HTX Research 最新研报解读OpenClaw：执行入口争夺与火币HTX 的 AI 战略路径

marsbitPublished on 2026-03-24Last updated on 2026-03-24

Abstract

近日，火币HTX旗下研究部门HTX Research发布研报《从OpenClaw爆发，看AI如何开始争夺真正的工作入口》，分析了AI从问答工具向执行层演进的新趋势。报告指出，OpenClaw作为开源个人AI助手，可通过消息平台接收任务并联动各类应用执行操作，标志着AI正从“回答问题”转向“执行任务”，争夺的是工作入口的控制权。这一转变得益于多趋势成熟：模型能力提升、消息入口高频易用、开源分发快速推广、self-hosted模式保障数据主权，以及中小团队对效率提升的迫切性。中国市场尤其适合此类工具落地，因大量工作依托企业微信、飞书等高频消息界面，已有地方政府推出相关扶持政策。然而，OpenClaw要成为基础设施仍需跨越安全、治理和模板三大门槛，包括防范恶意软件、建立权限审计机制及行业级模板支持。火币HTX在AI领域采取差异化策略，推出聚合多模型的AINFT产品，支持加密原生登录和弹性支付机制，将AI定位为平台服务入口与生态连接器。其AI Skills聚焦交易执行、市场分析等功能，旨在构建完整的用户体验闭环。报告认为，AI执行层仍处早期阶段，但竞争重点已从参数比拼转向入口控制、权限治理和生态建设。火币HTX的布局为加密平台如何整合AI能力提供了行业样本。

近日,火币HTX 旗下研究部门 HTX Research 发布最新研报《从OpenClaw爆发,看AI如何开始争夺真正的工作入口》,围绕近期快速崛起的开源项目 OpenClaw,系统分析 AI 从问答工具向可托管执行层演进的产业趋势,并深入剖析火币HTX 在 AI 方向上的产品布局与差异化竞争策略。

报告的核心议题是,当 AI 不再只是"回答问题"而开始"执行任务",谁将掌握下一阶段的工作入口。围绕这个问题,研报从产品形态、市场驱动力、人机分工演变、中国市场机会与风险门槛五个维度展开分析。

AI 执行层的出现:从"更会聊天"到"真的做事"

OpenClaw 之所以引发市场广泛关注,核心在于它切入的不是回答质量,而是执行能力。它被定义为一个运行在用户自有设备上的个人 AI 助手,能够通过 WhatsApp、Telegram、Slack、飞书、Teams 等多个消息入口接收任务,并联动文件、浏览器、日历、邮箱和终端完成动作。研报指出,它争夺的并非一个新的聊天界面,而是 AI 时代的执行入口——人逐渐退回到目标定义与关键判断的位置,部分执行链条开始由数字代理承担。

这种转变背后有五条趋势在同一时间成熟:模型能力进入"够用阶段",足以支撑中等复杂度的多步骤任务;消息入口的高频特性使 AI 可以嵌入既有工作界面而非要求用户迁移;开源分发机制让项目可以快速突破开发者圈层;self-hosted 模式回应了数据主权关切;而小团队对"少人做更多事"的现实需求,则提供了最直接的牵引力。

中国市场的特殊适配性

研报特别指出,OpenClaw 在中国市场的传播势能不容忽视。中国大量中小团队的真实工作发生在企业微信、飞书、客服后台等消息驱动型界面之间,天然适合这类执行层工具的渗透。深圳、无锡等地已围绕 OpenClaw 生态推出补贴、办公空间与创业扶持政策,将其与"一人公司"叙事联系起来。内容团队、代理运营、投研监控、客服分流等高消息密度场景,被认为是最先跑通的落地方向。

安全与治理:从热点项目走向基础设施的三道门槛

但 OpenClaw 距离成为基础设施仍需跨越三道门槛。首先是安全门槛——近期已出现通过伪造 GitHub 仓库和搜索广告传播恶意安装包的案例;其次是治理门槛,企业需要清晰的权限审计、动作回放与人工审批机制;第三是模板门槛,通用平台若缺乏行业级接入模板,将难以跨过"尝鲜"到"长期使用"的鸿沟。

