Agentic Learning Loops in Clinical Operations
2026-02-08
Learning loops need brakes as much as acceleration.
A static assistant saturates quickly. An agentic loop uses outcomes, feedback, and policy constraints to update behavior safely.
The design challenge is not only algorithmic; it is operational: where to allow adaptation and where to freeze policy.
Clinical operations benefits when loops are explicit, reviewable, and reversible.
“Adaptive systems are trustworthy only when rollback is designed in.”