Public sector AI needs a permission recovery plan

GovInsider recently reported that Singapore is preparing to put artificial intelligence (AI) agents in the hands of around 150,000 public officers, backed by a registry that records who owns each agent and what it does. 

The same central platform is expected to enforce guardrails such as blocking file deletion or external email.

That is exactly the kind of infrastructure public sector AI needs before scale. But the next question is what happens after a guardrail fires.

Imagine an AI agent used to help process licensing applications. It begins sending requests to an unapproved external service, so its outbound access is removed. The citizen portal remains open, officers can still see applications, and the incident is investigated. A vendor later patches the workflow, and the technical alert disappears.

Should the agent immediately get all its old permissions back?

To maintain public service continuity, you need to keep the service running in a restricted mode, then restore agent authority in steps.

Design a continuity ladder before deployment

A useful continuity ladder for public sector AI agents should have four states:

1. In the highest state, the agent can act within its approved scope. 
2. If trust falls, it can move to approval-required mode, where an officer must confirm consequential actions. 
3. If uncertainty remains, it can drop to advisory-only mode, still summarising information or preparing drafts without changing external systems. 
4. At the lowest state, the agency falls back to a manual or non-agent workflow for the essential service.

The purpose is practical: a safety response should narrow the agent's authority without unnecessarily taking the public service offline.

Singapore's Model AI Governance Framework for Agentic AI already recommends bounding an agent's autonomy and access to tools and data, defining meaningful human checkpoints and applying controls throughout the agent lifecycle. 

Its May update added real-world examples, including tiered autonomy based on severity and reversibility.

A continuity ladder extends that logic into incident recovery.

Restore permissions, not just software

A recovered service is not the same thing as recovered authority.

An agent may be able to log in, retrieve data and generate a correct answer while still lacking

enough evidence to justify external messaging, record changes, payments, approvals, or configuration actions.

Recovery should therefore be permission-specific. 

If the incident involved external communications, restore internal read access first. If it involved a write action, allow the agent to prepare the change before letting it execute. 

If it involved delegated work, confirm that downstream tasks have stopped or been revalidated before the original agent can delegate again.

The key public-service question is not whether the agent is 'back.' It is which authority the evidence supports giving back.

Make the registry part of recovery

Singapore's planned agent registry can do more than inventory ownership.
 

For each agent, the registry could record the approved continuity states, the permissions available in each state, the officer or office authorised to move between them, and the evidence required to climb back up the ladder.

That turns recovery from an improvised conference-call decision into a repeatable administrative process.

It also improves accountability. 

If an agent regains permission to send external messages or modify records, an auditor should be able to see who authorised that return, what test was passed and what condition would force the permission to contract again.

Test the fallback while the system is healthy

A degraded mode is useful only if public officers can operate it.

Agencies should rehearse the transition before incidents occur. 

Disable one consequential permission during a controlled exercise. Can officers continue serving citizens? Can they tell which tasks the agent may still perform? Can work already started by the agent be reconciled? Can the agency return to normal without losing the record of what happened during the restricted period?

Singapore has already been testing agentic systems through the AI Agents Sandbox launched by Cyber Security Agency of Singapore (CSA), GovTech Singapore, and Infocomm Media Development Authority (IMDA), with Google. 

Recovery drills would be a natural next step because they test the public service system around the agent, not only the agent itself.

Measure continuity, not only containment

The success metric should include the service outcome.

A security team may correctly restrict an agent and still create a public-service failure if officers cannot complete urgent work without it. 

Conversely, a restricted agent may remain highly useful if it can keep summarising cases, preparing options and preserving queues while humans take over consequential actions.

A recovery exercise should therefore record how quickly risky authority disappeared, how much of the public service remained available, how long manual work took, how many cases accumulated, and what evidence justified each restored permission.

Those are implementation metrics that public sector leaders can use.

Scale the recovery plan with the agents

Singapore's public sector is moving from experimentation toward large-scale agent deployment. The country is also building one of the world's most practical governance frameworks for that transition.

The next useful addition is simple: every agent that can perform a consequential public service action should have a prewritten permission-recovery plan and a tested restricted mode.

That plan should answer three questions before anything goes wrong: what essential service continues when the agent loses authority, who can restore each permission, and what evidence is required before they do.

A resilient public service should be able to make an AI agent less powerful without making the service less available than necessary.

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The author is a graduate student in Applied Intelligence at Georgetown University and an independent researcher focused on trustworthy AI, cybersecurity, autonomous systems and emerging computing architectures.

He holds a bachelor's degree in Computer Science and Engineering from the University of South Florida and an MBA. His work centers on verification, provenance, runtime authority, and resilient system design for AI-enabled technologies.