Independent assurance for AI agents. Audit-ready evidence for consequential agent actions. The system that performs the action should not vouch for the action.
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AI agents are moving from recommendations to consequential actions.
That creates a new problem: not just what an agent can do, but what an organization can independently prove it should have done.
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More than three months after an AI agent accessed non-public files on an Australian Medicare statistics portal, experts still disagree about what actually happened.
That disagreement is the story.
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What was the agent authorized to do?
What rules applied?
What actions did it actually take?
Did its trajectory remain within that authority?
Consequential agent actions should not require months of retrospective interpretation to answer those questions.
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The system that performs the action should not vouch for the action.
That is the case for independent Agentic Assurance.
#AgenticAI #AgenticAssurance #AIAssurance
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For consequential actions, the evidence should reconstruct:
authority
policy in force
approvals
actions taken
resulting state
exceptions
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Governance defines the boundaries.
Security controls what is technically permitted.
Observability records system behavior.
Agentic Assurance asks whether an agent’s actual actions remained within the authority, policies and controls that governed them.
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