Autonomous Science

TL;DR Traditional workflow managers treat science as a frozen DAG; the agentic era treats it as a living state machine that learns, optimizes, and—at scale—swarms. The payoff isn’t just speed. It’s a shift from execution pipelines to discovery loops, where hypotheses are generated, tested, and replanned continuously across labs, clouds, and HPC. Why this matters (beyond the lab) Enterprises keep wiring LLMs into point solutions and call it “automation.” Science, under stricter constraints (traceability, causality, irreversibility), is sketching a federated architecture where reasoning agents, facilities, and data fabrics negotiate in real time. If it works in a beamline, it’ll work in your back office. The blueprint is a reusable pattern for any AI-powered operation that must be auditable, distributed, and adaptive. ...

Virtuous Machines: Towards Artificial General Science reports something deceptively simple: an agentic AI designed three psychology studies, recruited and ran 288 human participants online, built the analysis code, and generated full manuscripts—end‑to‑end. Average system runtime per study: ~17 hours (compute time, excluding data collection). The paper frames this as a step toward “artificial general science.” The more immediate story for business leaders: a new production function for knowledge work—one that shifts the bottleneck from human hours to orchestration quality, governance, and data rights. ...