Microsoft, Ginkgo Collaborate to Demonstrate Lab-in-the-Loop Biology
By Bio-IT World Staff
June 15, 2026 | Earlier this month at Microsoft Build 2026, Microsoft announced general availability of Microsoft Discovery, a platform designed to support the iterative testing cycles inherent in research and discovery workflows by helping build and govern agentic AI workflows across scientific and engineering disciplines, supporting the iterative loops, evidence preservation, and tool coordination that define scientific work.
Now, in the life sciences specifically, Microsoft has announced a collaboration with Ginkgo Bioworks. The goal is to enable researchers to scope and plan experiments in Microsoft Discovery and run them directly on Ginkgo Cloud Lab, without requiring in-house automation.
Microsoft Discovery is designed to work within existing R&D environments, not replace them, the company stressed, helping experts understand the reasoning path behind outputs and keeping human judgment at the center of scientific and engineering decisions.
The collaboration with Ginkgo Bioworks creates a lab-in-the-loop model for biological research. This workflow is traditionally described as a continuous Design–Make–Test–Analyze loop, where scientists generate an experiment plan, hand off validated protocols for lab execution, and then learn from the resulting data to inform the next step. Rather than treating experimentation as a disconnected process, the interplay between Microsoft Discovery and Ginkgo agentic system is designed to create a tighter connection between scientific reasoning and real-world validation.
“Together, agentic AI and autonomous labs will change every part of the scientific process. Iteration cycles will get faster, experiments will require less manual hands-on time, and computational analyses will become more systematic and exhaustive. By making both easier to use, Microsoft and Ginkgo aim to bring greater speed, scale and reproducibility to pre-clinical research,” said Jason Kelly, CEO, Ginkgo Bioworks, in a Microsoft blog about the collaboration.
Example: RNA Design-to-Data
One example of this tighter reasoning loop under development is an RNA design-to-data workflow. In this scenario, Microsoft Discovery uses AI agents to help plan and scope the experiment, while Ginkgo’s automated lab synthesizes DNA templates, performs in vitro transcription, purifies, and quantitates yield and purity, and returns the resulting data for downstream analysis. In addition, Ginkgo Cloud Lab provides users with full transparency on the cost of the experiment before any lab experimentation begins.
This is a real-world Design–Make–Test–Analyze use case that demonstrates how agentic workflows can adapt based on experimental results and accelerate R&D compared with more manual approaches. This matters because modern life sciences teams need more than isolated predictions. They need workflows that connect long term scientific context, biological data, experimental design, and validation while preserving transparency and keeping experts in control. The collaboration with Ginkgo Bioworks extends that approach into biological experimentation. It also reflects a broader principle behind Microsoft Discovery: extensibility. Microsoft Discovery is a platform that can connect Microsoft innovations with partner tools, models, and datasets. In this case, that means pairing Microsoft Discovery’s agentic orchestration with Ginkgo’s autonomous lab execution to support a more connected model for biological discovery.
By connecting agentic AI with autonomous experimentation, Ginkgo Bioworks and Microsoft are working toward a future in which researchers can move faster from hypothesis to insight and do so with greater speed, scale, and reproducibility.