要真正发挥 AI 代理（AI agents） 的全部价值，往往需要重构底层工作流程，并持续跟上一个高速演进的 AI 生态。正因如此，围绕构建 AI 代理的软件公司，至少会催生出 两类全新的商业模式。 1️⃣ 为企业实施 AI 代理的服务型公司 随着企业希望在各类工作中部署 AI 代理，不可能每家公司都自行摸索完成。 大多数企业并不具备足够的 IT 团队能力来交付这一转型，因此将会出现全新一代的系统集成商，专门帮助企业： * 重构工作流程 * 实施 AI 技术 * 推动组织层面的变革管理 * 持续为企业维护与更新 AI 代理 真正有意思的是：由于 AI 代理几乎横跨所有业务线，这些新型系统集成商将不再只是传统以 IT 系统为核心的集成商。 他们必须在多种具体岗位职能上成为领域专家，覆盖从： * 市场营销 * 法律 * 医疗健康 * 到软件开发与工程 换句话说，这是“业务能力 + AI 实施能力”深度融合的新物种。 2️⃣ 从零起步、原生围绕 AI 代理构建的新型机构或公司 许多现有公司在 AI 转型上会行动过慢，因此将出现一整批从一开始就围绕 AI 代理构建的全新公司，直接攫取效率红利。 这些服务型公司或代理机构会亲自使用 AI 技术，从而能够： * 以更低成本 * 更快速度 * 或更高质量 * 为更广泛的客户群提供服务，这是过去无法做到的。 具体表现为： * 使用 AI 重塑商业模式的新一代律所 * 能为中小企业提供高质量服务的营销机构 * 以更低成本承接更大项目的工程与研发公司等等 总结 总体来看，由于“与 AI 代理共事”的方式本身发生了根本变化，未来将涌现出大量全新的商业形态。 AI 代理不仅改变工具，更在重塑组织、行业结构和商业模型本身。 To get the full benefit of AI agents you often need to change your underlying workflows, and keep up with a very fast moving AI space. Because of this, there are at least 2 entirely new categories of business models that will emerge around the software companies that build agents. 1. The services firm that implements AI agents in existing companies. As enterprises look to deploy AI agents across all forms of work, it’s not possible for every company to figure out how to do this on their own. Most companies don’t have the IT teams to deliver on this, so there will be entirely new system integrators that emerge to help companies redesign their workflows, implement the tech, drive the change management, and keep the AI agents up to date for the organization. But what’s super interesting is that because AI agents span almost every single line of business, these will not just be the classic system integrators whose primary focus is on IT systems. The system integrators will have to be domain experts at many different types of job functions, from marketing and legal to healthcare and coding. 2. New agency or firm that forms from the ground up to take advantage of the leverage of agents. Lots of companies will take too long to transform themselves with AI, so there will be an all new crop of companies that start from scratch the capture the gains. These services firms and agencies will use the technology themselves to offer cheaper, faster, or better quality of service to a broader range of clients than was possible before. This will be the new law firm that uses AI to change the business model of, marketing agencies that can support high quality campaigns for smaller size companies, engineering shops that can take on bigger project work at a lower cost, and so on. In all there are going to be lots of new forms of businesses that will emerge as a result of AI agents because of how different working with agents can be.
