Building Healthcare-Grade Multi-Agent Systems with Gemini

Disleve Kanku — Wed, 03 Dec 2025 21:38:02 +0000

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 course 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

2.Fragmented tools (PubMed to PDFs to spreadsheets)

3.No structured memory across searches

4.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 in the Intensive course was Day 5.

I realized that real-world workflows already operate like agent systems:

Someone interprets the question
Someone searches
Someone extracts the evidence
Someone synthesizes
Someone reviews the output

So 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,
and deployment

all 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 course, 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 course 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

This wasn’t a tutorial, it was a playbook for building real AI systems.

The biggest shift:
I no longer think in terms of prompts.
I now think 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, the tools, and the conceptual foundation to build something I’ve wanted for years.

This experience has genuinely been transformative and I’m excited to keep pushing the boundaries of what multi-agent systems can do in clinical research.

Thank you to the Kaggle team, instructors, and community for this opportunity.

DEV Community: Disleve Kanku

Building Healthcare-Grade Multi-Agent Systems with Gemini