This is effectively the #1 problem for AI agents in the enterprise. As we go from agentic coding (where a large amount of context is in the code base, and users are technical enough to get the rest to the agent easily) to a world of knowledge work agents, the context problem becomes much more acute. We see this every day with customers at Box. For existing digital knowledge, it’s often fragmented across legacy systems or environments that don’t play nice with agents, and have access controls that don’t map to the real work that needs to be done, which become a huge hurdle for getting agents the context they need. This has to all get moved to modern, secure cloud environments. But also, companies often haven’t captured and digitized some of the critical context that agents need to work with. Decisions, processes, and workflows often live in people’s heads and tribal knowledge that need to get turned into unstructured data for agents. This is actually one of the biggest points of leverage for applied AI companies, because they can work to specialize in getting agents exactly the information and domain expertise they need. But it’s also one of the reasons why FDEs and new system integrator plays will also work so well right now. The companies that figure this out will be able to get the most out of AI going forward.
这实际上是企业中 AI agents 面临的头号问题。随着我们从 agentic coding 走向知识工作 agents 的世界，context（上下文）问题会变得更加尖锐——在 agentic coding 里，大量 context 都在代码库中，而用户也足够懂技术，能比较容易地把其余信息提供给 agent。我们在 Box 与客户的日常接触中每天都能看到这一点。对于现有的数字化知识，它往往分散在各类 legacy systems（遗留系统）中，或者存在于那些无法很好配合 agents 的环境里；同时，这些环境的访问控制也常常无法映射到实际工作真正需要完成的方式，这就成为让 agents 获得所需 context 的巨大障碍。这些内容都必须迁移到现代化、安全的云环境中。但除此之外，很多公司其实还没有把 agents 开展工作所需的一些关键信息捕捉下来并完成数字化。决策、流程和工作流往往存在于人的脑子里，或者存在于 tribal knowledge（组织内的隐性经验）中，而这些都需要被转化为 agents 可用的 unstructured data（非结构化数据）。这其实是 applied AI companies 的一个最大杠杆点之一，因为它们可以专门去解决如何把 agents 真正需要的信息和领域专长准确地提供给它们。但这也是为什么 FDEs 以及新型 system integrator 模式在当下会如此有效的原因之一。那些把这个问题解决好的公司，未来将能够从 AI 中获得最大的收益。