Autonomous AI systems are beginning to move beyond software environments and into warehouses, delivery networks, and public spaces. The development is drawing attention to whether current AI rules cover systems that operate in physical environments.

Most existing AI governance frameworks have focused on online harms and model outputs, including bias, misinformation, and harmful content. Embodied AI systems carry risks in physical environments, where failures can affect infrastructure, property, or human safety.

Singapore’s Infocomm Media Development Authority published version 1.5 of its Model AI Governance Framework for Agentic AI on May 20. The framework sets out guidance for organisations deploying AI agents that can plan, make decisions, and take actions across multiple steps to complete user-defined goals.

The framework says agents can interact with tools, external systems, and other agents, including systems that update databases, write files, control devices, or perform transactions. It lists access controls, monitoring, and human approval among governance measures for deployment.

AI moves into physical systems

At an AI summit in Singapore last week, discussions around robotics and embodied AI focused on operational safety issues more commonly associated with aviation, industrial systems, and critical infrastructure oversight than conventional software regulation.

Speakers also discussed whether autonomous systems can operate safely and reliably in unpredictable real-world environments over extended periods.

Dr. Ya-Qin Zhang, founding dean of the Institute for AI Industry Research at Tsinghua University, said embodied AI systems amplify risks already associated with autonomous software. He said failures can directly affect transport systems, drones, logistics networks, and critical infrastructure.

“Any risk in the digital domain will be amplified in the physical domain, and the physical domain will have a physical consequence,” Zhang told MLex on the sidelines of the summit.

He added that vehicles, drones, smart grids, and other infrastructure could become exposed as AI systems are embedded more deeply into physical operations.

Speakers discussed reliability, operational monitoring, and post-deployment assurance as governance concerns. Summit discussions pointed to deployment-based governance models built around simulation, telemetry, and iterative testing, rather than one-time certification alone.

IMDA’s framework also recommends gradual rollouts, continuous monitoring, and further testing after deployment. It says agents interact dynamically with their environment and not all risks can be anticipated before release.

Monitoring becomes a deployment issue

Grab, which is piloting autonomous vehicles and delivery robots in Singapore’s Punggol district, said deployment governance depends heavily on simulation, testing, and continuous monitoring.

“We do a lot of simulation, we do a lot of testing in closed courses and open courses in order to make sure our robots are reliable,” Suthen Thomas Paradatheth, Grab’s chief technology officer, said during one of the summit panels.

“Before we scale to hundreds of robots, we make sure we crack it first in simulation and with a few robots,” he added.

Grab also pointed to monitoring systems designed to track robot performance and detect unexpected failures after deployment.

“There’s a long tail of issues that could emerge,” Paradatheth said.

The IMDA framework says organisations should assess agentic AI use cases based on data access, external system access, autonomy, and task complexity. It also points to the scope and reversibility of agent actions, third-party involvement, and overall system complexity.

It also recommends limiting agent access to tools and systems, applying least-privilege permissions, and defining standard operating procedures for agent workflows. Organisations should also set mechanisms to take agents offline when they malfunction.

Accountability spreads across more actors

MLex reported that embodied AI systems can involve several parties across development, manufacturing, and deployment. These include AI developers, robotics manufacturers, semiconductor suppliers, and infrastructure operators.

MLex also noted that responsibility can be harder to assign when systems continue adapting after deployment through software updates, telemetry, and operational data.

IMDA says organisations and humans remain accountable for agent actions, even when agents operate autonomously. The framework calls for clear responsibility across the agentic AI value chain, from model and platform providers to deployers, tooling providers, and end users.

Applied Materials said large-scale robotics deployment is also tied to semiconductor economics and systems integration. Om Nalamasu, the company’s chief technology officer, said robotics systems will depend on better sensors, energy efficiency, advanced packaging, and computing architectures.

Nalamasu said robotics systems would require purpose-built designs adapted to specific industrial ecosystems rather than a single solution for all environments.

Zhao Yuli, chief strategy officer of Chinese robotics startup Galbot, said Beijing is prioritising deployment scale and industrial commercialisation through government-backed testbeds, industrial partnerships, and long-term funding initiatives.

Galbot has deployed humanoid robotics systems in retail, warehouse, and pharmaceutical operations in China. These include autonomous stores that operate around the clock. Zhao said semi-structured industrial environments are likely to become an early commercialisation path because they offer more controllable operating conditions.

Japan is placing more focus on standards-setting, robotics datasets, and safety governance. Professor Yutaka Matsuo of the University of Tokyo’s Graduate School of Engineering pointed to an “AI Association” project aimed at collecting 100,000 hours of robotics data to support robotic foundation models.

Matsuo also referred to Japan’s AI Safety Institute and the Hiroshima AI Process as part of broader efforts to develop governance standards for embodied AI systems with Singapore and other Asian countries.

