Anthropic Pushes into Physical World with New Standard for AI Agents

·

A new interface from Anthropic is set to simplify the way artificial intelligence agents interact with machinery, marking a significant step into the physical world for the AI giant. The Model Hardware Standard (MHS) enables seamless communication between devices, including those used in scientific research and advanced manufacturing.

The MHS works by standardizing how information is transmitted between devices, much like a USB-C cord does for data transfer. This means that any device with a programmable interface can be easily integrated into an AI system using Anthropic’s technology.

According to Elizabeth Kelly, head of beneficial deployments at Anthropic, the new standard has far-reaching implications beyond just scientific research. ‘We built this for science to show the promise of AI,’ she said, ‘but there are huge benefits here for enterprise and industry as well.’

The announcement signals that Anthropic is expanding its presence in hardware development, where rivals such as OpenAI and Amazon have invested heavily in designing AI-native devices and manufacturing tools. To support this effort, Anthropic has built out a silicon team to design custom chips for its models.

Anthropic’s recent hire of Caitlin Kalinowski, a seasoned hardware executive with experience at OpenAI, Meta, and Apple, further underscores the company’s commitment to developing its own hardware capabilities. The new standard is also model-agnostic, meaning users are not limited to Anthropic’s family of Claude models.

The Model Hardware Standard is initially available to select organizations in science, robotics, and manufacturing as part of a research preview. However, Anthropic plans to open-source the standard eventually, allowing any device manufacturer to adopt it.

Anthropic has already demonstrated its commitment to open-sourcing standards with the release of the Model Context Protocol last year. This protocol enables easier connections between AI agents and data sources.