From 60% to 84%: Building an AI Agent for Public Health Data

The fixes that actually worked weren't about prompts. We built SaludAI , an open-source AI agent that takes clinical questions in natural language and queries a FHIR R4 server. Think: "How many patients with type 2 diabetes over 60 are in Buenos Aires?" — the agent resolves the terminology, builds the FHIR queries, follows references across resource types, and returns a traceable answer. No LangChain. The agent loop is ~300 lines of Python, every step logged to Langfuse . When something breaks, we can read exactly why. We benchmarked it: 100 questions, 200 synthetic Argentine patients, 10 FHIR resource types, 4 terminology systems. Inspired by FHIR-AgentBench (Verily/KAIST/MIT) — but on synthetic data, so the scores aren't directly comparable to their clinical dataset. Here's how accuracy evolved, and what each fix taught us. 60% → 82%: The agent wasn't seeing the data Our FHIR client returned only the first page. With 687 immunizations and 437 encounters across 200 patients, most coun