This is a six-part walkthrough of a retrieval assistant for chronic kidney disease (CKD), grounded in 22 real clinical guidelines — NICE, KDIGO, the UK Kidney Association, Kidney Care UK. I built it for the Kaggle MedGemma Impact Challenge, and I built it three times on purpose.
Most "agentic AI" demos skip the boring question: do you actually need the agents? So I started with the simplest thing that could work and only added machinery when a number told me to. That gives three levels.
The three levels
- Level 1 — Simple RAG. Retrieve the relevant guideline text, answer from it, cite the source. The honest baseline. (Part 3)
- Level 2 — Agentic RAG. The same retrieval wrapped in a LangGraph state machine that redacts PII, routes by intent, and scores itself. (Part 4)
- Level 3 — Multi-agent. A router hands off to Diet, Medication, Lifestyle and Knowledge specialists. (Part 5)
The shared foundation
All three levels sit on exactly the same base, which is what makes the comparison fair. The 22 guideline PDFs are turned into clean, chunked text and embedded with EmbeddingGemma into a ChromaDB vector store; MedGemma 4B writes the answers from whatever was retrieved. Only the orchestration changes between levels — never the model or the knowledge base. Building that foundation is its own job, and it's the next part of the series. (Part 2)
And an honest ending
I'll also show the evaluation, and it isn't flattering. RAG over dense clinical guidelines is genuinely hard, and the scores say so. I'd rather walk through why than pretend otherwise — that's Part 6.
Why this is worth reading
The pattern — start simple, earn each escalation with a measurement, and be honest when the numbers are middling — is how I'd approach any RAG build, clinical or not. The CKD domain just makes the stakes (and the difficulty) obvious.
Grounded, evaluated, privacy-aware RAG is a chunk of what I do at twentytwotensors. Get in touch if you're building one.