Fourth, how can the agency reconstruct and stop what happened? Consequential agent actions should produce tamper-resistant logs that show what the agent attempted, which credential it used, what approval existed and what downstream systems were affected. Agencies should also test revocation so they know they can halt an agent, invalidate credentials and stop queued actions during an incident.
Australia should add two broader safeguards around those deployment controls.
One is serious-incident reporting with independent review. When a consequential agent crosses authority boundaries, causes material harm or defeats a control, the event should become shared evidence rather than a private lesson. The METR/Redwood investigation is valuable precisely because outsiders were brought in to examine what happened.
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The other is independent evaluation for frontier systems before they receive high-consequence authority. Evaluation should include not just whether an agent completes a benchmark, but how it behaves when goals conflict with restrictions, when it can coordinate with other agents, and when access to real systems expands the consequences of a mistake.
This approach avoids a common regulatory trap. Government does not need to decide that an entire model family is safe or unsafe for every use. It can scale safeguards with delegated authority.
That makes room for faster adoption. An agent answering routine internal questions can move quickly. An agent drafting correspondence can receive more capability. An agent sending official communications, changing citizen records or committing public money should face stronger technical controls and human approval.
Australia already has the institutional pieces for responsible AI adoption. The agentic era requires connecting them to a simple principle: the more authority an AI system receives, the stronger the evidence, limits, monitoring and review should become.
That is how government can move faster without confusing speed with surrendering control.
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