Anthropic on Thursday unveiled a new interface designed to make it easier for artificial intelligence agents to communicate with and operate programmable machines, marking a deeper push by the Claude developer into robotics, scientific equipment and industrial automation.

The Model Hardware Standard, or MHS, is intended to provide a common way for AI agents to interact with devices that have programmable interfaces. Anthropic likened the concept to USB-C, which provides a standardized connection for transferring information between different devices.

“We built this for science to sort of show the promise of AI, but there’s also huge benefits here for enterprise and for industry,” Elizabeth Kelly, Anthropic’s head of beneficial deployments, told CNBC.

The initiative represents an important expansion of Anthropic’s strategy beyond software and into the physical infrastructure that AI agents need to interact with.

Today’s industrial and scientific equipment often relies on proprietary interfaces and specialized integration work. That can make deploying AI systems across different machines expensive and time-consuming because developers have to build separate connections for each piece of hardware. MHS is intended to reduce that friction by creating a common interface through which AI agents can communicate with programmable equipment.

Anthropic said the standard could initially be used in scientific research, robotics and advanced manufacturing. Its longer-term ambition is broader, with the company planning to open-source MHS so device manufacturers across industries can adopt the standard.

The approach is touted as the best bet because it could shift part of the AI infrastructure battle from models and data centers toward the interfaces connecting AI systems to the physical world.

AI agents are being developed to perform multi-step tasks autonomously. Their usefulness, however, depends on their ability to access information and take actions in external systems. Anthropic’s Model Context Protocol, launched as an open-source standard in 2024, addressed the data and software side of that problem by making it easier for AI agents to connect with data sources.

MHS extends that philosophy to hardware.

If widely adopted, a standardized hardware interface could allow an AI agent to move more easily between different machines without requiring developers to rebuild the integration layer from scratch. That could be valuable in laboratories and factories where equipment from different manufacturers operates within the same workflow.

Anthropic said MHS is model agnostic, meaning organizations will not have to use Anthropic’s Claude models to take advantage of the standard.

That detail could be critical to adoption. A standard controlled exclusively by one AI company would have limited appeal to manufacturers and enterprises that want flexibility over which models they deploy. By making MHS model-agnostic and eventually open source, Anthropic is attempting to position it as infrastructure rather than simply another Claude feature.

The approach also places Anthropic in an increasingly competitive race to establish itself across the AI technology stack.

OpenAI and Amazon have invested heavily in AI-native hardware and manufacturing technologies, while Anthropic is expanding its own hardware capabilities. The company is building a silicon team focused on custom chips for its AI models and recently hired Caitlin Kalinowski, a hardware executive who previously held roles at OpenAI, Meta, and Apple.

That hiring points to a broader effort to gain greater control over the hardware supporting Anthropic’s models.

MHS could complement that strategy by addressing a different layer of the physical AI ecosystem. Custom chips can make AI inference more efficient, while a standardized interface can make it easier for those AI systems to interact with machines and equipment.

The potential market extends well beyond humanoid robots.

Scientific laboratories contain automated instruments capable of conducting experiments, collecting measurements, and controlling physical processes. Manufacturing facilities use programmable machinery, sensors and robotic systems. In both environments, AI agents could eventually coordinate multiple machines as part of longer-running workflows.

The integration challenge is substantial because physical systems have different operating requirements, safety constraints, and communication protocols. A common interface cannot by itself eliminate those challenges, but it could reduce the software work required to establish basic communication between agents and machines.

Anthropic is initially limiting MHS to a select group of organizations through a research preview, suggesting the company is still testing how the standard performs in real-world environments. The decision to eventually open-source it could give Anthropic an opportunity to build an ecosystem around the technology before competitors establish competing standards.

That strategy mirrors the company’s earlier move with Model Context Protocol. By turning a proprietary capability into an open standard, Anthropic is understood to be aiming to encourage developers and businesses to build around an interface that remains compatible with its broader agent strategy.

The bigger opportunity is the emergence of what is increasingly being described as physical or embodied AI.

Large language models have largely operated inside computers, where their actions are limited to software environments. The next stage of agent development requires systems that can manipulate equipment, conduct experiments, operate industrial machinery, and interact with the physical environment.

For that to happen at scale, AI systems need standardized ways to communicate with the machines around them.

Anthropic’s MHS is an early attempt to address that infrastructure problem.

However, the company’s challenge will be persuading hardware manufacturers and industrial users that an open, model-agnostic standard is worth adopting. If enough manufacturers implement it, MHS could become a common integration layer between AI agents and physical equipment. If adoption remains limited, its value will be constrained by the fragmented hardware ecosystem it is designed to simplify.

The announcement nevertheless signals a broader ambition for Anthropic. The company is no longer positioning Claude solely as a digital assistant or enterprise software tool. It is building toward an ecosystem in which AI agents can access data, reason through complex tasks, and eventually control physical systems.