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.

Salesforce raised its full-year revenue and profit forecasts on Wednesday and deepened its partnership with Anthropic, giving investors fresh evidence that the rise of generative AI may be creating a new growth engine for the enterprise software giant rather than simply threatening its traditional business.

Shares of Salesforce jumped 14% in extended trading after the company unveiled “Claudeforce,” a new initiative that brings Anthropic’s Claude AI models together with Salesforce’s customer data, applications and workflows. The agreement expands a partnership announced in June and comes as investors have been questioning whether capable AI models could erode the value of conventional software.

The results provide a more complicated picture. Salesforce’s quarter ended July 31 produced revenue of $11.35 billion, up 11% from a year earlier, while the company raised its fiscal 2027 revenue forecast to between $46.1 billion and $46.4 billion from a previous range of $45.9 billion to $46.2 billion. It also lifted its adjusted earnings forecast to $16.67-$16.71 per share from $14.06-$14.12.

The stronger outlook has drawn interest because Salesforce is trying to prove that AI agents can become a meaningful source of incremental revenue while protecting the value of its core customer relationship management business.

Salesforce said momentum in Agentforce, Data 360 and Slack is helping offset volatility in traditional license revenue. The company is also expecting contributions from its planned acquisitions of Contentful and Fin, announced in June.

Agentforce is at the center of the strategy. Salesforce has been moving beyond AI assistants that simply answer questions toward autonomous agents capable of carrying out tasks in sales, customer service and other business functions. That potentially changes the economics of enterprise software because customers could pay not only for software seats, but for AI-driven work performed on their behalf.

Recent results suggest that business is gaining scale. Agentforce and Data 360 together generated nearly $3.9 billion in annual recurring revenue, according to MarketWatch, while Salesforce said it processed more than 7 billion agentic work units, including 3.2 billion during the latest quarter.

Besides concern over AI disruption, the threat to Salesforce has been that customers could use frontier models from companies such as Anthropic and OpenAI to build their own applications, reducing their dependence on large enterprise software vendors. Salesforce is instead attempting to make those models part of its own platform.

“Claudeforce” is therefore not just a conventional technology partnership but a representation of a strategy in which Salesforce provides the enterprise data, business processes and software environment while Anthropic supplies a powerful general-purpose AI model. The arrangement could allow Salesforce to benefit from improvements in frontier AI without having to develop every underlying model itself.

It also shows that model developers increasingly need access to proprietary business data and established distribution channels, while software companies need access to sophisticated AI models. Partnerships such as Salesforce-Anthropic can allow both sides to capture value from that relationship.

The financial results, however, require some qualification. Salesforce’s adjusted earnings of $5.90 per share were more than double the year-earlier level, but $2.53 per share came from gains on strategic investments, including its investment in Anthropic. Share repurchases also reduced the number of outstanding shares and boosted per-share earnings. Excluding the investment gain, adjusted EPS was $3.37, according to MarketWatch.

That makes the revenue outlook and the underlying operating performance more important indicators of whether Salesforce has genuinely turned the corner.

The company is also facing a broader challenge in its traditional software business. AI can make some software functions easier to reproduce now, potentially putting pressure on license growth and pricing. Salesforce’s answer is to move its value proposition higher up the stack, from providing software used by employees to providing an operating layer where employees and AI agents work together.

That approach helps explain why the company has been so aggressive in promoting Agentforce. If AI agents become the primary interface through which employees interact with corporate systems, Salesforce wants those agents running on its platform and using its data rather than bypassing it.

The results offer some support for that argument, but the durability of the trend remains the key question. Analysts have noted that Salesforce must demonstrate that AI-related growth is not simply shifting existing spending from conventional software products into new AI offerings. It needs to show that customers are expanding their overall spending because AI agents are producing measurable productivity gains.

The partnership with Anthropic also highlights a potential tension. Salesforce is relying on an outside model provider even as it seeks to make its own platform indispensable. The company now needs to maintain control over customer data, workflows, and distribution while avoiding excessive dependence on any single AI model provider.

Still, the immediate market reaction suggests investors were encouraged by the combination of stronger guidance and accelerating AI adoption. Salesforce had been caught in the selloff in software stocks as investors questioned whether AI would make established applications obsolete. However, the latest results provide a counterargument: rather than replacing enterprise software outright, AI may increase the value of platforms that already control business data and workflows.