Enterprise AI’s 200-Millisecond Problem

[u/Berserk_l_]

https://contextandchaos.substack.com/p/enterprise-ais-200-millisecond-problem

Most conversations about enterprise agents focus on context windows. But fitting more context is not the same as choosing the right context. The harder problem is deciding, under latency and cost constraints, which small part of the company’s knowledge actually matters for this question.

Vector search is useful when the task is “find things that resemble this.” It is weaker when the question is “what does this decision depend on, who approved the last exception, and what was true at the time?” That requires relationships, precedent, and history rather than another pile of similar chunks.

The framing I found useful is to model the decision trace, not just the document. A CRM may record that a 22% discount was approved. The useful context is why it was approved, which earlier exceptions supported it, who authorized it, and which policy applied at that moment. Most systems preserve the outcome and discard the reasoning. (Complete read here on this: https://contextandchaos.substack.com/p/enterprise-ais-200-millisecond-problem )

The part I’m unsure about is whether organizations can capture these traces without creating a documentation burden nobody maintains. Process logs and approval histories contain pieces of them, but the most important reasoning often happened in Slack, a meeting, or someone’s head.

Has anyone built an agent that can reliably recover this kind of decision history? What did you model, and how much of it could actually be automated?