Claude Code之父最新判断：AI时代团队分工被重写，这「五种人」最吃香

marsbitPublished on 2026-06-30Last updated on 2026-06-30

Abstract

在AI重塑软件行业的背景下，Anthropic Claude Code团队负责人Boris Cherny提出，传统的工程、产品、设计等岗位标签正被打破，团队分工演变为基于行为模式的五类新型角色： 1. **原型师**：专注提出大量创意与颠覆性想法，不追求每个都落地。 2. **构建者**：负责将粗糙原型转化为可投入生产的高可用产品或基础设施。 3. **清理师**：做减法，精简界面、重构代码、移除冗余，提升系统性能与可维护性。 4. **增长师**：推动成型产品小步快跑迭代，结合产品、数据与用户理解，使其从“能用”走向“被需要”。 5. **维护者**：保障成熟系统的长期稳定，专注安全性、可靠性及弹性。这些角色不绑定传统岗位，个人可横跨多种角色。团队组合需根据产品阶段动态调整：探索期需要原型师、构建者和清理师；增长期侧重构建者、清理师和增长师；成熟期则以清理师、增长师和维护者为主。讨论中，许多人认同角色应随项目灵活变化，避免自我固化。尽管AI能力增强，但人类在构建、清理等工作中仍不可或缺。未来，团队更关注成员能在产品生命周期的哪一阶段推动进展，而非僵化的岗位划分。

在 Agent Coding 火爆,重塑软件行业的当下,业界似乎已经逐渐接受「工程师」被改变的不争事实。可实际上,被改变的可能已经不只是「工程师」这一个岗位,更深刻的变革正在团队组织架构的底层悄然发生......

近日,Anthropic Claude Code 团队负责人 Boris Cherny 在 X 上提出了一个很有意思的观察。

他指出,随着工程、产品、设计、数据科学等职能逐渐融合,他一直在思考,未来这些角色会演变成什么样?以 Claude Code 团队为例,内部传统的「岗位标签」正在被彻底撕下,取而代之的是 5 类基于行为模式的「非绑定」新型角色:原型师、构建者、清理师、增长师、维护者。

原型师(The Prototyper):主要负责提出全新的想法,持续产出大量的创意,其中大多数最终不会上线。也就是说,他们追求的是想法的数量与颠覆性,而不纠结于是否每一个都要落地。

构建者(The Builder):主要负责把散落的创意或粗糙的原型,快速转化为可以真正投入生产环境、面向海量用户的产品或高可用基础设施。换句话说,他们负责解决的是从 0.1 到 1 的硬核飞跃。

清理师(The Sweeper):主要负责「做减法」,AI 时代最可怕的副作用是代码与功能的过度膨胀,清理师的职责是清理、精简用户界面,简化、重构混乱的代码与系统架构,将不必要的冗余功能移除,以换取系统的高性能与高可维护性。

增长师(The Growth):接手一个已经构建出来的成型产品,当产品进入市场,增长师负责进行小步快跑的持续迭代,要关心:如何让产品更接近市场?如何让用户更愿意留下来?如何让一个产品从「能用」走向「被需要」。不过这个角色并不等同于传统意义上的增长运营,而更接近产品、数据、用户理解和实验能力的结合体。

维护者(The Maintainer):负责一个成熟系统的长期运营,他们不一定参与追逐耀眼的新功能,但死磕安全性、可靠性、极致的运行效率和系统弹性,确保服务在任何极端流量下都能稳如磐石。

不过需要注意的是,这五类角色并不对应传统岗位。也就是说,它们不像传统的组织管理中,一个人的角色固定在职位头衔里。

Boris Cherny 认为,很多人可能会横跨两种角色,有时甚至会横跨三种角色。

「我也注意到,这些角色并不真正绑定具体岗位。比如在 Anthropic 内部,有些设计师更符合第 1 类,有些更符合第 2 类,有些则更符合第 3 类;工程师、产品经理、数据科学家也是如此。」

这就意味着,在高效的 AI 赋能团队中,许多成员不再是「单一螺丝钉」。一个设计师可以是原型师,也可以是清理师;一个工程师可以是构建者,也可以是维护者;一个产品经理可以承担增长师,也可以成为原型师;数据科学家可能不只会做分析,还可能直接参与产品增长和系统优化......

换句话说,未来团队看人的方式可能会发生变化。过去的问题可能主要是「你是什么岗位」?而未来或现在正在变成「你能在产品生命周期里推进哪一阶段」?

Boris Cherny 认为分析道,一个健康的团队所需要的这些角色的组合方式,具体取决于产品所处的阶段:

一个全新的、尚未找到产品市场契合度的产品,需要擅长 1、2、3 类角色的人;

一个正在增长、已经找到产品市场契合度的产品,需要 2、3、4 类角色,并配备一些 5 类角色;

一个已经拥有强产品市场契合度的产品,则需要 3、4、5 类角色,并保留一些 2 类角色。

「也许未来的产品角色会更像这样,而不是今天这种按专业领域划分的岗位。」

而此帖文一经发出,立即引起了网友的热议,大多数人表示赞同。

「这太符合人们真实的工作状态了。在一些项目里,我确实是 1+3 的组合,而在另一些项目里,我几乎就是纯粹的 4。岗位名称从来没有真正概括过这些。」

一位数据科学家也「现身说法」,说自己作为数据科学家,发现自己经常在做清理师类型的工作,同时还会带着数据科学的品味去搭产品。「所以这算不算我是 2+3 型?」

网友 Kun Chen@kunchenguid 则表示,深有同感。他说自己一直不太喜欢定义「角色原型」这件事,因为人们很容易看到之后就想:「啊,原来这就是我」,然后就不再继续反思自己。而在现实中,「一个人的角色往往需要随着项目一起变化。」

他举了个例子,比如刚开始做一个新项目时,他通常会是原型师和构建者;但很快,当那些粗糙、不完善的地方开始成为瓶颈时,他又会会变成清理师。而随着项目逐渐成熟,他又会转向增长师和维护者......「如果我把自己框死在某一种角色里,那么项目推进到某个阶段时,我就不得不放手。」

而且另一个现实是,现在大家越来越常同时推进多个项目,这就要求大家能在不同项目里扮演不同角色。「把自己归类到某种固定原型里,往往会限制一个人扩展野心。」

所以他的建议是:保持灵活,把注意力放在实现目标所需的最重要事情上,少纠结角色边界。因为这些边界只会随着时间继续变得模糊。

Boris Cherny 对此表示,这完全说中了自己的心声:「完全同意。角色往往会随着时间和项目阶段不断变化。」

也有网友表示质疑,「既然 AI 写代码这件事基本已经被解决了,为什么还需要构建者和清理师这类角色?难道不能直接让 Claude 不断循环执行吗?」

对此,Boris Cherny 的解释是,Claude 在不同程度上都能帮助完成这些事情,而且会随着时间继续变强。而当下,今天的 Claude 在承担清理师和构建者这两类工作上已经相当不错了。

那么你呢,如何看待这一职位角色变化?欢迎在评论区留言、交流!

参考链接:

https://x.com/bcherny/status/2071379474277613732

https://x.com/kunchenguid/status/2071382977628795289

本文来自微信公众号“机器之心”(ID:almosthuman2014),作者:关注AI的

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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.

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