Canonical · L3 Category·Shipped
Runtime governance for AI
Runtime governance shapes action before it becomes real. It is the layer that determines what an AI system may do while it is operating, not merely what it should have done after the fact.
Plain definition
Runtime governance for AI is the layer that determines what an AI system may do while it is operating. It works in the runtime, not in policy documents or after-the-fact audits.
Why it matters
Agentic systems act across tools, sessions, and time. Static policy cannot constrain them in motion. Runtime governance puts the constraint into the environment the agent reasons in.
What it is not
- Policy documentation.
- Audit logging.
- Monitoring.
- Output filtering.
Where it appears in Ubiquity
Ubiquity is Prompted LLC's runtime governance substrate. See the category map.
Ladder context
Demand ladder
L1 Pain→L2 Contrast→L3 Category→L4 Primitive→L5 Branded
← Up the funnel
AI guardrails vs. governance substrateDown the funnel →
AI governance research substrateAdjacent transitions
Related terms
Frequently asked
- What is runtime governance for AI?
- The layer that determines what an AI system may do while it is operating — not merely what it should have done after the fact.
- How is it different from policy?
- Policy is a document above the system. Runtime governance is encoded into the substrate beneath the agent.
- How is it different from monitoring?
- Monitoring observes. Runtime governance decides.
- Why does runtime governance matter for AI agents?
- Because agents act faster than after-the-fact review can constrain them. The constraint has to live at the moment of action.