Science agent platform uses Jev-like decision models to halve token cost
A science agent platform with 294 connectors and 200+ skills organizes them ontologically and uses Jev-like decision models to pick tools, cutting token cost in half while improving accuracy.
Ahem ahem. Topic: Own and Customize everything 294 Connectors across target science, mol engineering, developmental biology. DBs, tools, atlases, just ask for it. You can add custom ones. 200+ skills along the same lines. Add custom skills. Give permissions to all the platform workflows. Add custom instructions, give permissions what your agents can and can not use and when to ping you. Even create your own subagents. Compute: Select the compute capacity you need when agentic coding requires gpus. We have found ways where we have ontologically organized every connector and skill, use jev-like decision models, and cut the token cost by half, and actually getting better accuracy in using the right things in the right time. As said before, if we are going to do agentic science, lets do it in a right, controlled way, just like how controlled reproducible experiments looks like.
This post quotes another post: x.com/anindyadeeps/status/2114061288276144449