仅依赖通用模型无法形成护城河，企业需构建专有数据管道、定制化评估体系与闭环优化机制。 通过自主管理上下文窗口、反馈数据与模型微调，企业可确保AI系统持续适配业务需求并降低对第三方API的依赖。
在接下来的五年里，每家公司都会以两种方式之一使用人工智能：用于运营其关键业务部分，或作为他们向客户销售产品的一部分。在这两种情况下，通用智能都不足以应付需求。
要产生真正的影响，公司需要拥有自己的智能。拥有并不意味着从零开始构建每一层。它意味着控制那些决定智能如何表现、如何管理以及是否会随时间增长的部分。
现成的人工智能之所以有用，是因为它具有通用性。它可以回答常见问题，并在广泛的主题上进行推理。这使它在入门阶段很有帮助。但假设通用智能可以在公司内部运营业务，将是一种极度简化的看法。
公司在运营方式上有许多具体的细节。AI嵌入公司越深，这些细节就越重要。如果你希望将AI用作公司运营的核心部分，这些细节就非常重要。
举一个具体例子，假设一家大型保险公司使用AI辅助处理理赔。通用模型可以阅读保单文件，总结理赔内容，并解释常见的保险覆盖概念。但实际上处理理赔工作取决于保险公司自身的保单条款、各州的监管要求、欺诈信号、历史理赔模式、升级规则、客户分层以及风险承受能力。这些细节不会包含在通用模型的权重中。通用模型知道“免赔额”这个词的意思。但它不知道如何在特定司法管辖区、针对特定客户、根据特定保单、使用特定证据处理某种类型的理赔。
同样的逻辑也适用于AI本身就是产品的情况。如果你是一家垂直AI创业公司，构建AI支持代理（https://www.youtube.com/watch?v=uCKhOmth2ms）、法律助理或编码代理（https://www.youtube.com/watch?v=HbUznYhKFOc）——基础模型本身不是你的产品。其他人也可以调用同样的API。你的产品是围绕模型构建的系统：它处理的工作流、检索的上下文、可使用的工具、定义质量的评估、构建的记忆，以及通过与每个客户互动持续改进的反馈循环。
在这两种情况下，优势不在于通用智能，而在于适应特定业务的智能。
任何公司都可以使用人工智能。但这种通用智能不足以推动有意义的投资回报率或建立差异化的产品。以人工智能为核心建立的公司将拥有自己的智能。
拥有你的智能并不意味着自己构建人工智能堆栈的每一层。它意味着控制关键部分——决定智能如何表现、从何处学习、成本多少以及是否随时间改善的部分。
一个有用的类比是：供应链。零售商不需要制造其使用的每辆卡车、游轮或仓库机器人。但它确实需要拥有决定采购什么、库存如何移动、需求如何预测以及客户体验如何交付的系统。
人工智能也是这样运作的。你可以购买模型、计算能力和基础设施。但你需要拥有将这些输入转化为针对你业务的智能的系统。
这有三个部分：控制代理系统；拥有经济、质量和风险；以及随时间积累智能。
代理是原始智能与工作的桥梁。模型提供智能。控制逻辑决定如何应用该智能。上下文告诉系统在特定情境中什么是重要的。
第二层是控制逻辑：将模型输出转化为行动的编排逻辑。控制逻辑管理上下文的使用方式：https://www.langchain.com/blog/context-engineering-for-agents：路由、工具使用、工作流程步骤、技能等等。这是公司特定行为的大部分所在。如果控制逻辑是封闭的，你就是在接受别人对你的代理应如何工作的假设。
第三层是上下文本身。上下文使通用智能具体化：文档、政策、工具、技能、用户偏好、组织级知识和记忆。记忆尤其重要，因为这是系统随时间变得更有用的方式：https://www.langchain.com/blog/how-to-give-your-agent-memory。
如果你不拥有上下文和记忆：https://www.langchain.com/blog/your-harness-your-memory，你就不拥有系统积累的智能。
要拥有代理系统，你需要控制三个方面：模型、框架和上下文。
一旦 AI 开始执行实际工作，公司就需要像管理任何其他操作系统一样管理它。这意味着要控制工作的成本，衡量其质量：https://docs.langchain.com/langsmith/evaluation-concepts，定义系统可以行动的范围，并了解其执行时发生的事情。
成本很重要，因为只有在相对于其产生的回报来说便宜时，智能才有价值。随着 AI 采用的快速增加，成本也随之增加——以 Uber 的警示案例为例：https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-burns-its-2026-ai-budget-in-four-months-on-claude-code/