Singapore sets out agent controls

Singapore’s framework sets out four governance areas for agentic AI. These cover upfront risk assessment, human accountability, technical controls, and end-user responsibility. The framework describes them as an iterative process rather than a one-time assessment.

The framework says human oversight has to be adapted for agentic systems because continuous review of all workflows becomes impractical at scale. It recommends human approval at significant checkpoints, including high-stakes actions, irreversible actions, and outlier behaviour.

IMDA also identifies automation bias and alert fatigue as risks when humans supervise capable agents. It recommends auditing oversight through indicators such as human override rates and response times, and using automated real-time monitoring to flag unexpected behaviour.

The framework says users should be told what actions an agent can take, what data it can access, and what responsibilities remain with the user. It also recommends employee training on human-agent interaction, oversight, and the professional skills needed to assess agent outputs.

Companies test AI in regulated workflows

JPMorgan is implementing AI tools across its global investment banking business, Paul Uren, the bank’s Asia Pacific head of investment banking, told Reuters. The bank said the tools help bankers access more information and synthesise it with internal systems. They are also being used to prepare content and support client engagement.

JPMorgan CEO Jamie Dimon told Bloomberg News that the bank would hire more AI specialists and fewer traditional bankers. Reuters reported that global banks are increasing AI investment, reshaping workforces, and changing job roles.

The bank is also among selected organisations permitted by Anthropic to use its Mythos cybersecurity model under a controlled initiative known as Project Glasswing. According to Anthropic, Mythos can detect old vulnerabilities in browsers, infrastructure, and software.

Reuters reported that Goldman Sachs, Citigroup, Bank of America, and Morgan Stanley also have access to, or are testing, Mythos, citing sources and company executives.

IMDA’s framework includes a case study from OCBC Bank of Singapore on source-of-wealth analysis. The system parses income-related documents and drafts a source-of-wealth memo. It does not make credit, onboarding, or risk decisions autonomously.

In that case, the workflow is limited to task-level autonomy and operates only when triggered by predefined workflows. Human review is required at critical decision points, and final validation remains with designated reviewers.

Robots move into industrial use

In Japan, one-third of companies are already using or considering AI-powered robots, according to a Reuters survey conducted by Nikkei Research from May 1 to May 15. The survey contacted 492 companies, with 220 responding on the condition of anonymity.

About 4% of respondents said they already use AI robots, 5% plan to deploy them, and 25% are considering doing so. The remaining 66% said they had no such plans.

Transportation equipment manufacturers were the most active group in the survey, with 80% already using AI robots or considering deployment. By comparison, 94% of wholesale sector respondents said they had no plans to deploy AI robots.

Among companies using, planning to use, or considering AI robots, 71% selected manufacturing as a use case. Another 19% selected dangerous tasks, while 11% selected customer-facing services.

The Japanese government expects AI robots to help address the country’s chronic labour shortage and support its position in industrial robotics. Japan is home to robotics companies including Fanuc, Yaskawa Electric, and Kawasaki Heavy Industries, but faces competition from China and the United States in AI-enabled robotics.

Retail agents expand beyond search

Walmart has outlined plans to use agentic AI across shopping, employee, supplier, and developer workflows.

In July 2025, the retailer announced plans for four AI-powered “super agents.” They are designed for shoppers, store employees, suppliers and sellers, and software developers. Walmart said these agents would become the main entry point for AI interactions across those groups.

One of the tools, Sparky, is already available in Walmart’s app as a generative AI-powered shopping assistant. Hari Vasudev, Walmart’s US chief technology officer, said its expanded version would be able to reorder items and plan events. It would also use computer vision to suggest recipes based on the contents of a shopper’s fridge.

Walmart is also developing an Associate super agent for store workers and corporate staff. A separate Marty agent is being built for sellers, suppliers, and advertisers. The retailer is also working on a Developer super agent for testing, building, and launching future AI tools.

The company declined to say whether the agents would replace jobs. Dave Glick, senior vice president of enterprise business systems, said the tools would create new jobs, without giving further details.

(Photo by Growtika)

See also: OpenAI opens Singapore AI lab as IMDA updates AI framework

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OpenAI will open its first Applied AI Lab outside the US in Singapore. The lab is part of a new partnership with the Ministry of Digital Development and Information.

The initiative, called OpenAI for Singapore, was announced at the ATx Summit and is backed by a commitment of more than S$300 million.

The lab will create more than 200 Singapore-based technical roles over the next few years. OpenAI said Singapore will also become one of its global hubs for forward-deployed engineers who will work with organisations on AI deployment. OpenAI said the lab’s work will be aligned with Singapore’s AI Mission priorities which include public service, finance, and digital infrastructure.

