What is AI execution governance?
The controls between a model’s proposal and a real-world consequence.
Direct answer
AI execution governance determines whether and how an AI-proposed action may proceed, then records what was authorized, executed and verified.
Do we still need action governance if the agent runs in a sandbox?
A sandbox controls runtime access. A separate action boundary evaluates whether an otherwise permitted business action has current authority, policy approval and usable evidence. In this local benchmark, OpenShell admitted POST /payments; the Prudenze middleware then allowed, escalated or denied the proposed mock payment.
Worked benchmark example
Scenario B allowed a USD 500 mock payment and the observed amount matched. Scenario D stopped a USD 900 proposal because human approval was required. Both requests were technically permitted by the runtime policy.
What to ask in an evaluation
Ask where the control intercepts the action, whether ESCALATE prevents execution, and how the decision is linked to the observed side effect. A prompt instruction alone is not an enforcement boundary.
Why it matters
A plausible model response is not evidence that the proposed action is permitted, current or safe.
Concrete cross-sector example
In supply-chain operations, an agent can recommend an urgent purchase while the control layer verifies supplier status, spending authority and current inventory before release.
Control flow
Proposal → identify actor and target → evaluate authority and constraints → decide → execute within bounds → verify
Applicable Prudenze capabilities
Guardian
Evidence and research
Retain the normalized proposal, decision inputs, policy result, execution receipt and verification outcome.
Source: local OpenShell qualification, scenarios B and D. These are deterministic fixtures and a mock payment API, with no customer systems or real payments.
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