This is the year of agents in biology. What you're seeing in code is already unfolding in molecular data analysis, reorganizing workflows in basic research and drug development. Path forward is focused benchmarking + engineering scoped to specific types of assays. Just as coding agents had to reliably write JavaScript before they could build a browser, biology agents must first learn to accurately process and interpret concrete measurements, (eg. spatial assays), before they can reason about disease, drug mechanism, or patient response. Our roadmap reflects this progression: procedural skill in analysis -> emergent biological reasoning -> synthesis across data types, translational context, and realistic ambiguity. Towards systems that can eventually support expensive, high-stakes decisions in drug programs or research projects. Diffusion in biology is slower than software and needs to be thought through carefully. We work directly with the teams building measurement tech (eg. TakaraBio and Vizgen) and package assay-specific agents alongside their kits and instruments. Scientists complete sample preparation, then use these tech-specific agents to move from raw data to answers and figures. Our partners white-label our platform; we do not run a direct biotech sales motion. Now hiring rapidly across major assay categories, prioritized by which we believe will contribute most to the area under the molecular data curve over the next several years - Spatial - Single Cell - Epigenomics - Genomics - Perturbation/Screening - Diagnostics Looking for talented scientists and engineers with strong foundations in theory and deep experience in these areas to help us build scientifically accurate agents.