Bohrium + SciMaster: Building the infrastructure and ecosystem for agentic science at scale
Abstract
AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning, tool use, and verification. Scaling such agentic science remains difficult because workflows are hard to observe and reproduce, many scientific tools and laboratory systems are not agent-ready, and execution traces are often fragmented. We present Bohrium+SciMaster as an infrastructure-and-ecosystem approach to this problem. Bohrium turns scientific data, software, compute, and laboratory systems into governed, traceable, agent-ready capabilities, while SciMaster orchestrates these capabilities into long-horizon scientific workflows. Between them, a scientific intelligence substrate organizes reusable models, knowledge, and components into executable building blocks for workflow reasoning and action. Across eleven representative master agents spanning literature analysis, simulation, optimization, materials design, and experiment-facing workflows, we report deployment-observed reductions in end-to-end scientific cycle time, together with execution-grounded signals from real workloads at multi-million scale. Together, these results point toward Science-as-a-Service: scientific workflows become increasingly executable, observable, and continuously improvable while remaining anchored in traceable validation and human judgment.