NVIDIA has launched a structured workflow utilizing AI agent skills and its proprietary speech stack to accelerate the evaluation and refinement of clinical automatic speech recognition models. The initiative addresses a persistent challenge in medical voice AI: standard systems frequently misrecognize specialized terminology, including drug names, anatomical references, and procedural codes, while real patient audio remains restricted by privacy regulations and annotation bottlenecks. The solution leverages synthetic data generation paired with domain-specific evaluation benchmarks. By integrating NVIDIA NeMo Data Designer, Magpie TTS Multilingual, and NVIDIA Nemotron Speech, developers can rapidly construct pronunciation-aware audio datasets without handling protected health information. The workflow initiates through an interactive agent skill that gathers specialty requirements and curates a targeted terminology list. The system expands these seed terms into clinical sentences, automatically mapping phonetic pronunciations and injecting SSML tags to guide text-to-speech synthesis. A critical component is the explicit manual review gate for pronunciation gaps. When dictionary-derived phonetics are unavailable, an LLM-backed agent proposes candidate pronunciations that must be validated by human experts before synthesis. This review process ensures synthetic data accurately reflects medical terminology rather than propagating mispronunciations. Once validated, the text-to-speech engine generates controlled audio files compiled into a manifest format for downstream evaluation. The framework operates as a continuous improvement loop. After generating the benchmark, Nemotron Speech transcribes the synthetic audio to calculate entity-level metrics, including keyword error rate for target terminology, word error rate, and character error rate. The agent skill analyzes these results to determine the next operational step. If pronunciation coverage is insufficient, the workflow routes developers back to the build stage for additional term curation. If errors persist across correctly pronounced terms, the system triggers model adaptation using the NeMo Framework. Subsequent reevaluation cycles verify whether adjustments improved recognition accuracy for high-risk clinical categories. This structured approach transforms clinical ASR development into an iterative, decision-driven process. By decoupling benchmark creation from privacy constraints and embedding pronunciation validation into the automation pipeline, healthcare technology teams can stress-test voice systems against real-world terminology before deployment. The workflow demonstrates how agent-guided synthesis provides a scalable, compliance-friendly methodology for achieving production-grade recognition accuracy across diverse medical specialties.