Focus on deployment and talent

The company will work with government agencies and local partners on education and workforce programmes within the Ministry of Education and GovTech. OpenAI also plans to support educators through a Singapore chapter of the OpenAI Academy, participate in the National AI Impact Programme, and run Codex for Teachers hackathons.

The partnership includes plans to work with local partners on accelerator programmes for AI-native startups in the form of workshops for micro-entrepreneurs and small businesses, covering how founders and SMEs can use AI in operations and customer service.

Chng Kai Fong, Permanent Secretary for Digital Development and Information, said Singapore’s response to AI includes growing new sectors, anchoring global frontier companies, and equipping workers with relevant skills.

Singapore updates agentic AI framework

Singapore has also updated its governance framework for agentic AI, which was launched by the Infocomm Media Development Authority at the World Economic Forum in January 2026. The framework builds on Singapore’s earlier Model AI Governance Framework for AI, introduced in 2020, and gives organisations guidance on the responsible deployment of AI agents, including measures to reduce the risks inherent in agentic AI.

IMDA has now updated the framework after seeking feedback and case studies from the industry, with the revised version following input from more than 60 organisations, including AWS, DBS, Google, and Salesforce.

The update adds guidance on risks linked to multi-agent systems, third-party agents, automation bias, and human accountability. The framework now includes more than ten case studies showing how organisations have applied its recommendations.

The case studies were contributed by Singaporean and international organisations, including Ant International, City Developments Limited, Cyber Sierra, Dayos, Google, Knovel, OCBC, PwC, Stability Solutions, Tencent, Terminal 3, Workday, X0PA, and GovTech Singapore.

Case studies show governance controls

One case study focuses on Dayos, a Singapore-headquartered enterprise AI automation company with operations in the US. Dayos built an AI-powered ticketing agent that handles internal IT requests. The agent can resolve some requests automatically and route requests to a human when needed.

Dayos used tiered risk levels to determine what actions the agent could take. Low-risk and reversible actions, like password resets, could be automated and audited biweekly, while moderate-risk actions required human approval before execution. Higher-risk actions, like permission changes with limited reversibility, were excluded from the agent’s authority.

Tencent contributed a case study on CodeBuddy, an agentic AI coding system developed by Tencent Cloud. CodeBuddy can plan, write, and deploy code through natural language instructions and can access filesystems, terminal commands, external APIs, and MCP tools.

CodeBuddy uses preset defaults and configurable permissions. Human approval is required for actions like editing files, running shell commands, making network requests, or using external tools.

The system explains complex commands in plain language before users approve them. Suspicious commands still require human approval, even if similar commands had been pre-approved.

GovTech Singapore’s case study covers the rollout of agentic coding assistants in government. The first phase was limited to GovTech employees, did not allow external tools, and was restricted to low-risk systems. GovTech developed central logging and a framework for connecting approved external tools. The agency also tested the system against potential attacks.

(Photo by Mike Enerio)

See also: GPT-5.5 is OpenAI’s most capable agentic AI model yet

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Every major economy is staring at the same problem right now. Artificial intelligence is consuming electricity at a pace that grids were never designed to handle. In the US, capacity market prices in PJM, the country’s largest grid operator, have risen more than tenfold in two years, with data-centre growth identified as a primary driver. In Europe, utilities are scrambling to upgrade transmission infrastructure fast enough to keep pace with hyperscalers’ demand.

The International Energy Agency (IEA) projects global data-centre electricity consumption could approach 1,000 TWh by the end of this decade. Renewable energy is largely there, but the ability to coordinate it, through AI energy grid mapping at national scales, is what most countries still lack. But China just built it.

A study published in Nature this week by researchers from Peking University and Alibaba Group’s DAMO Academy has produced something that no country has managed before: a complete, high-resolution, AI-generated inventory of an entire nation’s wind and solar infrastructure, with the analytical framework to coordinate it as a unified system.

Using a deep-learning model trained on sub-metre satellite imagery, the team identified China’s 319,972 solar photovoltaic facilities and 91,609 wind turbines, processing 7.56 terabytes of imagery to do so.

AI energy grid mapping

Prior research into solar-wind complementarity – the idea that two sources can offset each other’s variability in time and geography – has largely relied on hypothetical or modelled deployment scenarios. How complementarity manifests under real-world infrastructure, and how it shapes system-level integration outcomes, has until now remained unclear.

The researchers show that solar-wind complementarity substantially reduces generation variability, with effectiveness increasing as the geographic scope of pairing expands.

In practical terms, the further apart the facilities being coordinated are, the more reliably they achieve balance. A cloud that covers solar farms in Gansu does not darken wind corridors in Inner Mongolia, for example. The study’s findings point to a structural inefficiency in how China currently manages its grid: coordination happens at a provincial rather than national level.

