What are AI agents in healthcare? (And where they fail)

The work they are bought for

Paperwork, mostly. Prior authorization, denied-claim appeals, coding and eligibility checks: policy-dense cases where the governing rule sits in a payer handbook rather than in a clinician's head. The AMA's survey of 1,000 physicians puts the burden at 39 prior authorization requests per physician per week and 13 hours of physician and staff time, with 40% of practices paying someone who does nothing else. Diagnosis gets the headlines. This is the line item that funds the purchase.

The reliability gap

Price in the distance between step accuracy and case completion. HealthAdminBench, a 2026 evaluation that ran computer-use agents against a simulated record system, two payer portals and a fax queue across 135 expert-written tasks, scored the best agent at 36.3% end-to-end task success while another model cleared 82.8% of the individual subtasks. Agents handle each click and still lose the case. Stale policy is the second trap: an agent can sound certain about a denial while quoting a payer rule that changed a year ago.

Where the return shows up

Large systems on Epic already get near-instant approvals from some payers through direct connections, with no AI in the path. Cleveland Clinic has run that for years. Agents pay off where that plumbing does not exist: independent practices, portals with no feed, and fax. From a security standpoint, hold a named approver on every write into a chart and log each action, because a wrong medication entry is a malpractice question rather than a support ticket. Fund agents against prior authorization and claims rework now. Wait on anything that touches clinical judgment.