Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.
无论是现有的咨询公司、新出现的公司、来自 agent vendor 的 FDE，还是新的内部 agent engineering 岗位，为了在企业中落地 agent 而将被创造出来的工作量，都会超过我们今天能想象的任何程度。在任何现有组织中实施 agent 的复杂性都非常真实。当我与大型企业交流时，我发现一旦你从 chat 范式走向能够参与有意义工作流的 agent，他们就需要做很多事情。首先，你必须让 agent 能够在你的各个系统之间安全地访问并对话你的数据。在很多情况下，企业拥有数十年的 legacy infrastructure，其中包含了 AI agent 所需的宝贵上下文。要把这些基础设施现代化，并迁移到能与 agent 良好协作的系统中，将需要海量工作。接着，你需要确保已经为 agent 实施了正确的 access controls 和 entitlements，设定了可被安全使用的合适 scopes，并且具备监控、记录日志以及保障其工作安全的方式。然后，你还需要以 agent 能够利用的方式，真正把组织中的流程文档化，好让它们能据此执行工作。你还需要弄清楚，当 agent 和人一起参与某个流程时，新的 workflow 应该是什么样，谁在什么环节介入。仅仅复制旧的 workflow，会削弱收益。哦，而且你很可能还需要为你最重要的新终态流程创建 evals。最后，你必须跟上 agent 领域中快速变化的一整套 best practices 和架构演进。人们随手就能更换自己的个人生产力工具，这当然很有趣，但要在业务流程里这么做，难度要高 100 倍。对于任何试图维持稳定系统设计的人来说，当下这种变化速度既是祝福，也是诅咒。所有这些都意味着，那些在上述这些组成部分（以及更多方面）发展出专长的个人和公司，将会被需要来帮助组织真正大规模实施 agent。这也是为什么当下 vertical AI agents 有其存在逻辑：它们可以深入某个业务领域，并帮助将自动化带入其中。无论你是在企业内部做这件事，还是作为外部业务服务提供商来做，这都是一个巨大的机会。
In general, we should treat AI like a utility, not like a being. The more we confuse what AI is the more we will make ourselves go crazy with analogies that will never fully hold true.
总的来说，我们应该把 AI 当作一种 utility，而不是当作一种存在。我们越是混淆 AI 到底是什么，就越会让自己陷入各种永远不可能完全成立的类比之中，最终把自己逼疯。