Transitioning to a unified national scale, the researchers argue, would make it easier to pair complementary energy sources, stabilise the grid, and avoid curtailment – the wasting of generated renewable power that has long been one of China’s most costly clean-energy problems.

Liu Yu, a professor at Peking University’s School of Earth and Space Sciences, described the inventory as allowing China to see its new-energy landscape from a “God’s-eye view,” a phrase that carries more operational weight than it might first suggest. Grid operators cannot optimise what they are not aware of – until now.

China is in the middle of an AI-driven electricity demand surge that is straining its grid. The rapid proliferation of data services and massive computing facilities have pushed the sector’s power consumption up 44% year-on-year in the first quarter of 2026, reaching 22.9 billion kilowatt-hours, according to the China Electricity Council.

That is an extraordinary rate of growth for a sector whose demand was already great. This has accelerated data-centre expansion in China’s northern and western provinces, where land is cheaper, wind and solar resources are more available, with commensurately lower electricity prices. The provinces being targeted for new data centres are the same regions with the highest solar-wind complementarity.

Behind the model

The technical achievement behind this is worth understanding in its own right. DAMO’s deep-learning model was trained to identify solar photovoltaic facilities and wind turbines from sub-metre resolution satellite imagery, a task complicated by the sheer diversity of installation types, terrain conditions, and image quality.

The resulting dataset covers installations in 1,915 Chinese counties, spanning everything from rooftop panels in coastal cities to utility-scale wind farms on the Mongolian plateau. Processing 7.56 terabytes of imagery to produce a nationally consistent, county-level inventory is a demonstration of what large-scale geospatial AI can do when applied to infrastructure problems, and a template that other countries could, in principle, replicate.

China’s clean energy sector generated an estimated 15.4 trillion yuan (US$2.26 trillion) in economic output last year, equivalent to Brazil’s entire GDP, according to the Finland-based Centre for Research on Energy and Clean Air. Managing an asset base of that scale without a national-level visibility tool was always going to be a limiting factor, a limit that’s now gone.

The study’s dataset and code have been made publicly available via Zenodo.

(Photo by Luo Lei)

See also: Inside China’s push to apply AI in its energy system

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The ceremony was scheduled. The CEOs were on the guest list. And then it wasn’t happening.

On Thursday, US President Donald Trump scrapped a planned AI executive order, which had already been delayed multiple times, citing concerns that it might erode America’s competitive edge over China.

“We’re leading China, we’re leading everybody, and I don’t want to do anything that’s going to get in the way of that lead,” Trump told reporters in the Oval Office. What he did not say was that the order had been effectively killed by the very industry it was meant to oversee.

Lobbied out in one night

According to Semafor, which first reported the backstory, the White House’s plans were halted after Elon Musk of xAI, Meta CEO Mark Zuckerberg, and venture capitalist David Sacks, who, until recently, was Trump’s AI and cryptocurrency tsar, all spoke directly with Trump between Wednesday night and Thursday morning.

The argument that landed, according to US media, citing sources, was an appeal to the “accelerationist” faction in the administration, including officials at the National Economic Council and staffers in the Vice President’s office.

The order itself was not a sweeping regulatory framework. It would have established a voluntary mechanism for AI developers to engage with federal agencies and submit advanced models for security review up to 90 days before their public release. No licensing regime. No mandatory hold periods. Voluntary.

That was apparently still too much. Trump said he postponed it “because I didn’t like certain aspects of it,” declining to specify which ones. He added that he worried it “could have been a blocker,” a telling phrase from a president who has otherwise positioned AI as a jobs and national security priority.

A vacuum with consequences

The US has yet to pass comprehensive AI legislation. What governance architecture exists has been assembled piecemeal, through executive orders, agency guidance, and voluntary agreements. Earlier this month, the federal Centre for AI Standards and Innovation announced evaluation agreements with Google DeepMind, Microsoft, and xAI, allowing the government to assess models before public availability. That programme continues regardless of Thursday’s non-signing.

But the broader picture is one of regulatory drift. In early March, the Trump administration released a National AI Legislative Framework urging Congress to preempt state-level AI laws that “impose undue burdens,” arguing for a single national standard over what it called “fifty discordant ones.” Congress has not acted on it.

The contrast with China is sharp and increasingly difficult to ignore. Beijing’s State Council issued a 2026 legislative work plan in May outlining plans to accelerate comprehensive AI legislation, deploying language on AI governance in formal planning documents for the first time. The National People’s Congress has listed AI legislation for review for the third consecutive year.

In April, Beijing issued new rules requiring AI companies to establish internal ethics review committees. China is writing rules. Washington is cancelling ceremonies.

