Originally published on PrepStack. This is **Part 4 of 6* of* Context Engineering for Enterprise AI.
Parts 1–3 gave us a context pipeline, a memory layer, and a multi-agent architecture. All real, all measurable — and all still a demo until you wrap them in what this part covers: governance, security, evaluation, observability, cost control, and reliability. That is the enterprise design that lets you ship AI to 110k paying users without losing sleep, money, or a compliance audit.
TL;DR
A context pipeline without governance is a liability, not a feature. The hard part of enterprise AI is not the model — it's the boundary around it.
Production metrics after the full enterprise design is in place:
