How We Built a Multi-Agent Diagnostic System for Healthcare
← Back to Insights
Case StudyCase Study

How We Built a Multi-Agent Diagnostic System for Healthcare

Satyron TeamMarch 22, 202615 min read

Veterinary medicine is high-stakes, low-margin and chronically under-staffed. We built a multi-agent diagnostic assistant to help clinicians triage cases faster without compromising clinical judgement.

See the Vet AI Agent project in fullOur healthcare & multi-agent AI services

The Orchestrator Pattern

A single supervisor agent owns the conversation and routes sub-tasks to specialist agents: history-taking, differential generation, RAG-grounded reference lookup, and a safety reviewer.

RAG-Grounded Medical Reasoning

Every diagnostic claim must cite a source from our curated veterinary corpus. Uncited claims are blocked at the safety layer.

Safety Guardrails

A dedicated reviewer agent runs after every response. Latency cost around 400ms. Worth every millisecond.

What We Would Revisit

We would build the eval harness before the agents. We would use LangGraph from day one. And we would invest earlier in structured output validation with Pydantic.

Building something similar?

Let's engineer it together.

Start a Project →

Continue Reading