Waters Corporation has launched AI Link, a software capability that integrates Anthropic's AI models into its laboratory workflows, promising to cut experiment timelines from weeks to days. The integration allows scientists to use natural language to direct lab instruments and manage workflows.
TL;DR
- Waters Corporation introduces AI Link, integrating Anthropic's AI models into lab workflows to automate tasks and reduce experiment timelines.
- The integration supports tasks such as creating methods, running acquisitions, and processing results, with a focus on maintaining data integrity and traceability.
- AI Link is available through an early-access program, with general availability planned for November 2026.
What happened
Waters Corporation has launched Waters AI Link, a software capability that brings Anthropic's Model Context Protocol (MCP) to its waters_connect informatics platform. This integration enables scientists to use natural language to direct Waters instruments and manage laboratory workflows through AI models and agents, including Anthropic's Claude and Claude Science.
AI Link supports tasks such as creating methods, running acquisitions, and processing results, while coordinating connected equipment like sample preparation systems. The aim is to help laboratories close the loop between computational research and physical experimentation, accelerating research and development.
Waters is also joining Anthropic's Model Hardware Standard (MHS) research program, which aims to make it easier for AI agents to operate physical laboratory equipment safely and autonomously. The standard is designed to reduce the time required to integrate instruments, potentially cutting a process that can take weeks or months to hours or minutes.
Why it matters
The integration of AI into laboratory workflows can significantly reduce the time scientists spend on manual tasks, allowing them to focus on more strategic aspects of research. According to Dave Leitham, Vice President of Informatics at Waters Corporation, the biggest gains are in the work around the run, such as building sample lists, setting up methods, and pulling results and reports.
AI-enabled workflows help teams investigate more possibilities, reduce manual hand-offs, and accelerate research and development. This is particularly important as AI models generate hypotheses at increasing speeds, putting pressure on researchers to test them efficiently.
The integration also addresses the challenges of taking AI-directed experiments from research into regulated development and quality control. Waters AI Link uses the Model Context Protocol, an open standard, to package what Waters knows about its software and data, ensuring data integrity, traceability, validation, and clear accountability.
Key facts
- Waters AI Link integrates Anthropic's Model Context Protocol (MCP) into the waters_connect informatics platform.
- The integration supports tasks such as creating methods, running acquisitions, and processing results.
- AI Link is available through an early-access program, with general availability planned for November 2026.
- Waters is joining Anthropic's Model Hardware Standard (MHS) research program to enable AI agents to operate physical laboratory equipment safely and autonomously.
- The integration aims to reduce the time required to integrate instruments from weeks or months to hours or minutes.
- Waters AI Link is designed to maintain data integrity and traceability, ensuring compliance with regulated laboratory environments.
- Dave Leitham, Vice President of Informatics at Waters Corporation, highlights that the biggest gains are in the work around the run, such as building sample lists and setting up methods.
- The integration is expected to cut experiment timelines from weeks to days in development settings.
Context
The launch of Waters AI Link is part of a broader industry shift towards automated, AI-powered research environments. Companies like Danaher and Roche are also advancing AI-driven research through their own initiatives.
The integration of AI into laboratory workflows is expected to blur the lines between computational research and physical experimentation, accelerating the pace of pharmaceutical research and development.
As AI models generate hypotheses at increasing speeds, the ability to test these hypotheses efficiently becomes crucial. AI-enabled workflows help teams investigate more possibilities and reduce manual hand-offs, ultimately accelerating research and development.