Most enterprise AI teams have invested in observability. They have prompt logs, model traces, evaluation pipelines, and dashboards that tell them - after the fact - what their agents said and did. That stack answers a forensic question: what happened. It does not answer a runtime question: should this happen.
A runtime-governance layer is the abstraction that decides, in the millisecond before an action fires, whether it is allowed - and writes the decision to a chain the enterprise can verify later.
This post will go deeper into the design choices behind that decision-time layer and why the typical "agent guardrail" library is not the same shape.