分散的AI代理（dai-agent）：Web3中值Internet的关键组成部分

莱特（Relly），分散的AI代理（Dai-Agent）的领域受到了极大的关注，许多文章介绍了相关项目的特征，他们解决的问题以及其未来的潜力。尽管这些文章有助于投资者在某种程度上了解这些项目，但大多数缺乏深入的分析，并且未能探索AI的基本特征和Web3的当前状态。因此，无论是优化Web3还是用作关键组件，很难阐明分散式AI在Web3中的实践中的作用。如果不阐明分散的AI和Web3的价值之间的内在逻辑，就不可能深入了解分散AI的作用或掌握其核心组件如何解决Web3中存在的问题。这不仅使我们难以准确选择高电势的投资方向，而且即使我们选择了正确的轨道和项目，我们也可能因市场情绪波动而难以持久。因此，我计划对当前Web3的基本状态以及AI的基本特征进行深入分析，探索两者的集成如何实现价值Internet的实施，以及ARWEAVE和AO如何帮助这一过程通过AI。由于内容的丰富性，我将详细说明两篇文章：

The emergence of decentralized AI agents (DAI-Agent) has sparked widespread interest within the crypto community, with numerous articles highlighting the unique characteristics of related projects, the problems they aim to solve, and their potential for future growth. While these articles offer valuable insights, many lack in-depth analysis and fail to fully explore the fundamental characteristics of AI and the current state of Web3. As a result, it becomes difficult to clarify the precise role of decentralized AI in the practice of the value internet in Web3, whether it serves to optimize Web3 or functions as a key component. Without establishing the intrinsic logic between decentralized AI and the value internet economy of Web3, it is impossible to deeply understand the role of decentralized AI or grasp how its core components address the issues present in Web3. For example, what problems do the decentralized model and DAI-Agent each solve, and what is the intrinsic logic between them and Web3? Without understanding these intrinsic logics, it is challenging to assess the potential value of this field. This not only makes it difficult for us to accurately choose high-potential investment directions, but even if we select the right track and project, we may struggle to persist due to market sentiment fluctuations. Therefore, I plan to conduct an in-depth analysis of the current basic state of Web3 and the fundamental characteristics of AI, exploring how the integration of the two can realize the implementation of the value internet, and how Arweave and AO can assist this process through AI. Due to the richness of the content, I will elaborate in two articles:

分散的AI代理商（Dai-Agent）的出现引起了加密社区的广泛兴趣，许多文章强调了相关项目的独特特征，他们旨在解决的问题以及其未来增长的潜力。尽管这些文章提供了有价值的见解，但许多人缺乏深入的分析，并且未能充分探索AI的基本特征和Web3的当前状态。结果，很难阐明分散AI在Web3中的价值实践中的确切作用，无论它是为了优化Web3还是作为关键组件的功能。如果不建立分散的AI和Web3的价值之间的内在逻辑，就不可能深入了解分散的AI的作用或掌握其核心组件如何解决Web3中存在的问题。例如，每个求解的分散模型和dai gent求解什么问题，它们与Web3之间的内在逻辑是什么？在不了解这些内在逻辑的情况下，评估该领域的潜在价值是一项挑战。这不仅使我们难以准确选择高电势的投资方向，而且即使我们选择了正确的轨道和项目，我们也可能因市场情绪波动而难以持久。因此，我计划对当前Web3的基本状态以及AI的基本特征进行深入分析，探索两者的集成如何实现价值Internet的实施，以及ARWEAVE和AO如何帮助这一过程通过AI。由于内容的丰富性，我将详细说明两篇文章：

Currently, many public chain projects focus primarily on optimizing and expanding underlying infrastructure, such as ETH and various L2s, Solana, and other blockchains. However, I believe that if we only pursue the expansion of blockchain without integrating AI, it will be difficult to advance the implementation of the value internet in Web3. Currently, in addition to limited scalability, Web3 also faces data fragmentation issues, where users' personal data is scattered across different chains and DApps, leading to management difficulties, high interaction costs, and complex operations, severely limiting users' active contribution of data. Furthermore, the decentralized nature leads to low management and collaboration efficiency. These issues greatly restrict the development of Web3. AI, with its ability to learn, infer, and make decisions autonomously, can serve as an intelligent assistant for users, significantly enhancing efficiency. The integration of the two will greatly improve user experience, lower entry barriers, and promote the development of Web3.

当前，许多公共连锁项目主要着重于优化和扩展基础设施，例如ETH和各种L2S，Solana和其他区块链。但是，我相信，如果我们仅在不集成AI的情况下追求扩展区块链的扩展，那么很难提高Web3中价值Internet的实施。当前，除了有限的可伸缩性外，Web3还面临数据碎片问题，在该问题中，用户的个人数据散布在不同的链条和DAPP上，从而导致管理困难，高相互作用成本和复杂的操作，从而严重限制了用户对数据的积极贡献。此外，分散的自然可以提高管理和协作效率低。这些问题极大地限制了Web3的开发。 AI具有自主学习，推断和做出决定的能力，可以作为用户的智能助手，从而大大提高效率。两者的集成将大大改善用户体验，降低进入障碍并促进Web3的开发。

Crucial to Web3 is users' control over their own data, a feature that DAI-Agent can help to achieve by centrally managing and aggregating data for users. This effectively addresses the pain point of data being scattered across various platforms, while also acting as an intelligent assistant to reduce operational difficulty and enhance interaction efficiency with Web3. For example, DAI-Agent can assist users in managing their DID lifecycle, including creating, updating, and revoking DIDs, thereby simplifying data management and usage experience. To lay the groundwork for subsequent discussions, it is necessary to explore the relationship between AI-Agent and DID in detail. In the Web3.0 environment, DID and DAI-Agent are highly complementary and compatible:

对Web3至关重要的是用户对自己数据的控制，这是Dai-Agent可以通过中央管理和汇总用户数据来实现的功能。这有效地解决了数据散布在各个平台上的痛点，同时还充当智能助手，以减少操作困难并提高与Web3的相互作用效率。例如，Dai-Agent可以帮助用户管理其DID生命周期，包括创建，更新和撤销DIDS，从而简化数据管理和使用体验。为了为随后的讨论奠定基础，有必要探索Ai-agent之间的关系并详细介绍。在Web3.0环境中，DID和Dai-Agent具有高度互补和兼容：

AI-Agent can integrate data across platforms (such as social, medical, and professional data), effectively breaking down information silos; its intelligent algorithms can filter, clean, and format data based on the needs of DID (such as assessing the credibility of various data sources, removing duplicate or low-value data, and organizing data according to DID data model specifications), ensuring the creation of high-quality DIDs. At the same time, using differential privacy, homomorphic encryption, and the latest multi-party secure computation (MPC) technologies, data analysis can be completed without disclosing the original data (for example, when aggregating sensitive medical data, it can meet health information needs while ensuring personal privacy). Additionally, as cross-chain interoperability protocols (such as Polkadot, Cosmos, etc.) continue to mature, DAI-Agent is expected to achieve seamless connections between more data sources, further enhancing the efficiency and accuracy of data integration. The decentralized architecture not only avoids the risks of single points of failure and data being controlled by a single entity but also enables automated data aggregation and real-time updates through smart contracts, providing strong support for building a trustworthy and dynamic digital identity system.

Ai-agent可以在平台（例如社交，医学和专业数据）之间集成数据，从而有效地分解信息筒仓；它的智能算法可以根据DID的需求过滤，清洁和格式数据（例如评估各种数据源的信誉，删除重复或低价值数据以及根据DID数据模型规范组织数据），以确保创建高质量的涂料。同时，使用差异隐私，同型加密和最新的多方安全计算（MPC）技术，可以完成数据分析而无需披露原始数据（例如，当汇总敏感医疗数据时，它可以符合健康信息在确保个人隐私的同时）。此外，随着跨链互操作性协议（例如Polkadot，Cosmos等）继续成熟，预计Dai-Agent将在更多数据源之间实现无缝连接，从而进一步提高数据集成的效率和准确性。分散的体系结构不仅避免了单个实体控制的单点故障和数据的风险，而且还可以通过智能合约来实现自动数据聚合和实时更新，从而为建立可信赖和动态的数字身份系统提供了强有力的支持。

In a decentralized environment, the digital identity system provides the necessary identity authentication and authorization mechanisms for DAI-Agent, allowing the AI-Agent to prove its legitimate identity and authority when securely interacting with other agents. This process relies not only on technical means but can also be governed and regulated by community participation through decentralized autonomous organization (DAO) mechanisms, further enhancing the system's transparency and security.

在分散的环境中，数字身份系统为Dai-Agent提供了必要的身份身份验证和授权机制，从而使Ai-Angent在与其他代理商安全互动时可以证明其合法的身份和权威。该过程不仅取决于技术手段，而且还可以通过分散的自治组织（DAO）机制来控制和监管，从而进一步提高了系统的透明度和安全性。

With the help of the DID system, the identity and behavior of DAI-Agent are more transparent and verifiable, thereby establishing trust and promoting collaboration among other agents; at the same time, AI-Agent effectively alleviates the low efficiency issues caused by decentralization by reducing the interaction costs between users

借助DID系统，Dai-Agent的身份和行为更加透明和可验证，从而建立了信任和促进其他代理之间的协作；同时，AI-Anent通过降低用户之间的相互作用成本有效地减轻了通过分散率引起的低效率问题