Demand-Driven Context: A Methodology for Building Enterprise Knowledge Bases Through Agent Failure
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 p...
| Authors | Raj Navakoti, Saideep Navakoti |
|---|---|
| Primary category | cs.AI |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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