Execution Evidence generated at the Execution Boundary can be used not only for Audit, but also as Feedback for continuously improving Governance through AI Vigilance.

Decisions formed on the Governance side are Enforced at the Execution Boundary without being re-decided, while information such as the Request, Constraint, Enforcement Action, Outcome, Time, and System is recorded as Execution Evidence.

Continuous observation of multiple Execution Evidence records can reveal changes that may not be visible in individual events. Examples include repeated Denials of a particular Action, concentrations of Hold under specific conditions, increases in Require Approval, or changes in Execution Patterns following a Policy update. Over time, these may appear as Deviation, Anomaly, Repetition, Shift, or Emerging Pattern.

AI Vigilance observes this Evidence to detect changes and signals across the Governance System rather than focusing only on individual Decisions. When signals indicate that Policy, Evaluation, Authority, Process, Constraint, or other Governance elements may require review, they are fed back into the higher-level Governance Process.

However, AI Vigilance does not autonomously rewrite Policy. Its role is to detect changes from Evidence and connect them to appropriate Evaluation or Escalation. Governance Updates themselves are performed according to the appropriate Authority and Process.

The Execution Boundary therefore becomes a connection point within the Feedback Loop:

Governance → Decision → Enforcement → Evidence → Vigilance → Governance Update

By returning the results of Execution to Governance as Evidence, Governance becomes not a fixed Rule Set, but a Governance System that continuously observes real-world Execution and evolves through a verifiable update Process.