Using R for clinical programming can be challenging, particularly with the specialized, often internal, packages used for reporting. To tackle these issues, we built our own AI assistant.

In this talk, I'll share our story of building a multi-agent R co-pilot with the LangGraph framework, including the lessons we learned and a few tricks we picked up. A key decision was giving each R package its own AI agent. This was a game-changer for getting good help on our internal packages where general-purpose LLMs just can't keep up.

I'll discuss our agent network architecture and the practical techniques used to give agents the right context to write, debug, and explain complex R code for clinical trials. I’ll wrap up with a live demo showing how our design solves real-world programming problems, speeding up development and leading to better, more reliable code.
Pawel Rucki
Roche
Building the Ultimate R AI Assistant
Using R for clinical programming can be challenging, particularly with the specialized, often internal, packages used for reporting. To tackle these issues, we built our own AI assistant.  In this talk, I'll share our story of building a multi-agent R co-pilot with the LangGraph framework, including the lessons we learned and a few tricks we picked up. A key decision was giving each R package its own AI agent. This was a game-changer for getting good help on our internal packages where general-purpose LLMs just can't keep up.  I'll discuss our agent network architecture and the practical techniques used to give agents the right context to write, debug, and explain complex R code for clinical trials. I’ll wrap up with a live demo showing how our design solves real-world programming problems, speeding up development and leading to better, more reliable code.