This is true of all agents, not just coding agents. Probably the biggest challenge that most companies run into in their agent strategy is getting agents the right constrained context to work with for a task. Too much information or conflicting sources, and the agent can easily draw from the data and produce the wrong result. Conflicting sources of truth for documents, data sources that haven’t been kept up to date, knowledge management systems that rely on tribal knowledge to navigate, and so on. On the other end, of course, too little information and the upside is highly limited of agents in the first place. Thus, a lot of challenges with AI strategies are actually data strategy challenges in disguise. This is why there’s such a significant premium on getting structured and unstructured data environments setup properly so agents can work with information effectively. Critical for any large enterprise adopting agents, and also a clear benefit in some cases to startups that can be designed this way from scratch.
这一点适用于所有 agent，不只是 coding agent。大多数公司在其 agent 战略中遇到的最大挑战，很可能是如何为 agent 提供适合完成任务、且受到恰当约束的上下文（context）。信息太多，或者信息来源彼此冲突，agent 就很容易基于这些数据得出错误结果。比如文档存在相互冲突的事实来源（sources of truth），数据源没有持续更新，知识管理系统依赖 tribal knowledge（仅靠组织内部心照不宣的经验）才能导航，等等。另一方面，当然，如果信息太少，那么 agent 本身能够带来的上行空间也会非常有限。因此，AI 战略中的许多挑战，其实是披着外衣的数据战略挑战。这也就是为什么，正确搭建 structured 和 unstructured data 环境会有如此显著的溢价，因为这样 agent 才能有效地处理信息。这对于任何采用 agent 的大型企业都至关重要；而对于能够从零开始按这种方式设计的 startup，在某些情况下也显然是一种优势。