Large language model agents demonstrate expert-level reasoning, yet consistently fail on enterprise-specific tasks due to missing domain knowledge -- terminology, operational procedures, system interdependencies, and institutional decisions that exist largely as tribal knowledge. Current approaches fall into two categories: top-down knowledge engineering, which documents domain knowledge before agents use it, and bottom-up automation, where agents learn from task experience. Both have fundamental limitations: top-down efforts produce bloated, untested knowledge bases; bottom-up approaches cannot acquire knowledge that exists only in human heads. We present Demand-Driven Context (DDC), a problem-first methodology that uses agent failure as the primary signal for what domain knowledge to curate. Inspired by Test-Driven Development, DDC inverts knowledge engineering: instead of curating knowledge and hoping it is useful, DDC gives agents real problems, lets them demand the context they need, and curates only the minimum knowledge required to succeed. We describe the methodology, its entity meta-model, and a convergence hypothesis suggesting that 20-30 problem cycles produce a knowledge base sufficient for a given domain role. We demonstrate DDC through a worked example in retail order fulfillment, where nine cycles targeting an SRE incident management agent produce a reusable knowledge base of 46 entities. Finally, we propose a scaling architecture for enterprise adoption with semi-automated curation and human governance.

翻译：大型语言模型智能体展现出专家级的推理能力，但在企业特定任务上却持续失败，这主要归因于领域知识的缺失——包括术语、操作流程、系统间依赖关系以及机构决策等，这些知识大多以隐性知识的形式存在。现有方法可分为两类：自上而下的知识工程（即在智能体使用前预先整理领域知识）和自下而上的自动化（即智能体从任务经验中学习）。两者均存在根本性局限：自上而下的方法会产生臃肿且未经验证的知识库；自下而上的方法无法获取仅存在于人脑中的知识。本文提出需求驱动上下文（DDC），这是一种以问题为先的方法论，将智能体失败作为筛选领域知识的主要信号。受测试驱动开发启发，DDC颠覆了传统知识工程模式：不是先整理知识并期望其有用，而是让智能体面对真实问题，使其主动索取所需上下文，并仅整理成功所需的最简知识。我们阐述了该方法论、其实体元模型，并提出一个收敛假说：经过20-30个问题周期即可构建出满足特定领域角色需求的知识库。我们通过零售订单履约的实例演示DDC，其中针对站点可靠性工程（SRE）事件管理智能体的九个周期产生了包含46个实体的可复用知识库。最后，我们提出一种支持企业级扩展的架构，该架构包含半自动化知识整理与人工治理机制。