A data assistant proposes exporting a broad customer dataset to an unfamiliar destination at 02:13.
PF SystemsOperational authority for agentic AIStart with one workflow Runtime governance places an explicit decision boundary between an AI proposal and a consequential action. The model can remain probabilistic and replaceable while the organisation retains control over what may actually happen.
The operational problem
Connecting an agent to tools creates capability—not permission.
An agent may hold a credential, call an API or reach a machine. Those connections prove that it can act. They do not prove that this actor is authorised to take this particular action, for this purpose, in this context.
PF Systems addresses this last mile: the point where intelligence is about to become action.
Customer data example · Deny
One proposed action. One governed consequence.
The action is denied because destination, volume and stated purpose do not meet the defined authority conditions.
The operator can see who requested the export, the destination, the relevant condition and why no record was released.
A clear five-outcome control model
Binary blocking can make governance commercially obstructive. PF Kernel returns Allow, Deny, Modify, Step Up or Stop the Line so that the response can match the consequence.
Separate roles preserve accountability
PF Memory manages governed knowledge and context. PF Kernel remains the decision authority. PF Core preserves the linked evidence required to trace, check and replay the governed evaluation. The underlying probabilistic AI is not made deterministic.
Runtime authority can follow the workflow
The boundary can support cloud, private, on-premises, hybrid and selected edge deployments. ClientBridge connects existing applications and tools without inheriting PF Kernel authority.
A controlled first step
Which AI action should your organisation govern first?
Start with one consequential workflow, its authority boundary and the evidence needed afterwards.
Request a governed AI briefing