrated execution thinking into a multi-Agent collaborative division of labor paradigm. This approach enables more efficient and effective task completion.
By combining the strengths of MCP and A2A, developers can create sophisticated multi-agent systems that can tackle complex tasks. This approach involves breaking down tasks into smaller, more manageable components and assigning them to specialized agents. Each agent can focus on a specific aspect of the task, leveraging its unique expertise and capabilities.
Multi-agent systems offer several advantages over single-agent systems. They are more robust, scalable, and adaptable. They can also handle more complex and nuanced tasks. However, building effective multi-agent systems requires careful design and coordination. It is important to ensure that the agents can communicate and collaborate effectively.
A Collaborative Ecosystem of AI Agents
For example, an analysis agent could be tasked with reading on-chain data and analyzing market trends, while other agents could focus on prediction and risk control. This collaborative approach allows for a more efficient and effective execution of complex tasks, moving away from the traditional monolithic agent paradigm. This ecosystem could dramatically increase the applications for Web3 AI agents.
The key to success lies in the seamless integration of these agents, allowing them to communicate and collaborate effectively. This requires a robust communication framework and a shared understanding of the task at hand. The agents need to be able to exchange information, negotiate goals, and coordinate actions.
Building a collaborative ecosystem of AI agents requires a commitment to open standards and interoperability. The agents need to be able to communicate with each other regardless of their underlying technology or platform. This requires the adoption of common protocols and data formats.
MCP Success Stories as Blueprints for Web3
All the successful application cases of MCP provide successful examples for the birth of a new generation of trading and game Agents in web3. These examples demonstrate the potential of MCP to enable new and innovative applications of AI in various domains.
The success stories of MCP in the Web2 world provide valuable blueprints for the development of Web3 trading and gaming agents. By learning from the experiences of Web2 pioneers, Web3 developers can accelerate the adoption of AI in these critical sectors. The focus should be on adapting and extending the MCP framework to meet the unique requirements of the Web3 ecosystem.
Web3 offers several advantages over Web2 for AI development, including increased transparency, data ownership, and user control. By combining the strengths of both Web2 and Web3, developers can create a new generation of AI applications that are both powerful and ethical. The key is to focus on building practical applications that solve real-world problems and provide tangible value to users.
The Hybrid Approach: Combining Web2 Pragmatism with Web3 Values
The Advantages of a Hybrid Framework
In addition to these, the hybrid framework standard based on MCP and A2A also has advantages such as friendliness to web2 users and application landing speed. At present, it is only necessary to consider how to combine web3’s value capture and incentive mechanism with application scenarios such as DeFai and GameFai. If projects are still adhering to web3 pure conceptualism and refuse to embrace web2 pragmatism, they may miss the next new trend of AI Agent. By leveraging Web2 infrastructure and expertise, developers can accelerate the development and deployment of Web3 AI applications.
The hybrid framework, combining the strengths of MCP and A2A with the values of Web3, offers several key advantages, including:
User-Friendliness: By leveraging the existing infrastructure and tools of Web2, the hybrid framework can provide a more familiar and intuitive experience for users, lowering the barrier to entry for Web3 applications. This is crucial for attracting mainstream adoption of Web3 AI.
Rapid Deployment: The hybrid framework allows developers to quickly deploy AI-powered applications by leveraging existing Web2 technologies and infrastructure. This reduces the time and cost associated with building and deploying Web3 AI applications.
Value Capture and Incentive Mechanisms: By integrating Web3’s value capture and incentive mechanisms, the hybrid framework can align the interests of users, developers, and other stakeholders, fostering a more sustainable and equitable ecosystem. This can be achieved through tokenomics, decentralized governance, and other mechanisms.
Integrating Web3 Values into Web2 Frameworks
The challenge lies in seamlessly integrating Web3 values into Web2 frameworks. This requires careful consideration of how to incorporate decentralized governance, data ownership, and tokenomics into existing systems. The goal is to create a hybrid framework that combines the best of both worlds: the pragmatism and scalability of Web2 with the transparency and user-centricity of Web3.
Decentralized governance can be implemented through DAOs (Decentralized Autonomous Organizations), allowing users to participate in the decision-making process and influence the development of the AI applications. Data ownership can be ensured through the use of blockchain technology, giving users control over their data and allowing them to monetize it. Tokenomics can be used to incentivize users to contribute to the ecosystem and reward them for their participation.
The Risk of Pure Conceptualism
Projects that cling to pure Web3 conceptualism without embracing the pragmatism of Web2 risk missing out on the next wave of AI Agent innovation. The future of AI lies in the intersection of these two worlds, where the ideals of Web3 are tempered by the practicality of Web2. A balanced approach is essential for building successful and sustainable Web3 AI applications.
The Web3 AI community needs to be more open to collaboration and experimentation. By embracing the lessons learned from Web2 and incorporating them into their development processes, they can avoid the pitfalls of pure conceptualism and build AI applications that are both innovative and practical.
The Future of AI Agents: A Synthesis of Ideals and Pragmatism
In a nutshell, the new momentum of the next wave of AI Agent is brewing, but it is no longer the pure narrative and concept-hyping posture of the past, but must be supported by pragmatism and application landing. This shift towards practicality is driven by the growing demand for real-world solutions and the increasing maturity of the Web3 ecosystem.
The future of AI Agents lies in a synthesis of ideals and pragmatism. By combining the visionary goals of Web3 with the practical approach of Web2, we can create a new generation of AI-powered applications that are both innovative and impactful. The next wave of AI Agent development will be driven by practical applications and real-world value, not just hype and empty promises. It is an era of concrete achievements. Web3 AI agent is ready to take off.