Arm has launched Arm Total Design for Physical AI alongside a new robotics framework to establish common standards across automated systems.

Physical industries – spanning mining, agriculture, manufacturing, and global transport – account for trillions of dollars in economic activity and an estimated $200 billion annual compute opportunity by the 2030s.

To address engineering fragmentation across these sectors, Arm is convening more than 80 partner organisations spanning software, hardware, and AI. Initial ecosystem participants include AWS, ECARX, Hugging Face, Liquid AI, NXP, PlusAI, PSYONIC, QNX, Qwen, Siemens, and Unitree Robotics.

The initiative targets physical systems that combine AI models, runtime software, compute silicon, sensors, and actuators to sense, reason, and act in operational environments. Hardware manufacturers and software developers require standardised baselines to reduce integration risk, optimise compute workloads, and move from proof-of-concept testing to deployment at scale.

Arm standardises capability tiers for robotics systems

Robotics currently lacks a common method to describe, compare, and communicate system capabilities, according to an architectural manifesto (PDF) published by Arm chief architect Richard Grisenthwaite. This fragmentation makes robotic systems harder to design, integrate, and scale across industrial deployments.

In response, Arm has introduced the Robotics Capability Framework as a collaborative starting point for a shared technical vocabulary, patterned after the SAE Levels used for driving automation.

Arm’s new framework categorises robotic systems across progressing tiers of operational sophistication, mapping machines from reactive setups to context-aware, cognitive, and self-improving systems.

Each capability tier links real-world use cases to machine behaviours, outputs, and hardware constraints. These criteria establish parameters for system latency, compute placement, memory allocation, power constraints, determinism, and safety standards.

Arm developed the initial baseline using feedback from across the robotics sector. Participating organisations contributing to the framework include Anaxi Labs, ANYbotics, FMC³ Robotics, Fourier, GALBOT, Gravis Robotics, Lenovo, McKinsey, and Robotec.ai.

Virtual platforms accelerate pre-silicon automotive physical AI development

Arm Total Design for Physical AI extends a collaborative development structure previously used for cloud AI infrastructure. The programme brings together AI models, virtual platforms, digital twins, sensors, compute silicon, and software stacks to enable earlier development and testing cycles.

Autonomous transport and robotics face common technical requirements across sensory perception, AI processing, real-time control, safety, and power-efficient compute. Arm demonstrated this collaborative methodology in the automotive sector alongside AWS, Google, HERE, RemotiveLabs, and Siemens.

The participating automotive companies developed an integrated digital cockpit reference solution. This environment enabled software engineering teams to develop, test, and validate complex automotive code on the Arm Zena CSS platform prior to physical silicon availability.

Arm is now soliciting technical contributions from the wider engineering community to expand the Robotics Capability Framework as physical AI implementations progress.

Learn more about physical AI during the Physical AI Expo held in Amsterdam, London, and North America.

See also: NVIDIA Jetson Orin Nano 2 brings physical AI to drones and robots

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A new open-source toolkit from Microsoft focuses on runtime security to force strict governance onto enterprise AI agents. The release tackles a growing anxiety: autonomous language models are now executing code and hitting corporate networks way faster than traditional policy controls can keep up.

AI integration used to mean conversational interfaces and advisory copilots. Those systems had read-only access to specific datasets, keeping humans strictly in the execution loop. Organisations are currently deploying agentic frameworks that take independent action, wiring these models directly into internal application programming interfaces, cloud storage repositories, and continuous integration pipelines.

When an autonomous agent can read an email, decide to write a script, and push that script to a server, stricter governance is vital. Static code analysis and pre-deployment vulnerability scanning just can’t handle the non-deterministic nature of large language models. One prompt injection attack (or even a basic hallucination) could send an agent to overwrite a database or pull out customer records.

Microsoft’s new toolkit looks at runtime security instead, providing a way to monitor, evaluate, and block actions at the moment the model tries to execute them. It beats relying on prior training or static parameter checks.

Intercepting the tool-calling layer in real time

Looking at the mechanics of agentic tool calling shows how this works. When an enterprise AI agent has to step outside its core neural network to do something like query an inventory system, it generates a command to hit an external tool.

Microsoft’s framework drops a policy enforcement engine right between the language model and the broader corporate network. Every time the agent tries to trigger an outside function, the toolkit grabs the request and checks the intended action against a central set of governance rules. If the action breaks policy (e.g. an agent authorised only to read inventory data tries to fire off a purchase order) the toolkit blocks the API call and logs the event so a human can review it.

Security teams get a verifiable, auditable trail of every single autonomous decision. Developers also win here; they can build complex multi-agent systems without having to hardcode security protocols into every individual model prompt. Security policies get decoupled from the core application logic entirely and are managed at the infrastructure level.

Most legacy systems were never built to talk to non-deterministic software. An old mainframe database or a customised enterprise resource planning suite doesn’t have native defenses against a machine learning model shooting over malformed requests. Microsoft’s toolkit steps in as a protective translation layer. Even if an underlying language model gets compromised by external inputs; the system’s perimeter holds.

Security leaders might wonder why Microsoft decided to release this runtime toolkit under an open-source license. It comes down to how modern software supply chains actually work.

Developers are currently rushing to build autonomous workflows using a massive mix of open-source libraries, frameworks, and third-party models. If Microsoft locked this runtime security feature to its proprietary platforms, development teams would probably just bypass it for faster, unvetted workarounds to hit their deadlines.

Pushing the toolkit out openly means security and governance controls can fit into any technology stack. It doesn’t matter if an organisation runs local open-weight models, leans on competitors like Anthropic, or deploys hybrid architectures.

Setting up an open standard for AI agent security also lets the wider cybersecurity community chip in. Security vendors can stack commercial dashboards and incident response integrations on top of this open foundation, which speeds up the maturity of the whole ecosystem. For businesses, they avoid vendor lock-in but still get a universally scrutinised security baseline.

The next phase of enterprise AI governance

Enterprise governance doesn’t just stop at security; it hits financial and operational oversight too. Autonomous agents run in a continuous loop of reasoning and execution, burning API tokens at every step. Startups and enterprises are already seeing token costs explode when they deploy agentic systems.

Without runtime governance, an agent tasked with looking up a market trend might decide to hit an expensive proprietary database thousands of times before it finishes. Left alone, a badly configured agent caught in a recursive loop can rack up massive cloud computing bills in a few hours.

The runtime toolkit gives teams a way to slap hard limits on token consumption and API call frequency. By setting boundaries on exactly how many actions an agent can take within a specific timeframe, forecasting computing costs gets much easier. It also stops runaway processes from eating up system resources.

A runtime governance layer hands over the quantitative metrics and control mechanisms needed to meet compliance mandates. The days of just trusting model providers to filter out bad outputs are ending. System safety now falls on the infrastructure that actually executes the models’ decisions

Getting a mature governance program off the ground is going to demand tight collaboration between development operations, legal, and security teams. Language models are only scaling up in capability, and the organisations putting strict runtime controls in place today are the only ones who will be equipped to handle the autonomous workflows of tomorrow.

See also: As AI agents take on more tasks, governance becomes a priority

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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