AutoDNA, described in a Nature paper by Wu et al., demonstrates an AI-native lab capable of autonomous biomolecular operations.

Supporting data for the study are available in the Article and Supplementary Information, with sequencing data and agent outputs deposited on Zenodo and the hg38 genome reference downloaded from NCBI, with additional data available on reasonable request.

The article provides a comprehensive list of related references on autonomous labs, AI agents in chemistry, and DNA data storage to establish scholarly context.

AutoDNA is presented as a step toward closing the gap between experimental design and laboratory implementation, signaling progress in autonomous molecular biology workflows.

The piece highlights that AI is expanding from computational tasks into wet-lab work, including DNA sequencing and experimental execution.

References to Wu, Swanson, Wittmann, Zou, and colleagues indicate an ongoing research trajectory in AI-driven wet-lab automation.

AutoDNA aims to enable AI agents to plan, execute, and monitor DNA experiments, reducing manual labor and accelerating the experimental cycle.

Code from the study is available on GitHub at https://github.com/IPADS-X/AutoDNA, with the specific version archived on Zenodo.

Source data are provided with the paper under a dedicated section.

An AI system coordinates laboratory instruments to perform multi-step molecular-biology experiments with minimal human intervention, bridging proposing experiments and executing them.