Large language models and wearable device data offer a chance to improve personal health, though individual needs vary widely for health queries. A single system struggles with both specific and open-ended health questions. To address this, the Personal Health Agent (PHA) research framework was created to reason about multimodal data for personalized, evidence-based guidance. PHA uses a multi-agent architecture with specialist sub-agents for data science, domain expertise, and health coaching. Real-world data from a study involving wearable data, questionnaires, and blood tests was used for evaluation. The system underwent extensive automated and human evaluations across ten benchmark tasks, involving thousands of annotations and significant expert effort. This work represents a comprehensive evaluation of a health agent and lays the groundwork for accessible personal health agents. This research outlines a conceptual framework and is not a description of any current public product or service. The approach involved user-centered design, analyzing over 1,300 health queries and surveying users to identify key support areas. The system's evaluation focused on benchmarking individual agents and the integrated PHA, using both automated and human assessments.