Multi-agent systems for coordinated scientific workflows
Why Now ⁉️: Data Science → Data Engineering → GenAI → Multi-Agent Systems
My career across Deloitte, CDC, and Roche has shown me how much of drug development and surveillance still relies on fragmented workflows, manual coordination, and human effort just to move data from one step to the next.
Even with modern pipelines and dashboards, the real bottleneck has always been orchestration—translating strategy into experiments, experiments into analysis, and analysis into decisions.
When I started experimenting with GenAI and multi-agent systems, I realized they could do more than summarize documents—they could reason, coordinate, and collaborate across tasks the same way humans do.
Retriever Validator Reasoner Planner Visualizer
Suddenly, the pieces started working together.
🧠 From Pipelines to Agents
This shift inspired my vision of a Virtual Pharma Enterprise:
- strategy → experiment design
- experiment → data generation
- data → analysis
- analysis → decision
All coordinated by intelligent agents.
🔬 A Toy Multi-Agent Demo
To make this idea more concrete, I built a small prototype that simulates a multi-agent literature review workflow.
Open in Colab
Run the full toy pipeline interactively:
🧩 What this demo shows
- retrieval of biomedical abstracts
- summarization into evidence
- validation / classification
- final structured report