Healthcare AI safety is often discussed in terms of model accuracy, validation, and monitoring. Agentic systems introduce another engineering requirement: the ability to recover safely when actions fail.
A robust architecture should distinguish between proposed, authorized, executing, completed, and failed actions. Each state should have defined transitions and appropriate safeguards.
For reversible operations, systems may support cancellation, compensating actions, or restoration of previous states. For multi-step workflows, developers should consider partial completion, duplicate execution, inconsistent records, and failures between dependent tasks.
Audit logs should capture what happened, when it happened, and which authorization permitted the action. Recovery procedures should also avoid silently erasing the history of a clinical or administrative change.
Importantly, not every healthcare action can be undone. High-consequence actions require proportionate preventive controls, explicit authorization where appropriate, and escalation when uncertainty arises.
A safe agent should recognize when it must stop rather than continue pursuing its original objective.
Call to action: Software engineers, clinical informaticians, and healthcare AI researchers, what design patterns have you found useful for safe recovery in automated workflows?
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