Who shapes US AI policy

Thursday’s episode clarified something implicit for months: in the current administration, the effective veto on AI regulation sits with a small group of industry principals who have direct access to the president.

Musk, whose xAI is a direct competitor to OpenAI and Anthropic, has a structural interest in keeping the regulatory field open. Zuckerberg’s Meta has similarly positioned itself as a champion of open-source AI development. Sacks, despite having formally left his White House advisory role in March, evidently retains enough influence to shape executive action.

Separately, Semafor reports that OpenAI has secured White House backing for a parallel effort to push AI regulations at the state level, an interesting manoeuvre given that Trump’s earlier executive order threatened states that enacted AI laws the administration disliked. That the administration appears to be simultaneously discouraging state regulation and endorsing OpenAI’s state-level strategy suggests the policy coherence problem runs deeper than one postponed signing.

The China frame does real work, but in both directions

Trump’s stated reason for pulling back, protecting the US lead over China, is the same logic that has driven every major AI policy decision since he returned to office, from the H200 export licence framework to the Stargate infrastructure programme. It is also the logic that China is watching closely.

At the Trump-Xi summit in Beijing earlier this month, the two leaders agreed to launch an intergovernmental dialogue on AI, according to the Chinese Foreign Ministry. Beijing will have noted that Washington’s internal debate about even voluntary AI oversight was resolved not by policymakers, but by the companies that stand to profit most from the absence of guardrails.

In a report by the South China Morning Post, Lizzi C. Lee, a fellow at the Asia Society Policy Institute’s Centre for China Analysis, noted that both the US and China are grappling with the same underlying question: where should the regulatory frontier sit for frontier AI, particularly as models become more capable of autonomous action and more relevant to cybersecurity.

“I think a separate, potentially more important race is on governance and safety: not about who has the most advanced models, but who can govern powerful AI without choking off innovation,” she said.

The same report highlighted what Kyle Chan at the Brookings Institution put it more simply: “AI safety and regulation can be done in a way that doesn’t compromise innovation.”

Neither argument was enough on Thursday. Whether it becomes enough next time, assuming there is a next time, remains unclear.

(Photo by White House)

See also: The US-China AI gap closes amid responsible AI concerns

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, click here for more information.

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The Nvidia Vera chip is rarely the headline when earnings beat estimates, but it should be. When Nvidia reported Q1 revenue of US$81.62 billion on Wednesday, beating analyst estimates of US$78.86 billion, and guided Q2 at US$91 billion–well above Wall Street’s US$86.84 billion forecast–the numbers did what Nvidia numbers always do: dominate the room. 

But buried in CEO Jensen Huang’s conference call with analysts was something more strategically interesting than another quarterly beat. Huang told analysts that Nvidia’s new Vera central processors unlock access to a US$200 billion market, one that sits entirely outside the US$1 trillion the company has already forecast from its Blackwell and Rubin AI GPU lineup between 2025 and 2027. 

He expects Vera chip revenue to hit US$20 billion by the end of this fiscal year. “I expect (Vera) to be the second largest” sales contributor, Huang said during the call.

That’s not a footnote. That’s a second front.

The Vera chip and the inference pivot

The reason Nvidia needs a second front is straightforward: its biggest customers are building their own. Google, Amazon, and Microsoft–collectively expected to pour more than US$700 billion into AI infrastructure this year, up sharply from around US$400 billion in 2025, are simultaneously pouring funds into custom silicon to run AI models. Intel and AMD are also touting CPUs as a credible play for inference workloads. 

The narrative in the chip industry has shifted from who can train the biggest model to who can serve it cheapest and fastest. Inference is where Nvidia’s GPU dominance is most exposed. Training large models is still firmly Nvidia territory, but inference, generating answers at scale, in real time, is increasingly where custom chips from Google’s TPU line, Amazon’s Trainium and others are making their case.

Nvidia’s answer is Vera. The chip, developed in part using technology from Groq, a startup specialising in inference that Nvidia licensed in a deal reportedly worth around US$17 billion, targets exactly this workload. The full Vera Rubin platform, which combines the Vera CPU with Rubin GPUs, is set to launch later this year.

Supply is already the constraint

Huang was candid about one problem: supply. “My sense is that we’ll be supply-constrained through the entire life of Vera Rubin,” he said on the call. It’s a telling admission for a product Nvidia is positioning as a major growth pillar. To get ahead of disruptions, Nvidia is spending heavily on the supply chain. The company disclosed that its supply commitments rose to US$119 billion in Q1, up from US$95.2 billion the previous quarter, a significant jump that reflects both confidence in demand and anxiety about a global memory chip crunch.

Nvidia also announced a US$80 billion share repurchase programme and raised its quarterly cash dividend to 25 cents per share, from 1 cent, moves that signal financial confidence even as Huang warned of tightening supply.

