What it is
Agentis is the why of the stack. AIthena is the data. Navari is the how. Agentis decides what gets built.
The problem it fixes
An agent platform attracts demand faster than anyone can prioritize it. Without a strategy layer every function builds its own agents, the data team curates for whoever asks loudest, and the stack turns into a pile of point solutions. The clinical teams with the most valuable questions often had the least AI expertise to frame them.
What it does
- Takes a strategic question from clinical development and decomposes it into candidate AI use cases.
- Scores them on a value framework and keeps one prioritized backlog.
- Defines the MVP and the success criteria for each, then hands the build to Navari and the data request to AIthena.
- Runs each use case through experiment, learn and adapt cycles until it's a sustained capability.
Three pillars
Agentis was written as one operating strategy for data, models and agents. AIthena supplies AI-ready trial data. Foundation models, built in house and from partners, supply the analysis. Navari's agents connect the two and do the work. Agentis sets which problem all three point at.
The numbers
- 14 use cases running in parallel in the first wave, live by November 2025, across clinical decision support, bioinformatics and patient safety.
- At prototype stage, three clinical studies delivered in six weeks.
Where it stood when I left
I defined the framework and its place in the stack and sponsored it. When I handed off in mid-2026 the first wave was still in execution, with ownership split across the clinical vertical leads. I haven't tracked it since.
Connected work