An AI-native experimental laboratory for autonomous biomolecular operations

Developing artificial intelligence (AI)-native experimental laboratories for autonomous scientific exploration with minimal human intervention represents a long-standing goal. Although autonomous experimental systems are emerging^1-4, especially in chemical and materials science, they remain largely confined to domains with singular objectives, well-defined workflows and simple instrumentation. This limitation may reflect an 'add-on' architecture that uses AI as a task-specific tool rather than providing it with a global understanding of experiments, especially under engineering-oriented circumstances. Here we present AutoDNA, an AI-native autonomous biomolecular engineering laboratory targeting multi-objective experiments across diverse instruments. Through the co-design of models, experiments and instruments, AutoDNA realizes the co-evolution of AI and the automation system within an end-to-end multi-user framework. The system autonomously orchestrates over 20 types of instruments across more than 11,000 steps to execute core nucleic acid operations including synthesis, transcription, amplification and sequencing, while human intervention is limited to hardware maintenance tasks, such as reagent and labware replenishment. Across four representative applications, AutoDNA matched expert-optimized enzymatic DNA synthesis yields, accelerated concurrent protocol execution by 3.2-fold, completed an end-to-end DNA data storage cycle with exact recovery of the original digital content, and identified and validated a clinically detected KRAS G13D mutation. AutoDNA establishes a blueprint towards science-as-a-service, accelerating biological discovery and advancing various applications in synthetic biology and information storage.