The question investors are asking

Despite the beats, Nvidia shares fell 1.6% in extended trading after the results. eMarketer analyst Jacob Bourne captured the mood: “Nvidia delivered another beat, but at this point that’s essentially priced in as it keeps beating quarter after quarter. The lingering question is whether it can convince investors the AI buildout has durability into 2027 and 2028, especially as the narrative shifts toward inference workloads and competing silicon from Google, Amazon, AMD, and Intel.”

Huang pushed back with numbers of his own. He pointed to a growing sub-segment of AI-specific cloud customers whose spend is now roughly equal to the hyperscalers, but growing faster quarter-over-quarter. “We should be growing faster than hyperscale capex,” he said.

The Vera chip is central to that argument. Whether the supply chain cooperates is a different question entirely.

(Image source: Nvidia’s Newsroom)

See Also: The Nvidia H200 China deal survived the Trump-Xi summit–just not in the way anyone expected

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Alibaba has unveiled a new AI processor built specifically for AI agents, pairing the chip announcement with a multi-year silicon roadmap and a new large language model, signalling that the company is building an integrated AI stack rather than just filling a gap left by US export controls.

The Zhenwu M890, developed by Alibaba’s semiconductor subsidiary T-Head, delivers three times the performance of its predecessor, the Zhenwu 810E, according to the company, as per Reuters report. But the performance jump is less notable than the architectural intent behind the chip: the M890 is purpose-built for AI agents, where software systems must retain long stretches of context, coordinate with other models in real time, and execute complex multi-step tasks with limited human intervention. 

Those demands, heavy on memory bandwidth and inter-model communication, are meaningfully different from what standard inference chips are optimised for. The difference matters because it tells you something about where Alibaba thinks AI compute is heading. The company isn’t designing around today’s dominant use case; it’s building for the workload profile it expects to define enterprise AI over the next several years.

Built for AI agents, not just inference

More significant than the chip itself is the roadmap Alibaba put alongside it. The M890 will be followed by the V900 in the third quarter of 2027, expected to deliver another roughly threefold performance gain, followed by the J900 in the third quarter of 2028. That’s a deliberate, sustained cadence of in-house silicon upgrades that mirrors the kind of tick-tock product cycles Nvidia has used to maintain its lead in AI accelerators.

The parallel to Huawei is worth noting. Huawei laid out a similar chip roadmap for its Ascend line last year, and both announcements reflect the same underlying reality: Chinese technology companies have concluded that depending on foreign silicon, even in scenarios where export restrictions might ease, is a structural risk they cannot accept. The response has been to treat semiconductor development as a long-term capability-building exercise rather than a procurement problem.

Alibaba’s commitment to that exercise is not shallow. The company pledged more than 380 billion yuan, roughly US$53 billion, on cloud and AI infrastructure over three years last year, its largest-ever investment commitment to the sector. The M890 and its successors are downstream of that spending.

Traction that predates the announcement

T-Head said it has shipped more than 560,000 Zhenwu units to date, with over 400 external customers across 20 industries deploying the chips, including automakers and financial services firms. That is a material production footprint, not lab hardware, and it provides Alibaba with real-world deployment data at scale ahead of the M890’s rollout.

The new chip will be available to Chinese enterprise customers through Alibaba Cloud’s domestic model platform, Bailian, packaged inside the Panjiu AL128, a server system that stacks 128 M890 accelerators into a single rack.

The software side of the stack

Alongside the hardware, Alibaba announced Qwen 3.7-Max, the latest version of its flagship large language model, described as engineered for advanced coding and long-running agent tasks. The company said the model can operate continuously for up to 35 hours without performance degradation, a capability specification that only makes sense if you are designing for extended autonomous operation.

The timing is deliberate. Releasing a chip and a model optimised for the same workload class on the same day is a platform play. Alibaba is building a closed loop: its own silicon in T-Head, its own model in Qwen, its own cloud delivery in Bailian. Each component reinforces the others, and the combined stack is designed to reduce enterprise customers’ dependence on any external vendor.

More than half a million chips have been shipped. A successor is arriving in 2027, with another planned for 2028. T-Head is not hedging. At some point, building around US export controls stops being a workaround and starts being a strategy. Alibaba appears to have crossed that line.

(Image source: The White House)

See Also: Alibaba Qwen is challenging proprietary AI model economics

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The AI and Big Data programme on day two of TechEx North America referred at least once to the “AI graveyard,” meaning the large number of pilots that never become durable systems. That phrase set the tone. The question was proof.

The Enterprise AI Implementation, ROI and Adoption track dealt with the hard middle of AI work. Its sessions covered stalled pilots, agentic AI for business impact, the move from experimentation to impact, the decision to buy or build, and durable ROI and autonomous decisioning. A system has to be adopted, governed and measured before it deserved to be called successful.

