5 Lessons from building a voice AI prescription agent

Building a voice AI agent for prescription refill requests demonstrates that high-stakes automation requires system-level safeguards beyond a speech-to-text pipeline and LLM prompt. The demo positions the agent solely as an intake assistant that gathers required information and transfers cases to a pharmacist, rather than approving or dispensing medication itself. Its design emphasizes selecting speech recognition based on accuracy for critical domain-specific terms such as drug names and dosages, supplying both the recognizer and LLM with medication vocabulary and reference data, and separating probabilistic language-model tasks from deterministic code for validation, normalization, and completeness checks. Multiple guardrail layers detect emergencies, route medical advice or dosage changes for review, enforce required fields, and prevent unsafe outcomes even if the model produces unexpected responses. Using Stream’s Vision Agents SDK, AssemblyAI transcription, Gemini reasoning, and Inworld text-to-speech, the project illustrates a broader approach for voice agents in healthcare, finance, legal services, and other consequential settings: define a firm human handoff boundary, use domain-specific evaluation, and build defense in depth.

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