Aaron LevieCo-founder & CEO of Box
目录
观点原文
要真正发挥 AI 代理（AI agents） 的全部价值，往往需要重构底层工作流程，并持续跟上一个高速演进的 AI 生态。正因如此，围绕构建 AI 代理的软件公司，至少会催生出 两类全新的商业模式。
1️⃣ 为企业实施 AI 代理的服务型公司
随着企业希望在各类工作中部署 AI 代理，不可能每家公司都自行摸索完成。
大多数企业并不具备足够的 IT 团队能力来交付这一转型，因此将会出现全新一代的系统集成商，专门帮助企业：
- 重构工作流程
- 实施 AI 技术
- 推动组织层面的变革管理
- 持续为企业维护与更新 AI 代理
真正有意思的是：由于 AI 代理几乎横跨所有业务线，这些新型系统集成商将不再只是传统以 IT 系统为核心的集成商。
他们必须在多种具体岗位职能上成为领域专家，覆盖从：
- 市场营销
- 法律
- 医疗健康
- 到软件开发与工程
换句话说，这是“业务能力 + AI 实施能力”深度融合的新物种。
2️⃣ 从零起步、原生围绕 AI 代理构建的新型机构或公司
许多现有公司在 AI 转型上会行动过慢，因此将出现一整批从一开始就围绕 AI 代理构建的全新公司，直接攫取效率红利。
这些服务型公司或代理机构会亲自使用 AI 技术，从而能够：
- 以更低成本
- 更快速度
- 或更高质量
- 为更广泛的客户群提供服务，这是过去无法做到的。
具体表现为：
- 使用 AI 重塑商业模式的新一代律所
- 能为中小企业提供高质量服务的营销机构
- 以更低成本承接更大项目的工程与研发公司等等
总结
总体来看，由于“与 AI 代理共事”的方式本身发生了根本变化，未来将涌现出大量全新的商业形态。
AI 代理不仅改变工具，更在重塑组织、行业结构和商业模型本身。
To get the full benefit of AI agents you often need to change your underlying workflows, and keep up with a very fast moving AI space. Because of this, there are at least 2 entirely new categories of business models that will emerge around the software companies that build agents.
- The services firm that implements AI agents in existing companies.
As enterprises look to deploy AI agents across all forms of work, it’s not possible for every company to figure out how to do this on their own.
Most companies don’t have the IT teams to deliver on this, so there will be entirely new system integrators that emerge to help companies redesign their workflows, implement the tech, drive the change management, and keep the AI agents up to date for the organization.
But what’s super interesting is that because AI agents span almost every single line of business, these will not just be the classic system integrators whose primary focus is on IT systems. The system integrators will have to be domain experts at many different types of job functions, from marketing and legal to healthcare and coding.
- New agency or firm that forms from the ground up to take advantage of the leverage of agents.
Lots of companies will take too long to transform themselves with AI, so there will be an all new crop of companies that start from scratch the capture the gains. These services firms and agencies will use the technology themselves to offer cheaper, faster, or better quality of service to a broader range of clients than was possible before.
This will be the new law firm that uses AI to change the business model of, marketing agencies that can support high quality campaigns for smaller size companies, engineering shops that can take on bigger project work at a lower cost, and so on.
In all there are going to be lots of new forms of businesses that will emerge as a result of AI agents because of how different working with agents can be.
ABAB AI 解读
这段话的本质，不是在讲“AI 会带来新公司”，而是在揭示一次类似工业革命级别的“组织形态重构”。我从历史—金融—产业结构三个最高层次来解读。
一、这是一次“工作组织方式”的断裂，不是技术升级
绝大多数人低估了这一点。
历史上真正改变世界的技术，并不是“效率工具”，而是改变了“工作如何被组织”的技术：
- 蒸汽机 → 工厂制度
- 电力 → 流水线与规模化企业
- 互联网 → 平台公司与外包全球化
- AI agents → 工作被“模块化 + 代理化”
AI agent 的核心不是“自动化一个任务”，而是：把“人类岗位”拆解为可由多个智能体协同完成的流程网络。
一旦你接受这一点，就会明白：👉 原有组织结构天然失效。
二、为什么会出现“第一类新物种”：AI 系统集成商（但不是你以为的那种）
作者说的第一类公司，本质是：“组织重构承包商”、而不是 IT 外包。
关键认知点（很多人没意识到）：AI agent 的部署 ≠ 软件安装。真正难的是：
- 谁该被 agent 替代
- 谁该与 agent 协作
- 决策权如何重新分配
- 风险与责任如何重新定义
这在金融史上非常熟悉—— 👉 这类似 ERP 时代，但复杂度高一个数量级。
为什么传统咨询公司 / SI 不够用？
因为这次不是：“IT + 流程”
而是：“岗位本身 + 判断逻辑 + 专业责任”
所以新一代集成商必须是：
- 半个律师
- 半个医生
- 半个工程师
- 半个产品经理
- 再加上 AI 架构师
👉 这是一个高门槛、强护城河、极易形成寡头的赛道。
三、第二类公司更重要：这是“AI 原生职业公司”的诞生
这是整段话里真正的炸点。
作者在说一句很重的话：不是所有公司都值得被 AI 改造。很多公司会被直接绕过。
历史类比非常清楚：不是每一家手工业作坊升级成工厂、而是新工厂直接碾压旧作坊
这些 AI 原生公司有什么本质不同？
不是“用了 AI”，而是：人从一开始就不是核心产能
核心单位是：
- agent
- agent 编排
- agent 监督
人类角色是：
- 定义目标
- 审核异常
- 承担法律/伦理/最终责任
结果是：
一个“AI 律所” → 可以服务过去 10 倍的客户
一个“AI 营销机构” → 可以把高端服务下沉到中小企业
一个“AI 工程公司” → 可以承接过去根本不可能的规模
👉 这是生产函数的改变，不是效率优化。
四、从金融与资本角度看：谁会赢，谁会死？输家是谁？
- 转型慢的传统服务公司
- 组织臃肿、靠人工堆规模的企业
- 管理层只把 AI 当“降本工具”的公司
真正的赢家是谁？
- AI 原生服务公司
- 毛利高
- 人效极高
- 极易形成网络效应
- 掌握 agent 编排与标准的公司
- 类似“新一代操作系统”
- 为 agent 时代提供合规、责任、保险、审计的新型金融/法律机构
👉 资本最终会追逐这些公司，而不是“会用点 AI 的旧公司”。
五、一句真正的“顶层结论”
AI agents 不会首先取代人，而是会先取代“以人为中心设计的组织结构”。而这，正是为什么：
- 会出现全新的公司形态
- 会出现全新的赢家
- 会让很多“今天看起来很稳的公司”突然变得不稳
AI
相关观点
