Decision Governance Checklist
Before an AI-generated recommendation becomes an enterprise action, a production system should be able to answer five questions.
1. Is the Context Sufficient?
Does the system have enough relevant, current, trusted, and authorized information to support the action?
2. Is the Action Permitted?
Does the proposed action fall within applicable policy and authority boundaries under the current conditions?
3. What Runtime Controls Apply?
Should the action proceed autonomously, require additional validation, or invoke a checkpoint? Does the system have structured exception handling for conditions that fall outside normal parameters?
4. Can the Decision Be Reconstructed?
Can the organization later understand the relevant context, governance basis, authorized action, and resulting outcome?
5. Will the Outcome Inform Future Governance?
Can operational results help improve future policies, controls, and autonomy decisions?
Control Surfaces
Each question maps to a control surface within a governed execution architecture:
Context Sufficiency · Action Permission · Runtime Governance · Decision Lineage · Feedback Memory
Feedback Memory is the control surface — the governance requirement that outcomes feed back to strengthen future decisions. Agent Atlas by Apova implements this through Decision Memory, the institutional knowledge base that connects decisions to their contexts, policies, and outcomes over time.
A workflow that cannot answer these questions has a gap between AI reasoning and accountable enterprise execution. The checklist is designed to help engineering, operations, risk, and compliance teams evaluate that gap — without tying governance to a particular model or provider. Organizations operating in domains with high regulatory density, such as payments and financial operations, will find these questions especially relevant.