From knowledge to controlled agents

world open.
mission[currentRevision -> r2].
check1:Check[revision -> r1; approved -> true].
mission:MayProceed <- mission[currentRevision -> ?Revision]
    AND ?Check:Check[revision -> ?Revision; approved -> true].
?- mission:MayProceed.
expect unknown: mission:MayProceed.

The check approves an older revision. The current revision is r2, so the rule cannot derive permission. In this open model the question is unknown. It is not safe to proceed merely because some approval exists somewhere in the database.

FrameX models preconditions, evidence requirements, workflow states and permission rules. An external host invokes tools, records receipts and updates observations; the engine then recomputes consequences. Revision matching prevents a stale approval from authorizing a new state.

For H2, a model can propose an interpretation or extracted fact for FrameX to check. In the other direction, derived answers and provenance can constrain a prompt that asks the model to explain or request clarification. For H3, rules determine the next eligible action and the conditions for adaptation or termination. Python supplies generic adapters rather than hiding domain policy in a convenience function.

External declarations need the documented tool host; an offline rule run is not evidence of a real model call.

Continue with Tool Calling for a runnable host episode, or Hybrid AI with FrameX for the invariants and patterns used to design the boundary between models and rules.

Try it

Change the current revision to r1 and predict the permission. Then explain what an executor must check again immediately before acting.