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With all the examples we’ve seen most recently of AI coding agents being able to handle longer and more complex tasks, this has created a sort of existential dread in some around what the role of humans and existing skills will be in a world where AI can do almost anything. Even Boris Cherny, the creator of Claude Code, noted that 100% of his contributed code was written by Claude in the past 30 days in late December. And we’re seeing countless examples of engineers that are running up to dozens of agents in parallel, all handling different parts of a project, swiftly jumping between the work of each one, doing the equivalent of weeks of engineering work in hours or a couple of days. If AI can do all the coding for us, this begs the question, is there even a role for skilled engineers in the future? And more broadly, is there a role for anyone with deep domain expertise -whether it’s legal, marketing, architecture, or medical fields- in a future where AI can instantly do so much of what today’s jobs look like? The answer, emphatically, is yes. In fact, I’d argue, there has never been a time in history where it’s been better to be a highly skilled in a particular field. When you assess what an engineer does today, the process is far more complex than just writing code. You’re taking in requirements from the customer (or from a product manager, etc.), testing your code, integrating it with a broader project, ensuring that it ships properly, fixing future bugs and defining new features, handling issues as they arise, coming up with the next set of features to build based on customer problems, upgrading various libraries and packages for security issues and new dependencies, and dozens of other individual tasks that deliver real, working software. AI increasingly can do any single one of these tasks individually, but job of an engineer is putting all the work together, successfully. And those that understand how those tasks all come together are in the best position to deliver the most value, and get the greatest upside, with AI. This is why many of the best engineers you know are having the time of their life right now with AI coding. Because instead of having to spend a uniform amount of time on both the parts of coding that are value-add and differentiating from the stuff that’s rote, you can deploy tasks to agents that offload to the drudgery parts, freeing up more time for the work that is fun and delivers the real value. History doesn’t repeat itself, but it often rhymes Now, you may be wondering, this must fundamentally change the nature of work in a way that leaves less to be done by people, ultimately rendering skills useless, right? In fact, there’s already plenty of historical precedent for this evolution of work. If you look at the processes of any high-skill job starting from the pre-PC era to the AI era, nearly every single job would look fundamentally different today than it did just a few decades ago. There's a scene in the 1982 movie The Verdict where Paul Newman and Jack Warden's characters are combing through volumes of legal books for hours while doing research for an upcoming trial. You can just feel from watching the scene how long and tedious this process once was. Just a few years later, a lawyer would have pulled up a computer and retrieved the same data nearly instantly. Just as Sam Altman said a subsistence farmer might look at today’s jobs as way too easy and trivial, the lawyer from 1982 might imagine how easy law has become and devoid of critical thinking or research. And yet, the nature of the legal profession has only become more complex and sophisticated over time with a growing range of regulatory challenges, legal risks companies face, and so on. The foundational skills of the legal profession have been largely unchanged, and counterintuitively, despite many efficiency gains over time, the ABA pegs active attorneys having gone from roughly 400,000 in 1975 to roughly 1,375,000 in 2025. Or take that modern engineering job, right before the moment AI coding went mainstream. Many things have gotten simpler in the past few decades of