The session on the AI graveyard was useful because it named the failure pattern. Many companies have enough budget to start AI experiments and enough executive attention to publicise them. Fewer have the data quality, process design, operating authority, and risk control to keep them going.

A day-two session on moving beyond copilots towards agentic AI framed the issue as business impact not novelty. Copilots have been useful as individual productivity tools, but their value is often hard to measure. Agents promise a closer connection to business process, yet they also increase the need for boundaries. An agent that can act in systems has to be evaluated by the quality of the action.

That point linked directly with the Future of AI track. Its opening theme, trust as a competitive advantage, was a useful counterweight to speed. The programme dealt with transparency, governance, regulation, banking analytics, and risk. It also included material from Hex on data agent, with evaluation and governance built in. Agentic AI will not mature in enterprise settings if evaluation remains informal.

Governance appeared in several forms. There was cross-functional governance, which reflects the reality that AI risk does not belong to legal, security or engineering. There was governance in the data layer, where trust depends on lineage and quality. There was governance around agent personas and risk stacks, where companies need to understand what an AI agent is permitted to know and do. The banking session gave the theme a sectoral focus, since financial services have less room for undefinedassurances about automation.

Digital Transformation Week carried the same day-two pressure into business delivery. The programme was built around real use cases, business impact, ROI, AI agents built on APIs, change readiness, government service transformation, city innovation and the conversion of data into financial value. The change-readiness material was especially important. AI fails because staff do not change routines, managers do not alter incentives, or the data needed for daily use never appears in the right place.

Sessions involving the DMV and the City of San Jose placed AI and transformation inside government service. In government, the measure of quality includes reliability, access, explainability and public trust. The Dow material on turning data into dollars sat at the commercial end of the same argument. In both cases, value depends on connecting data work with an accountable outcome.

The Cyber Security and Cloud Expo day-two programme expanded on risk. Its cloud-first enterprise track dealt with AI-led threats, cloud security, the “GenAI velocity gap,” threat intelligence, identity security and AI governance. The cyber programme treated AI as a force that changes attack and defence alike. It can help automate defensive work, but it can also accelerate misuse, widen leakage routes, and increase the strain on existing controls.

The phrase “velocity gap” was used several times during day two. Business units are adopting generative AI faster than many security teams can oversee it: the tools arrive first, policy and monitoring arrive later. The sessions on jailbreaking and data leaks made the point more concretely. If staff place sensitive material into unsanctioned tools, or if approved AI systems are poorly bounded, cloud security and data governance become one and the same.

Zero trust was presented as one answer, with a stronger interpretation of zero trust must now include AI systems, agents, and the data around them. Identity is not limited to human users, but services, agents and automated workflows require permission models as well. The cloud-first enterprise is therefore becoming a place where identity, data classification, AI governance, and threat detection are part of the same control mechanisms.

(Image source: TechEx/TechForge)

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 co-located with other leading technology events. Click here for more information.

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Day two of TechEx North America has been more of a deeper, critical examination of AI in the enterprise, but with a optimistic bent. The AI and Big Data programme opened with reference to what was termed the “AI graveyard” – that is, AI projects that seem to perform well in pilot, but don’t seem to cut it in the real world. Despite the presence of what might be a negative term, multiple speakers and sessions addressed ways in which the forward-thinking business might not ever have to experience the technological cemetery.

The different show tracks of the second day of this event dived deeper into the pervasive issues that may be affecting AI deployments. Sessions in the Enterprise AI Implementation, ROI and Adoption tracks took stalled pilots as a starting point, and tried to ascertain the reasons behind faltering projects. There was a good deal of sound advice for organisations, with sessions on focusing agentic AI on specific business areas, building agent-ready data foundations (planning for success under the hood), and the realities of token-based AI charging on the business’s finances.

At an infra level, there were deeper discussions too on whether companies should buy or build physical infrastructure for their AI projects, and the best ways to create durable ROI on data and AI projects when all the many effecting factors are given due consideration..

In projects where AI roll-outs get stuck, the core issue could be epitomised by the concept of the ‘personal copilot’. This works well on a single worker’s desk and for their individual workflows, but doesn’t really scale to a whole department – never mind a whole business. Many companies report having the budget to start such AI experiments at the level of the single user, and there are usually great results. When said user is a C-suite executive, a personally-achieved efficiency tends to increase the levels of excitement around the company, which has to be considered a positive. But transitioning from this point to meaningful change across the business is where many organisations find their individual struggles and roadblocks. Here was the meat and gravy of day two’s activities on the show floor and the numerous stages at the San Jose McEnery Convention Center.

