Bounded Runtime Control
Runtime Governance
Elora studies how AI runtime behaviour can be observed, constrained, explained, and governed without treating model output or learned signals as authority.
Bounded Runtime Control
Elora studies how AI runtime behaviour can be observed, constrained, explained, and governed without treating model output or learned signals as authority.
The governor separates four responsibilities so that escalating from information to intervention is deliberate and reviewable.
Collect bounded runtime signals, state changes, and evidence from registered execution surfaces.
Evaluate conditions against the available policy, runtime context, and evidence without silently converting inference into fact.
Present a proposed response and its evidence for review or downstream governance.
Permit a bounded intervention only where explicit authority, policy, and admissibility controls allow it.
CPU and memory admission, leases, lifecycle accounting, and terminal release help keep worker activity inside declared operating bounds.
State evidence makes pressure, continuity, memory use, and interventions inspectable rather than hiding them behind a single health score.
Related pressure signals and focus interventions can be grouped into one decision story, preserving both the trigger and the resulting response.
Runtime intelligence contributes learned evidence and recommendations. It remains advisory and cannot grant itself execution authority.
Runtime controls are not autonomous permission. Elora re-evaluates proposal, identity, policy, context, and authority at the consequence-bearing boundary, with outcomes linked into Decisions and Replay evidence.