火币HTX 的 AI 路径:从模型聚合到平台级服务入口

作为深耕 AI 领域的全球性加密交易平台,火币HTX 在 AI 方向上的路径与 OpenClaw 代表的趋势形成互补。OpenClaw 代表的是"AI 作为执行层"的方向,而火币HTX 正在推进的是"AI 作为平台服务入口与生态连接器"的现实落地。

火币HTX 体系内推出的自研 AINFT 产品,聚合了包括 OpenAI、Anthropic、Google 在内的主流大模型能力,用户通过单一入口即可调用不同模型,无需在多个平台间切换。在登录层面,AINFT 采用 TronLink 钱包签名,无需绑定手机号或信用卡,贴近加密原生用户习惯。支付层面则采用"随充随用"的 Pay-as-you-go 机制,打破传统 AI 产品的月度订阅逻辑,更契合链上用户高频、小额、弹性使用的特征。

这一产品设计体现出,火币HTX 对 AI 的理解已从"效率工具"升级为"平台能力延伸"——未来用户进入平台,不一定只是为了交易,也可能是为了使用 AI 和智能服务,进而回流至交易与平台活动。

竞争策略层面,在当前主流交易平台纷纷推出 AI Skills 的背景下,火币HTX 采取了更聚焦的差异化打法:HTX AI Skills 首发即覆盖现货与合约交易执行,后续计划补充市场分析、市场情报与 App 内置 AI 助手,围绕"交易执行、风险评估、市场情报、用户入口"四个关键层面建立闭环。研报指出,竞争力的核心不在于 Skill 数量的堆叠,而在于谁最先将执行、风险、情报与入口串联为完整的用户体验。

赛道仍处早期,但方向已经清晰

AI 从工具层向执行层的演进仍然处于非常早期的阶段,安全、治理与生态成熟度都还远未到位。但 OpenClaw 的爆发已经让市场比较清晰地看到,AI 的下一步竞争可能不再只是参数和回答质量的比拼,而会延伸到入口控制、权限治理、技能生态与组织信任的综合层面。火币HTX 在这一方向上的提前布局,则为加密平台如何将 AI 从外部能力转化为自身可运营、可增长的长期资产,提供了一个值得关注的行业样本。

关于 HTX Research

HTX Research 是火币HTX 旗下的专属研究部门,负责对加密货币、区块链技术及新兴市场趋势等广泛领域进行深入分析,撰写全面报告,并提供专业评估。HTX Research 致力于提供基于数据的洞察和战略前瞻,在塑造行业观点和支持数字资产领域的明智决策方面发挥着关键作用。凭借严谨的研究方法和前沿的数据分析,HTX Research 始终站在创新前沿,引领行业思想发展,并促进对不断变化的市场动态的深入理解。访问我们。

如您希望沟通,请联系 [email protected]