coding: there are open source libraries you just pull down instantly for almost every problem some other engineer has once dealt with in the wild, and you no longer have to think about managing infrastructure, sharding databases, handling identity services, doing billing, or an array of tasks that every engineer had to do themselves just a couple decades back. And yet, we now have more software than ever before to manage and more complexity in our systems than ever before; and as a result, no less of a need for experts to help with all this work. What we know from history is that the jobs simply morph over time. We make one thing easier through automation or abundance, and as a result our expectations begin to grow on what we can now do in that space, and the skills become just as important. The expectations of what an engineer must deliver on today has just grown in scope, the demands of a lawyer’s advisory capabilities just increase, and the presentation and market knowledge you expect from your banker or consultant has gone up tenfold. Skills as leverage Max Levchin said it best just this week: “just as today we think of codegen AI as a 10x productivity multiplier for CS degrees (or equivalent), in a year or two we will be thinking of CS degrees as 10x productivity multipliers over codegen AI.” You can replace “CS” and “codegen” for any skill and see the same effect. The real leverage from AI comes from your depth of knowledge in the field you’re bringing automation to. One of the most important foundations for getting ahead with AI agents is being able to understand what tasks to give them, how to frame those tasks, how to know when those tasks aren’t being executed successfully, and then figuring out what to do *after* the AI has delivered the task. Critically, being able to hold a full mental model in your head of what the agent is doing, where it should go next, what “good” should look like is the critical judgment necessary to produce real value with agents vs. just use them to take shots in the dark. Without this underlying knowledge you’re going to have a very hard time producing ongoing value at any reasonable scale or frequency. The expert CG animator will know the right ways to get the AI to produce the best scenes, and then stitch them together the best to get emotion out of the audience; the expert lawyer knows when advice for a case would be impractical or when the AI should be overridden due to subtle nuance about a situation; the expert engineer knows when an agent has backed itself into an architectural dead-end that won’t scale for future features; and the expert marketer knows the message that will resonate with their core audience best. By today's standards, all of these jobs will look fundamentally different in the future. We'll barely recognize the things that the experts are typing into their machines to do their work, but all of their underlying knowledge and skills of what they are doing and why will be as necessary as ever. And instead of people and companies simply using AI agents to automate the work that we already do manually, jobs will simply be expanded over time to expect higher, or new types of output. We’ll demand far more from our software, we’ll expect more medical breakthroughs, require better expertise from our healthcare providers, and bar will just rise in essentially every other domain out there. As a minor example of this dynamic, at Box, we now have solutions engineers (the deeply technical counterparts to our sales team) build fully custom, working demos of Box’s APIs embedded into our customer’s software. Instead of a static, generic demo, they can now bring the full platform to life in a way that would have been cost prohibitive to demonstrate for every client before. But now it’s a quick task. As this trend propagates across the market, customers will soon expect similarly customized experiences when they’re pitched by software companies. Just at the same moment when the rote task of pitches or architectures gets automated by AI, a new set of more creative and elaborate tasks become required as a part of a solution