Anthropic proposes plumbing spec to link AI agents to lab kit and robots

Anthropic on Thursday teased a protocol for allowing AI agents "to safely operate physical devices," a somewhat optimistic ambition given it cannot reliably anticipate how its models behave.

The as-yet-unpublished protocol, dubbed the Model Hardware Standard (MHS), is similar in concept to the Model Context Protocol (MCP), a way for AI models to connect to data sources.

Anthropic intends for MHS to allow Claude or other models to drive the hardware used in laboratories, factories, and robots.

Supopse you are an Iranian research scientist setting up a network of centrifuges to enrich uranium.

You might look at how your machinery spun out of control in 2010 and think: "Maybe if we use an AI model and MHS, we could avoid that sort of mishap next time." MHS, however, is being battle-tested in quieter regions of the world, specifically at Howard Hughes Medical Institute's (HHMI) Janelia Research Campus in Maryland.

And now other entities with suitable laboratory and industrial equipment can apply to join the research preview.

"It typically takes a lab or manufacturing facility weeks, if not months, to set up and integrate their hardware," Anthropic explained in its post.

"Most devices don’t communicate with each other, instead requiring specialists to build bespoke integrations.

MHS reduces this integration work to hours or minutes." Linking hardware to an AI model requires a programmable interface.

Many industrial machines offer one to expose relevant controls and data — but the ecosystem of such devices is very diverse and it is not easy for developers to make a hookup.

MHS aspires to be a universal translation layer.

The MHS driver software uses a limited set of primitives, such as "read" and "write," an approach similar to the way a few simple tools like Bash can be used to power AI agents.

The driver makes connected devices discoverable in a standard format.

It also supports tags that convey information about device functions, and lets users provide that data by conversing with the model during setup.