Managing Agentic Flows with Pydantic Graph
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Graph-based Healthcare Series — 3
This is the third post in an ongoing series on graph-based healthcare tools. Stay tuned for upcoming entries on clinical modeling, decision support systems, and graph-powered AI assistants.
In our previous post, we demonstrated how the IMNCI graph model could power a graph-based retrieval-augmented generation (graph RAG) pipeline. By combining structured clinical knowledge with large language models (LLMs), we laid the foundation for a system that supports real-world diagnostic workflows.
In this installment, we take that idea further by introducing agentic flows—a new phase in our clinical decision support pipeline. Here, an intelligent, dialogue-capable assistant doesn’t just answer queries; it actively guides the diagnostic process. This assistant leverages the structured IMNCI graph as its reasoning backbone and uses pydantic graph to statefully orchestrate a set of modular, task-specific assistants (tools).