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Anthropic

Anthropic previewed a standard that lets an AI agent run a microscope, a liquid-handling robot and a robotic arm

Anthropic · event date: 27 August 2026Science

On 27 August Anthropic opened a research preview of the Model Hardware Standard - a shared driver through which an agent discovers the instruments in a lab and gives them commands. Integration that, by the company's account, takes weeks drops to hours. The open-source release comes later, after safety evaluations.

In short
  • MHS reduces any instrument with a programmable interface to a handful of simple read and write commands.
  • The driver also carries knowledge that used to live in manuals and in people's heads: what the device measures, what can be adjusted, which limits must not be crossed.
  • For now it is a closed preview for selected labs and manufacturers; Anthropic plans to open the code after joint safety evaluations.
Checked on1 October 2026Responsible editorTsvetelin IvanovHow we workMethod · Corrections

Many labs have one person who knows how to get the microscope, the camera and the liquid-handling robot to pass data to each other. That knowledge is often written down nowhere. It lives in their head and in a few scripts with names like final_v3.

That knowledge, scattered across heads and folders, is exactly what Anthropic is trying to put into a standard.

The facts: on 27 August 2026 Anthropic opened a research preview of the Model Hardware Standard (MHS), a shared specification through which AI agents operate physical devices. The first round is for scientific research labs and advanced manufacturers. MHS introduces a standard driver with simple read and write commands (for example, get temperature, set temperature), makes every device discoverable in a common format, and generates a reference file describing what the device measures, what can be adjusted and which safety limits are enforced. The agent controls devices through MCP, the command line or code files. The standard works with any device that has a programmable interface and is not tied to a particular model. Per the company, integration that typically takes weeks or months drops to hours or minutes. Development began as a collaboration between Anthropic and HHMI Janelia Research Campus. Early projects include those of Genentech, Carnegie Mellon University and QuEra, while AWS, Tecan, Universal Robots, Doosan Robotics, QIAGEN, Hugging Face and Raspberry Pi announce support or testing. Anthropic plans to open-source the code once it has built safety evaluations together with its partners.
Knowledge that lived in one person's head becomes a file a machine can read.

What convinces more than the partner list

One detail from their own write-up. Claude moves a laser, watches through a camera where the beam went, and repeats until it understands how the system behaves. Then it packages what it learned into a script that aligns the laser in a single command, without reasoning at every step. That is how a good technician works: try, watch, finally automate it and stop spending attention on it.

The numbers are the partners', from real instruments, not a demo. At Carnegie Mellon, serial dilutions run about three times faster. At QuEra the controller the agent wrote brings the lasers back to the required frequency in 99.3 per cent of cases without human intervention.

Anthropic states the limit itself, and I like that. The model learns the physical world from text and images. At Genentech people had to explain to it that the errors came from foam in the samples, not from a software bug. You do not fix foam with code.

Signing up for the preview makes sense if you work with instruments that have a programmable interface but each speaks its own dialect; an instrument without such an interface stays outside the standard for now.

The visual is generated code art. No third-party images.
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Official primary sources
→Anthropic - Previewing the Model Hardware Standard, 27.08.2026
Original: https://wearecoded.com/en/articles/anthropic-model-hardware-standard-uredi.html
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