engineer’s job. And here’s the amazing thing with AI. Just as it makes the experts far more capable than before, it also makes it so anyone can learn to become an expert much faster. Dylan Field frames this as AI lowering the floor and raising the ceiling. In his case, it means anyone who’s motivated can now learn how to do amazing design if they want. The tools that every young person now has available to them is unprecedented. Anyone, if they so choose, has a complete tutor available to them in every skill on the planet. When I was growing up and getting into building websites, it was only by sheer coincidence that my neighbor (and ultimately a Box cofounder) happened to be a programmer that could help multiply my skills; but now everyone has that neighbor available to them 24/7. Right now, the next Zuckerberg is getting into coding for the first time because of AI. The next George Lucas is someone developing AI worlds that have never been seen before, and producing a level of entertainment that would not have been possible even a couple of years ago without tens of millions of dollars. The same will be true in life sciences, finance, and every other technical field out there. This is the greatest time in history if you’re curious, resourceful, and are fundamentally want to learn and pick up new skills. Skills don’t go away in the 21st century with AI. The leverage you get from them have only gone up.Aaron Levie · 2026/01/11
- 当下，对任何具备创业精神的人来说，在自己所在公司里最大的机会之一，就是成为那个帮助组织理解并部署 AI 智能体（AI agents）的人。 部署智能体并不容易。目前真正掌握这项能力的人仍然非常有限。虽然几乎所有最佳实践都能在网上找到，但前提是——你必须持续、高强度地关注这个领域的每一个新进展：包括编程型智能体、记忆机制、文件系统与工具调用、Agent 框架（agent harnesses）、上下文工程（context engineering）等等。 更重要的是，这个领域的最佳实践会不断复利式叠加。这意味着：你领先得越早，就越容易理解并部署下一波新出现的能力。 如果你能做到这一点，那么在你加入的任何一家公司里，你都会看起来像是拥有一台时间机器的人。 这是一个巨大的机会。 One of the biggest opportunities available to anyone entrepreneurial right now in their company is to be the person that helps their organization understand and deploy AI agents. Deploying agents is hard. There is still a relatively narrow universe of people with this skill. While all of the best practices are available online, you have to be paying attention to every new thing that comes out around coding agents, memory, filesystem and tool use, agent harnesses, context engineering, and so on. And the best practices in this space just continue to compound, which means the farther ahead you are the more you understand about how to deploy what comes up next. So if you can do that, you will look like you have a time machine to any company you join. Huge opportunity.Aaron Levie · 2026/01/07
- 上下文时代 彼得·德鲁克（Peter Drucker）——管理与商业领域最重要的思想家之一（也是“知识工作者”一词的提出者）——在 1990 年代就指出：“知识已经成为关键的经济资源，并且是占主导地位、甚至可能是唯一的竞争优势来源。”他对 AI 时代的预判，几乎精准到不可思议。 随着 AI 进步持续不减，AI 模型将越来越能够增强或自动化各种知识工作者的任务。正如我们在编程领域看到的那样，这些模型将演化为 AI 代理：在几乎所有工作领域中，成为专家级律师、医疗专业人士、商业战略家、科学研究者等。 但默认情况下，这些模型并不了解你的具体团队或组织。它们可能瞬间被要求为 福特审查法律合同，下一秒又为 高盛编写新软件代码。它们是完全通用的超级智能系统，可以为任何提出请求的人执行任何任务。其结果是：你的公司与另一家公司拿到的是同一个“专家律师”、同一个“工程师”。 因此，我们必须面对的问题是：在人人都能获得同等智能的世界里，公司如何实现差异化？ 当然，差异化的一部分来自团队和员工如何高效使用 AI 代理；但真正的终极放大器将是代理所获得的上下文（context）。这些上下文包括：要打造的正确产品、服务客户的方式、要进入的市场、客户互动的具体细节、组织内部的“部落知识”，以及随着时间积累、授权或并购而形成的、只属于你公司的大量专有信息。 过去一年，“上下文工程（context engineering）”为解决这一问题而迅速发展，但它并不容易。想象一下：一个默认对你组织一无所知的专家律师或工程师，你却只有一份文档的空间来向他说明其全部工作内容、可使用的系统、所需数据、目标等等。把正确的信息交到代理手中，成为提升生产力的关键前提。 事实上，这个问题在 AI 代理出现之前就困扰着企业。公司一直是各种上下文的集合体，试图从中榨取最大价值：流程、知识产权、独特想法与路线图、决策方式、客户信息等等。然而，随着规模扩大，企业只能利用其中很小一部分知识（无论是否已数字化）。惠普（HP） 90 年代的 CEO 卢·普拉特（Lew Platt） 曾说：“如果 HP 知道 HP 所知道的一切，我们的生产力将提升三倍。” AI 代理终于让这一愿景成为可能。我们第一次能够真正动用沉睡在组织内部的知识与信息。Atlassian 的 迈克·坎农-布鲁克斯（Mike Cannon-Brookes） 将其形容为一种“数据的梅特卡夫定律”：就像网络效应一样，数据越多，整体系统越强。 这意味着，在 21 世纪，最关键的竞争优势之一，将是企业捕获、管理并围绕正确上下文构建流程的能力。 拥有更好市场定价与租金洞察的房地产公司会赢得更多客户；能利用海量数据开发新药的制药公司会创造更多收入；能产出更好营销活动的代理机构会更具差异化。 然而，把正确的数据（上下文）交给代理，在规模化时是个难题。正如 Foundation Capital 的 贾娅·古普塔（Jaya Gupta） 和 阿舒·加格（Ashu Garg） 在其关于上下文图谱的犀利文章中所指出的那样，许多 AI 代理所需的关键决策轨迹，并不存在于任何现有软件中。而这只是代理所需上下文的一种：它们还需要客户数据、企业文档、对话历史、项目时间线、研究数据、营销素材、财务记录、代码库、HR 信息等等。 因此，设计系统以让代理访问这些数据，并确保所有代理能在这些数据上互操作，至关重要。在一些情况下，现有的系统将成为这些数据与代理的合理“归宿”；而在另一些情况下——尤其是当今天还不存在相应软件时——我们将需要全新的工具。即便如此，未来十年里，数据访问控制与权限冲突、治理与合规、以及连接分散的企业系统，都将是持续的工作。 此外，企业还必须推进大量变革管理。