Building AI agents for healthcare operations: where they fit in prior auth, coding, and scheduling, the architecture they need.

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AI agents go beyond chatbots: they perceive a task, reason through clinical and payer rules, take an action inside the EHR or payer system, then verify the result. The best fits in healthcare operations are high-volume, rule-governed tasks like eligibility checks, scheduling, and prior authorization, but autonomy has to match risk, not model capability. Medical coding is the hardest test case, since every AI-suggested code needs a traceable link back to documentation for audit purposes. A production-grade agent needs five layers: reasoning, tools, EHR/FHIR integration, a deterministic guardrail layer sitting outside the model, and an audit trail, plus governance that defines autonomy boundaries, explainability, and override paths before launch. Start with one narrow task, prove accuracy and auditability, then expand.