能够锁定成本：https://www.langchain.com/blog/building-governed-agents-a-framework-for-cost-control-and-compliance — 无论是在用户层面、组织层面还是代理层面——对于可靠地扩展 AI 至关重要。
质量需要被衡量，而不是假设的。如果你升级模型、更改提示、添加工具或调整工作流，你需要知道系统是否变得更好或退步。如果你无法衡量这一点，你就无法系统地改进代理，这意味着你无法真正控制和拥有它。
边界定义 AI 可以独立行动的范围以及需要监督的地方。公司需要控制代理可以访问的数据、可以使用的工具、哪些操作需要审批，以及何时应升级问题。拥有你的智能意味着控制它能做什么。
可观测性使系统有责任可循：https://www.langchain.com/blog/on-agent-frameworks-and-agent-observability。如果代理采取了某个动作，企业需要看到它看到了什么，做了什么，调用了哪些工具，以及原因。这对于既能够改进系统，也能够审计和信任系统都很重要。
最好的 AI 系统会随着使用而变得更好。第 100 次交互应该比第一次更有价值，因为系统已经更多地了解了你的用户、工作流、策略、失败和偏好。你需要拥有系统所学到的内容，以及学习过程本身。
系统学习意味着什么？
这个学习循环从追踪开始。追踪显示了代理实际做了什么：https://www.langchain.com/blog/in-software-the-code-documents-the-app-in-ai-the-traces-do：它看到了什么上下文、调用了哪些工具、卡在哪里了，以及产生了什么结果。反馈赋予这些追踪以意义：行为是否有用、被接受、被拒绝、效率低下、有风险或错误。追踪和反馈一起成为改进的原材料：https://www.langchain.com/blog/improving-agents-is-a-data-mining-problem。
这些追踪及其相关的反馈可以用来改进系统——通过改变提示、框架和工具。但是，和这些变化同样重要的是与这些变化相关的评估。对于每一次变化，你都应该增加评估以捕捉该变化，并确保未来的变化不会导致回滚。
至少，这些学习成果需要由你来拥有。它们需要能够从一个模型迁移到另一个模型，从一个系统迁移到另一个系统。更重要的是——你应该自己拥有这个完整的循环。再次强调——代理第一次被使用的表现其实并不重要。重要的是第100次使用时它有多好。如果这一点重要，那么你就需要掌握驱动这一点的循环。
你是否真正拥有你的智能？以下是一组你应该问自己的问题，以确定这一点：
[ ] 如果一个全新的模型提供商明天推出一个最先进的模型，你能轻松切换吗？
[ ] 如果你正在使用的模型提供商弃用了某个模型——你能自己托管它以避免任何中断吗？
[ ] 你是否了解——并能控制——编排逻辑，这样你就能确切知道并控制每一步输入到大语言模型中的内容？
[ ] 你能否在不同的云环境之间或在设备上，以完全相同的方式运行你的代理？
[ ] 你能否按用户为单位监控和控制 AI 支出？
[ ] 如果有人问为什么代理做了某件事，你能向他们展示所有步骤的完整追踪（以及他们为什么这样做的任何内部逻辑）吗？
[ ] 你是否有评估机制以确保更换模型不会引入回滚？
[ ] 当用户第100次使用你的代理时，它会比第一次使用时更了解他们，并提供更好的体验吗？
[ ] 当你的代理学习时——你能把这些学习成果移植到完全不同的系统吗？
[ ] 你能控制你的代理如何学习吗？
公司不需要构建AI堆栈的每一层。他们应该购买那些困难的、通用的、无差异的部分。
但他们需要掌握能带来优势积累的层：代理系统、其所做工作的治理，以及通过使用使其变得更好的反馈循环。
任何公司都可以采用AI。那些通过AI创造持久价值的公司将是拥有自己智能的公司。
我们的代理工程平台LangSmith帮助开发者调试每一个代理决策、评估更改，并一键部署。
Over the next five years, every company will use AI in one of two ways: to run critical parts of their business, or as part of the product they sell to customers. In both cases, generic intelligence will not be enough.