我们曾以为 AI 会适应我们的工作方式，但事实证明，鉴于其强大的能力与内在限制，我们反而需要去适应 AI 的工作方式。这意味着必须优化组织与工作流，以便为代理提供成功所需的上下文。其核心原则是：用户现在要负责指挥和引导代理的工作，并确保在过程中提供正确的上下文。 本质上，当下的个人贡献者，将在未来成为“代理的管理者”。他们的新职责包括：监督与升级路径、跨代理的协调，以及在不同代理之间“护送”工作——就像前 AI 时代的团队管理者一样。 总体趋势已经十分清晰：我们正进入“上下文时代”。 能够积累并最好地利用上下文的团队与公司，将实现最高的生产力与产出；做不到的，将越来越难以具有竞争力地服务客户。正如彼得·德鲁克当年所预言的那样。 The Era of Context Peter Drucker, one of the most important thinkers on management and business (and coiner of the term knowledge worker), in the 90s said, "knowledge has become the key economic resource and the dominant-and perhaps even the only-source of competitive advantage." He couldn’t have predicted the AI era more accurately. As AI progress continues unabated, AI models will get more and more capable of augmenting or automating a variety of knowledge worker tasks. Just as we've seen in coding, these models will morph into AI agents that are expert lawyers, healthcare professionals, business strategists, scientific researchers, or other roles in almost every domain of work. But these models, by default, don't know anything about your particular team or organization. In an instant, they might be asked to review a legal contract for Ford, and then in the next second be writing code for a new piece of software at Goldman Sachs. They are fully general-purpose superintelligence systems that can take on any task for anyone that’s asking. As a consequence, that means that your company is getting the same expert lawyer as another company, the same engineer, and so on. The question that we will have to wrestle with is, in a world where everyone has access to the same intelligence, how does a company differentiate? Certainly it will be about how teams and employees use AI agents effectively, but the ultimate force-multiplier will be the context that the agents get. Context about the right products to build, the ways to serve customers, the markets to go after, the specific details of client interactions, the tribal knowledge in an organization, and endless amounts of other proprietary information to your company that's developed over time, licensed, or acquired. Context engineering has blown up over the past year to tackle just this, and it's not an easy problem. Just imagine taking an expert lawyer or engineer that by default knows absolutely nothing about your organization, and you only have a single document’s worth of space to describes their entire job, every system they have to leverage, all the data that they are supposed to work with for that particular task, what their objectives are, and so on. Getting the right information to them becomes a critically important requirement to drive their productivity. Now, this problem has already plagued companies well before agents existed. Companies have always been a collection of various forms of context that they try and extract the most value out of: their processes, intellectual property, unique ideas and roadmap, ways they makes decisions, information about their customers, and so on. And companies, as they scale, have only been able to utilize a small portion of this knowledge (digitized or not). Lew Platt, the CEO of HP in the 90s, once said, “if HP knew what HP knows, we would be three times more productive.” AI Agents finally make this vision possible. We can, for the first time, begin to tap into this wealth of knowledge and information sitting inside of our organizations. Mike Cannon-Brookes of Atlassian once framed this as a sort of a Metcalfe's law of data. Like the original theory on network effects, the more data you have, the more powerful the overall system becomes. This means that in the 21st century, one of the most critical forms of competitive advantage will be a company's ability to capture, manage, and build processes around the right context. The real estate firm that has better insights on market pricing and rent dynamics will get more clients, the pharmaceutical company that can develop and design new drugs using reams of data will generate more revenue, the marketing agency that can generate better campaigns will differentiate more. Now, getting the right data (or context) to