08/21/2026
AI demos usually show the ideal path.
But real healthcare workflows are rarely that predictable.
Patient responses can be unexpected. Information may be missing. Systems can go offline. Data sources can disagree. And sometimes, a task simply cannot be completed as planned.
That is where the reliability of an AI agent is truly tested.
A dependable healthcare AI agent should recognize when something goes wrong, recover where possible, request what is missing, and hand over the task without losing important context.
The question is not only, “Can the AI complete the task?”
It is also, “What happens when it can’t?”
Designing for exceptions is what turns an AI capability into a dependable healthcare workflow.
https://www.cabotsolutions.com/ai-agents-for-healthcare