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Agent S: The Future of Autonomous Interaction in Web3 Introduction In the ever-evolving landscape of Web3 and cryptocurrency, innovations are constantly redefining how individuals interact with digital platforms. One such pioneering project, Agent S, promises to revolutionise human-computer interaction through its open agentic framework. By paving the way for autonomous interactions, Agent S aims to simplify complex tasks, offering transformative applications in artificial intelligence (AI). This detailed exploration will delve into the project's intricacies, its unique features, and the implications for the cryptocurrency domain. What is Agent S? Agent S stands as a groundbreaking open agentic framework, specifically designed to tackle three fundamental challenges in the automation of computer tasks: Acquiring Domain-Specific Knowledge: The framework intelligently learns from various external knowledge sources and internal experiences. This dual approach empowers it to build a rich repository of domain-specific knowledge, enhancing its performance in task execution. Planning Over Long Task Horizons: Agent S employs experience-augmented hierarchical planning, a strategic approach that facilitates efficient breakdown and execution of intricate tasks. This feature significantly enhances its ability to manage multiple subtasks efficiently and effectively. Handling Dynamic, Non-Uniform Interfaces: The project introduces the Agent-Computer Interface (ACI), an innovative solution that enhances the interaction between agents and users. Utilizing Multimodal Large Language Models (MLLMs), Agent S can navigate and manipulate diverse graphical user interfaces seamlessly. Through these pioneering features, Agent S provides a robust framework that addresses the complexities involved in automating human interaction with machines, setting the stage for myriad applications in AI and beyond. Who is the Creator of Agent S? While the concept of Agent S is fundamentally innovative, specific information about its creator remains elusive. The creator is currently unknown, which highlights either the nascent stage of the project or the strategic choice to keep founding members under wraps. Regardless of anonymity, the focus remains on the framework's capabilities and potential. Who are the Investors of Agent S? As Agent S is relatively new in the cryptographic ecosystem, detailed information regarding its investors and financial backers is not explicitly documented. The lack of publicly available insights into the investment foundations or organisations supporting the project raises questions about its funding structure and development roadmap. Understanding the backing is crucial for gauging the project's sustainability and potential market impact. How Does Agent S Work? At the core of Agent S lies cutting-edge technology that enables it to function effectively in diverse settings. Its operational model is built around several key features: Human-like Computer Interaction: The framework offers advanced AI planning, striving to make interactions with computers more intuitive. By mimicking human behaviour in tasks execution, it promises to elevate user experiences. Narrative Memory: Employed to leverage high-level experiences, Agent S utilises narrative memory to keep track of task histories, thereby enhancing its decision-making processes. Episodic Memory: This feature provides users with step-by-step guidance, allowing the framework to offer contextual support as tasks unfold. Support for OpenACI: With the ability to run locally, Agent S allows users to maintain control over their interactions and workflows, aligning with the decentralised ethos of Web3. Easy Integration with External APIs: Its versatility and compatibility with various AI platforms ensure that Agent S can fit seamlessly into existing technological ecosystems, making it an appealing choice for developers and organisations. These functionalities collectively contribute to Agent S's unique position within the crypto space, as it automates complex, multi-step tasks with minimal human intervention. As the project evolves, its potential applications in Web3 could redefine how digital interactions unfold. Timeline of Agent S The development and milestones of Agent S can be encapsulated in a timeline that highlights its significant events: September 27, 2024: The concept of Agent S was launched in a comprehensive research paper titled “An Open Agentic Framework that Uses Computers Like a Human,” showcasing the groundwork for the project. October 10, 2024: The research paper was made publicly available on arXiv, offering an in-depth exploration of the framework and its performance evaluation based on the OSWorld benchmark. October 12, 2024: A video presentation was released, providing a visual insight into the capabilities and features of Agent S, further engaging potential users and investors. These markers in the timeline not only illustrate the progress of Agent S but also indicate its commitment to transparency and community engagement. Key Points About Agent S As the Agent S framework continues to evolve, several key attributes stand out, underscoring its innovative nature and potential: Innovative Framework: Designed to provide an intuitive use of computers akin to human interaction, Agent S brings a novel approach to task automation. Autonomous Interaction: The ability to interact autonomously with computers through GUI signifies a leap towards more intelligent and efficient computing solutions. Complex Task Automation: With its robust methodology, it can automate complex, multi-step tasks, making processes faster and less error-prone. Continuous Improvement: The learning mechanisms enable Agent S to improve from past experiences, continually enhancing its performance and efficacy. Versatility: Its adaptability across different operating environments like OSWorld and WindowsAgentArena ensures that it can serve a broad range of applications. As Agent S positions itself in the Web3 and crypto landscape, its potential to enhance interaction capabilities and automate processes signifies a significant advancement in AI technologies. Through its innovative framework, Agent S exemplifies the future of digital interactions, promising a more seamless and efficient experience for users across various industries. Conclusion Agent S represents a bold leap forward in the marriage of AI and Web3, with the capacity to redefine how we interact with technology. While still in its early stages, the possibilities for its application are vast and compelling. Through its comprehensive framework addressing critical challenges, Agent S aims to bring autonomous interactions to the forefront of the digital experience. As we move deeper into the realms of cryptocurrency and decentralisation, projects like Agent S will undoubtedly play a crucial role in shaping the future of technology and human-computer collaboration.

731 Total ViewsPublished 2025.01.14Updated 2025.01.14

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