agents is a hard problem at scale. As Jaya Gupta and Ashu Garg at Foundation Capital pointed out in their provocative essay on context graphs, many of the critical decision traces for AI agents to operate with either aren't in any existing software today. And this is just one example of the kind of context that agents will need to operate on. They’ll need customer data, enterprise documents, conversation history, project timelines, research data, marketing assets, financial records, codebases, HR information, and much more. Designing our systems to get agents access to that data, and ensuring that all of our agents can interoperate on that data is going to be incredibly important. In some cases existing systems of record will become the logical houses for that data and the corresponding agents; and in other cases -especially where there is no such software today- we will need all new tools, just as Jaya and Ashu laid out. Even then, managing the myriad conflicts that emerge in data access controls and permissions so agents don’t leak data, governance and compliance challenges around what things an agent can execute on, and connecting our disparate enterprise systems to make this work is going to be an ongoing work for the next decade. Further, companies will have to drive a substantial amount of change management to make this all work. We imagined that AI systems would adapt to how we work, but it turns out due to their extreme power (and inherent limitations) we will instead adapt to how they work. This means we will have to optimize our organizations and workflows to best enable context for agents to be successful. The core tenant of this change is that the user is responsible now for directing and guiding agents on how to do their work, ensuring it gets the right context along the way. In essence, the individual contributor of today becomes the manager of agents in the future. Their new responsibilities will be providing the oversight and escalation paths, a meaningful amount of coordination throughout the work that the agents are doing, and shepherding work between the various agents - just like managers of teams in the pre-AI era. The overall trend is clear, and we are entering the era of context. The teams and companies that can accumulate and best utilize context will drive the greatest productivity and highest output. Those that don’t will find it harder and harder to serve customers competitively. Just as Peter Drucker predicted.Aaron Levie · 2026/01/05
- 这就是为什么上下文工程（context engineering）如此重要。 我们刚刚花了 2 个小时，仅仅围绕一种问题类型，讨论一个智能体究竟应该依赖自身的内部知识，还是尝试在数据中寻找相关上下文。而在数百个测试用例中，我们只讨论完了 2 个。 甚至连参与这次头脑风暴的人，也无法就“在这种情况下，人类会怎么做”达成一致。这里根本不存在唯一正确的答案，而且答案始终是高度依赖具体情境、并且因客户而异的。 上下文工程中的一切，都是在多个维度之间做取舍： 你希望智能体多快给出答案？ 你希望用户需要多少来回交互？ 在尝试回答问题之前，它应该先做多少准备工作？ 它如何判断自己已经拥有足够完整的来源材料来回答问题？ 给出错误答案的风险有多高？等等。 你在其中任何一个维度上做出的决定，都会在另一个维度上产生后果。没有免费的午餐。这正是为什么构建 AI 智能体如此疯狂、也如此困难。 这也凸显了一个事实：在 LLM 之上，仍然蕴藏着巨大的价值空间。把这些决策做对，直接决定了产品价值主张的质量。 Here’s why context engineering is such a big deal. We just spent 2 hours debating when an agent should rely on its internal knowledge vs. trying to find relevant context within data for just one type of question. We got through 2 test cases of hundreds. Even the people involved in the brainstorm couldn’t all agree on what they would expect humans to do in this situation. There truly was no right answer, and it’s always context specific customer by customer. Everything in context engineering is a tradeoff between a variety of factors: how fast do you want the agent to answer a question, how much back and forth interaction do you want to require for the user, how much work should it do before trying to answer a question, how does it know it has the exhaustive source material to answer the question, what’s the risk level of the wrong answer, and so on. Every decision you make on one of these dimensions has a consequence on the other end. There’s no free lunch. This is why building AI agents is so wild. It also highlights how much value there is above the LLM layer. Getting these decisions right directly relates to the quality of the value proposition.Aaron Levie · 2025/12/24