An AI-powered cloud biofoundry for autonomous biological research

Abstract Autonomous experimentation, where an automated system designs, performs, analyzes, and iteratively improves experiments with minimal human intervention represents a transformative scientific research paradigm. However, it remains hampered by the disconnect between computational design and experimental execution. Here we present iCloudBiofoundry, a cloud-accessible, scalable, and self-evolving platform that bridges this divide. A scientific multi-agent system, iBioGenie, plans research workflows by converting natural language research objectives into testable hypotheses and experimentally grounded protocols using literature, databases, and computational tools. A physical AI layer, BiofoundryAI, translates them into executable workflows that run autonomously on distributed robotic biofoundries, and collects the data for iterative design. We demonstrate the end-to-end capabilities of iCloudBiofoundry across three representative case studies spanning enzyme retrieval, protein engineering, and metabolic engineering. By integrating agentic AI with physical AI, iCloudBiofoundry provides a cloud accessible platform that unifies computational design and robotic execution, enabling autonomous biological research. The iCloudBiofoundry platform is available at: https://icloudbiofoundry.illinois.edu