To have real impact, companies need to own their intelligence. Ownership does not mean building every layer from scratch. It means controlling the parts that determine how intelligence behaves, how it’s managed, and whether it compounds over time.
Off-the-shelf AI is useful because it is general. It can answer common questions and reason across a broad set of topics. This makes it helpful to get started. But to assume that generic intelligence can run operations inside a company would be a gross simplification.
Companies have MANY specifics in the way they operate. The deeper you embed AI into your company, the more these specifics matter. If you are hoping to use AI as a core part of operating your company, these specifics matter a lot.
As a concrete example, let’s consider a large insurer using AI to help process claims. A generic model can read a policy document, summarize a claim, and explain common coverage concepts. But to actually do the work to handle a claim depends on the insurer’s own policy language, state-by-state regulatory requirements, fraud signals, historical claim patterns, escalation rules, customer tiering, and risk tolerance. These specifics will not be in a generic models weights. A generic model knows the meaning of the word “deductible”. But it does not know how to handle a specific type of claim, for this customer, in this jurisdiction, under this policy, with this evidence.
The same logic applies when AI is the product itself. If you are a vertical AI startup building an AI support agent：https://www.youtube.com/watch?v=uCKhOmth2ms, legal assistant, or coding agent：https://www.youtube.com/watch?v=HbUznYhKFOc - the base model is not your product. Everyone else can call the same API. Your product is the system around the model: the workflows it handles, the context it retrieves, the tools it can use, the evals that define quality, the memory it builds, and the feedback loop that improves it with every customer interaction.
In both cases, the advantage is not generic intelligence. It is intelligence adapted to a specific business.
Any company can use AI. But this generic intelligence will not be enough to drive meaningful ROI or build a differentiated product. Companies built around AI will own their intelligence.
Owning your intelligence does not mean building every layer of the AI stack yourself. It means controlling the critical parts - the parts that determine how intelligence behaves, what it learns from, how much it costs, and whether it improves over time.
A useful analogy here: the supply chain. A retailer does not need to manufacture every truck, ship, or warehouse robot it uses. But it does need to own the system that determines what gets sourced, how inventory moves, how demand is forecast, and how the customer experience is delivered.
AI works the same way. You can buy models, compute, and infrastructure. But you need to own the system that turns those inputs into intelligence specific to your business.
There are three parts to this: controlling the agent system; owning the economics, quality and risk; and compounding the intelligence over time.
An agent is the bridge between raw intelligence and work. The model provides the intelligence. The harness determines how that intelligence is applied. Context tells the system what matters in a specific situation.
The second layer is the harness: the orchestration logic that turns model outputs into actions. The harness controls everything around how context is used：https://www.langchain.com/blog/context-engineering-for-agents: routing, tool use, workflow steps, skills, and more. This is where much of the company-specific behavior lives. If the harness is closed, you are accepting someone else’s assumptions about how your agent should work.
The third layer is the context itself. Context is what makes generic intelligence specific: documents, policies, tools, skills, user preferences, organization-level knowledge, and memory. Memory is especially important because it is how the system becomes more useful：https://www.langchain.com/blog/how-to-give-your-agent-memory over time.
If you do not own context and memory：https://www.langchain.com/blog/your-harness-your-memory, you do not own the intelligence your system accumulates.
