The past couple months we may be witnessing what the Applied AI layer will look like at scale. Despite some of the initial critique that this would just be a thin layer on the LLM, it’s turning out that actually driving agentic workflows in an enterprise is far more complex. And anywhere there’s complexity you generally gain a moat and value over time. Here are a few of the components that appear to make up the playbook based on the examples we’re collectively seeing in coding, legal, healthcare, customer support, financial services and other fields: * Build the features that bridge the gap between the intelligence and the workflow. Some workflows can be automated by simply going to a general purpose interface, but others need tuned interfaces and features tied to the work they’re augmenting or automating. They need features that are specific to capturing the kind of data that’s needed as context for the agent. And they need a variety of bespoke tools for the agent to use, and unique interfaces for the human-in-the-loop UX. Going far deeper than just presenting the output tokens is clearly critical, and the more depth there is here definitionally the more sustaining value. * Act as the model router balancing frontier intelligence with cheaper models. A natural advantage that any model neutral platform has is that it can naturally (in a business model-aligned way) leverage whatever level of intelligence is necessary for the workflows they’re automating to get done. There are plenty of scenarios where you need GPT-5.5 or Fable level capability, and also lots of workloads where a more efficient closed or open weights do the trick. Only the companies that have deep evals on specific tasks across all models, and the ability business model wise to leverage them, are in a great position. * Drive the actual implementation and change management via FDE or equivalent. A big reason the applied layer works at scale is that most enterprises need some degree of help and support with change management in implementing agents for their workflows. Data has to be cleaned up and moved to modern systems, processes have to be re-engineered and documented, workflows have to be evaled, SLAs have to get achieved, and so on. All of this is going to be unique for every type of process that gets implemented, which means the companies that have expertise in a given domain and come with all the relevant best practices will be in a strong position. * Implement domain specific GTM that creates expertise in that field. Beyond FDEs the companies that can build sales and GTM motions aligned to their domains also have a natural advantage. Most IT and line of business leaders have too many things to do in any given day; so if you’re not on their agenda, likely someone else is. Depending on the industry, there are entirely different sets of language you use, ways of working through security and compliance, regulatory controls you have to support, industry events that companies convene at, different system integrator and consulting partners you need to work with, and so on. The more generalized this gets the less you can speak the customers language, which is where the applied layer has a leg up. A final note. There remains a view that a lot of this is all mitigated by model intelligence alone, and the bitter lesson solves all of this in the limit. That’s possibly true, but enterprises need help changing *today*. And many aspects of how to bring intelligence to real world work don’t only depend on the axis of the pure capability of the model, so most of what you’re doing now to win ends up being important no matter how good the models get.
过去几个月里，我们或许正在见证大规模 Applied AI（应用层 AI）会是什么样子。尽管一开始有一些批评认为，这不过是叠在 LLM 之上的一层很薄的外壳，但事实证明，要真正把 agentic workflows（agent 驱动的工作流）推进到企业环境中，复杂得多。而凡是存在复杂性的地方，通常就会随着时间形成 moat（护城河）和价值。基于我们在 coding、legal、healthcare、customer support、financial services 及其他领域共同看到的案例，下面是一些似乎正在构成这套 playbook（方法论）的组件：* 构建能够弥合 intelligence 与 workflow 之间差距的功能。有些工作流只需要进入一个通用界面就能自动化，但另一些则需要经过调优的界面，以及与其所增强或自动化的工作紧密绑定的功能。它们需要专门用于采集 agent 所需上下文数据的功能；也需要各种定制工具供 agent 使用，以及面向 human-in-the-loop UX 的独特界面。显然，仅仅呈现输出 token 远远不够，必须做得更深；而这里做得越深，从定义上讲，可持续的价值也就越大。* 充当 model router（模型路由器），在前沿 intelligence 与更便宜的模型之间做平衡。任何 model neutral platform（模型中立平台）的一个天然优势在于，它可以很自然地——并且以符合其商业模式的方式——调用完成其所自动化工作流所需的 intelligence 水平。有很多场景确实需要 GPT-5.5 或 Fable 级别的能力，但也有大量工作负载使用更高效的 closed 或 open weights 模型就足够了。只有那些对所有模型在具体任务上做了深入 evals（评测），并且在商业模式上也有能力去灵活利用它们的公司，才真正处于有利位置。* 通过 FDE 或同类角色推动实际实施与 change management（变更管理）。应用层之所以能够大规模发挥作用，一个重要原因在于，大多数企业在为其工作流部署 agents 时，都需要某种程度的变更管理帮助与支持。数据必须被清洗并迁移到现代系统中；流程必须被重新设计并文档化；工作流必须经过 eval；SLA 必须达成，等等。所有这些对每一种被实施的流程都会是独特的，这意味着，那些在特定 domain（领域）中拥有专业能力，并且自带相关最佳实践的公司，将处于强势位置。* 落地 domain specific GTM（领域特定的 Go-To-Market），从而在该领域形成专长。除了 FDE 之外，那些能够建立与其所在领域相匹配的销售和 GTM 动作的公司，也拥有天然优势。大多数 IT 和业务条线负责人每天都有太多事情要处理；所以如果你不在他们的议程上，很可能别人就在。视行业而定，你需要使用完全不同的话语体系，采用不同的安全与合规推进方式，支持必须满足的监管控制，参加公司会聚集的行业活动，并与不同的 system integrator 和咨询伙伴合作，等等。越是做得泛化，你就越难真正说客户的语言，这正是应用层占优的地方。最后补充一点。仍然有人认为，这一切最终都会被模型 intelligence 本身所化解，bitter lesson 会在极限情况下解决所有这些问题。这或许没错，但企业需要的是今天就获得变革帮助。而且，将 intelligence 引入现实世界工作中的许多方面，并不只取决于模型纯能力这一条轴线，所以你现在为了赢而做的大部分事情，不管模型未来变得多强，最终都会依然重要。