Using electronic health record (EHR) data to answer clinical research questions is challenging. It requires understanding the research question, finding relevant information across different sources, and combining that information to produce meaningful results. We developed an agentic large language model (LLM) pipeline that decomposes investigator-authored requests into source-specific tasks, generates and executes SQL on EHR, integrates structured and unstructured EHR evidence, and applies study-specific clinical note adjudication. We evaluated this workflow against data analyst-identified cohorts across 50 pediatric orthopedic clinical data query requests. Of these, 43 were included in manual-review-adjusted performance analyses. The median adjusted positive predictive value, sensitivity among patients identified by either approach, and corresponding F1 score were 0.93 (95% CI, 0.88-0.98), 0.92 (95% CI, 0.90-0.95) and 0.90 (95% CI, 0.87-0.94), respectively. These findings support the feasibility of translating natural-language research intent into executable, autonomous multi-stage EHR workflows.