To own the agent system, you need control over all three: the model, the harness, and the context.
Once AI is doing real work, companies need to manage it like any other operating system. That means controlling the cost of the work, measuring its quality：https://docs.langchain.com/langsmith/evaluation-concepts, defining where the system can act, and knowing what happened when it does.
Cost matters because intelligence is only valuable if it is cheap relative to the return it generates. As AI adoption has increased rapidly, so have costs - see Uber’s cautionary tale：https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-burns-its-2026-ai-budget-in-four-months-on-claude-code/ as an example.
Being able to lock down costs：https://www.langchain.com/blog/building-governed-agents-a-framework-for-cost-control-and-compliance - on a user, orgs, or agent level - is crucial to being able to scale AI reliably.
Quality needs to be measured, not assumed. If you upgrade a model, change a prompt, add a tool, or adjust a workflow, you need to know whether the system got better or regressed. If you are unable to measure this, you are unable to improve your agent systematically, which means you are unable to truly control and own it.
Boundaries define where AI can act independently and where it needs supervision. Companies need to control what data agents can access, what tools they can use, which actions require approval, and when they should escalate. Owning your intelligence means controlling what it can do.
Observability makes the system accountable：https://www.langchain.com/blog/on-agent-frameworks-and-agent-observability. If an agent takes an action, the business needs to see what it saw, what it did, what tools it called, and why. This is important for both being able to improve the system but also being able to audit and trust the system.
The best AI systems get better with use. The 100th interaction should be more valuable than the 1st because the system has learned more about your users, workflows, policies, failures, and preferences. You need to own both what the system has learned, as well as the learning process itself.
What does it mean for a system to learn?
That learning loop starts with traces. Traces show what the agent actually did：https://www.langchain.com/blog/in-software-the-code-documents-the-app-in-ai-the-traces-do: what context it saw, what tools it called, where it got stuck, and what it produced. Feedback gives those traces meaning: whether the behavior was useful, accepted, rejected, inefficient, risky, or wrong. Together, traces and feedback become the raw material for improvement：https://www.langchain.com/blog/improving-agents-is-a-data-mining-problem.
Those traces and their associated feedback can be used to improve the system - by changing the prompts, harness, tools. But just as important as those changes are the evals associated with those changes. For every change, you should be adding an eval to capture that change, and make sure that future changes don’t cause regressions.
At a bare minimum, these learnings need to be owned by you. They need to be portable from from model to model, from system to system. Even more - you should own this entire loop yourself. Again - it doesn’t really matter how well the agent does the first time someone uses. It matters how much better it is the 100th time they use it. If that’s what matters, then you need to own the loop that drives that.
Do you truly own your intelligence? Here’s a set of questions you should be asking yourself to determine that:
[ ] If a brand new model provider launched a SOTA model tomorrow, could you easily switch to it?
[ ] If the model provider you are using deprecates a model - could you host it yourself to avoid any disruption?
[ ] Do you know - and can control - how the orchestration logic works, such that you can know and control exactly what goes into the LLM at every step?
[ ] Can you run your agent in the exact same way in one cloud vs another vs on device?
[ ] Can you monitor and control AI spend on per user basis?
[ ] If someone asks why an agent did something, can you show them a full trace of all the steps they did (and any internal logic for why they did that?)
[ ] Do you have evals in place to be confident that swapping models won’t introduce regressions?
[ ] When a user uses your agent for the 100th time, will it know more about them and be a better experience than when they used it for the 1st time?
[ ] When your agent learns - can you port those learnings to a different system entirely?
[ ] Can you control how your agent learns?
Companies do not need to build every layer of the AI stack. They should buy the parts that are hard, generic, and undifferentiated.
But they need to own the layer where advantage compounds: the agent system, the governance around the work it does, and the feedback loop that makes it better with use.
Any company can adopt AI. The companies that create lasting value with AI will be the ones that own their intelligence.
LangSmith, our agent engineering platform, helps developers debug every agent decision, eval changes, and deploy in one click.
扫码直达文章详情