Building Healthcare-Grade Multi-Agent Systems with Gemini
Source: Dev.to
Introduction — Why I Joined the Agents Intensive
When I enrolled in the Kaggle AI Agents Intensive, I had one goal: to learn how to build AI systems that go beyond chatbots—systems that can reason, take actions, coordinate, and transform real workflows.
As someone working at the intersection of data engineering and healthcare, I wanted to see whether agentic architectures could meaningfully improve clinical research, where slow evidence synthesis costs time, money, and sometimes lives.
The answer turned out to be yes, and the Intensive gave me the exact structure, concepts, and building blocks I needed.
The Problem — Healthcare Research Overload
Clinical researchers are drowning in papers. PubMed adds thousands of new articles every week, yet most researchers still copy‑paste into Google Docs, manually extract findings, and lose track of what they’ve reviewed.
This creates four fundamental problems:
- Too much literature to scan manually
- Fragmented tools (PubMed → PDFs → spreadsheets)
- No structured memory across searches
- No reproducibility when using typical LLM assistants
Healthcare research requires traceability, auditability, and evidence‑grounded outputs—something a single monolithic LLM cannot reliably guarantee. The Intensive helped me realize that agents, not prompts, are the right solution.
Why Multi‑Agent Systems?
The turning point was Day 5 of the course. Real‑world workflows already operate like agent systems:
- Someone interprets the question
- Someone searches
- Someone extracts the evidence
- Someone synthesizes
- Someone reviews the output
Instead of forcing one LLM to do everything, I built a pipeline of specialized agents, each with a clear and testable responsibility.
Kaggle’s lessons on:
- tool design
- memory/state
- agent‑to‑agent communication
- observability
- evaluation
- deployment
showed me how to move from “chatbot” thinking to real “AI system” engineering.
What I Built — The Healthcare Research Agent
Using everything from the Intensive, I built a fully functioning, healthcare‑grade multi‑agent research system powered by:
- Gemini 1.5 Pro for reasoning
- PubMed E‑utilities for biomedical search
- Supabase for sessions, logs, and reproducibility
- FastAPI for orchestration
- Docker + Cloud Run for deployment
The Agents
QueryAgent
Converts a biomedical question into PICO + a valid PubMed search query.
SearchAgent
Hits PubMed, retrieves metadata, and returns candidate papers.
EvidenceExtractorAgent
Pulls abstracts and extracts structured evidence: interventions, outcomes, effect sizes.
SummarizerAgent
Synthesizes everything into a concise research brief with inline citations.
Orchestrator
Coordinates all agent‑to‑agent messaging and ensures the workflow executes safely.
Why This Matters
Instead of asking a chatbot:
“What’s the evidence for metformin + insulin in Type 2 diabetes?”
and hoping it’s correct, my system performs a full evidence pipeline with logs, provenance, citations, and stateful sessions. It’s built for researchers, not casual AI use.
What I Learned from Kaggle
The Intensive taught me concepts that are rare to find all in one place:
- Agents need tools
- Tools need safety
- Sessions create continuity
- Memory creates personalization
- Observability makes production possible
- Evaluation prevents regression
- Deployment forces clarity
It wasn’t a tutorial; it was a playbook for building real AI systems. The biggest shift for me was moving from thinking in terms of prompts to thinking in modular workflows, state machines, and agent ecosystems.
What’s Next — The Road Ahead
In the next 90 days I plan to:
- Expand to full‑text PDF extraction
- Add a vector RAG pipeline
- Build specialized oncology and cardiology variants
- Create a research‑agent dashboard
- Publish an open benchmark for biomedical agent systems
My long‑term goal is to contribute to the emerging field of healthcare‑grade agentic AI, where safety, reproducibility, and transparency matter most. The Kaggle AI Agents Intensive gave me the structure, tools, and conceptual foundation to build something I’ve wanted for years. This experience has been transformative, and I’m excited to keep pushing the boundaries of what multi‑agent systems can do in clinical research.