Cyber issues

Despite the use of terms like ‘stalled’ and ‘difficult to scale’, in the Cyber Security and Cloud Expo stage, speakers cited the the speed at which businesses and organisations adopt agentic AI systems as a cause of a ‘velocity gap’. Where AI deployments are successful, they gain traction fast! But security and governance issues crop up when business units adopt generative AI faster than the security team can govern and ensure the enterprise’s safety.

Like the proverbial double-edged sword, AI can be considered as a force that changes and can improve both attack and defence in the cybersecurity space. There are the issues created internally by unbounded agents and large language models, plus the addition to attackers’ arsenals of AI scanning tools that can identify potential exploits.

Also prevalent among the round-table discussions and keynote speeches was the older theme of shadow IT, now presenting in its new guise as shadow AI. If staff place sensitive material into unsanctioned tools for example, or if approved AI systems are poorly bounded and managed, then the attack surface can expand without the cybersecurity team even being aware of it happening. Therefore, data governance and system oversight are becoming more intertwined than before – this was the message from both cybersecurity strands of the show, and the Cloud and Big Data elements too.

For pure-play cybersecurity functions, zero trust was presented as one answer to the runaway adoption of AI outside the auspices of cybersecurity teams – the adoption the ‘denial by default’ position for humans and machines alike. Proof of identity and privilege levels need also to apply to services and agents; that way, automated workflows are subject to the same permission models as every other element in the IT stack.

The second day of TechEx North America was certainly not a rejection of decision-makers’ AI ambitions – the role of AI and even agents were things of accepted fact among speakers, thought-leaders, and delegates at the event. But there were details and considerations presented by representatives from different industries and business functions, each with positive and insightful things to contribute. Each placed their concerns and their enthusiasms on the table, adding to the discussions around AI implementation in 2026.

The march of the robots

And there was a great deal of excitement, still, in many areas of the conference floor. The humanoid robots on show were a source of much enthusiasm (everyone seems to love a lovable android!), but more pragmatically, the new Physical AI track drew some of the show’s biggest audiences. Multiple delegates away from the track cited software coding as the place that has first yielded positive results from the use of large language models in professional settings. And from many places too came the opinion that automated physical systems will be the next industry segment set to benefit from concerted work around new models and their practical harnesses.

The AI models at the heart of next-gen physical AI are unlikely to be LLMs (although these will be useful if the devices are designed to interact with humans), and as such models develop and emerge from their research stages, it’s the TechEx Events series that will be the first to showcase and present these, and how they can work viably in business contexts.

New learning strands to the event

This year’s event saw a welcome injection of pragmatic coding, with hands-on learning sessions that took attendees through spinning up their own AI agentic models, with lessons in how agents can improve themselves, right from interactive Google Colab instances. The TechEx Learning Hub also featured workshops from Nvidia and the ever-popular Google Hackathon, with learners ranging in abilities from those that needed introducing to an IDE through to those that came with software skills already well-tuned. Putting learnings into practice is what this event is all about, whether it’s C-suite decision makers taking on lessons on best strategic practices, or developers turning creative ideas into reality.

TechEx takes the cutting edge, and distils it through the business lens; pragmatic yet future facing. Catch the next leg of TechEx in Amsterdam this September – who knows how far we may have progressed in the space of four short months?

(Image source: TechEx Events)

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 co-located with other leading technology events. Click here for more information.

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President Trump flew to Beijing, brought Jensen Huang along at the last minute, and left two days later, telling reporters that “something could happen” on chip exports. Nothing did. Not a single Nvidia H200 has shipped to China since Trump first authorised the sales in December 2025, and US Trade Representative Jamieson Greer told Bloomberg that semiconductor controls were not even on the bilateral agenda. 

The summit theatre obscured a more interesting development underneath it. The H200 isn’t stuck because Washington won’t allow it. Washington already has allowed it. Roughly 10 Chinese firms, including Alibaba, Tencent, ByteDance, and JD.com, hold approved US export licences for up to 75,000 units each, with Lenovo and Foxconn authorised as distributors. The chips aren’t moving because Beijing won’t let its own companies take delivery.

Two frameworks, one deadlock

The mechanics of the stalemate are worth understanding clearly. US rules require that all H200 chips ordered by Chinese clients be used only in China. Beijing, meanwhile, has instructed Chinese tech companies to limit their use of Nvidia chips to overseas operations while supporting domestic manufacturing. The two requirements are mutually exclusive. 

Chips cleared for export cannot legally be deployed where Beijing wants to deploy them, and Beijing won’t authorise the domestic use the US licences require, according to Implicator.

Commerce Secretary Howard Lutnick stated at a Senate hearing last month that Chinese firms are trying to keep their investment focused on domestic suppliers, including Huawei. Beijing’s State Council has also ordered a supply-chain security review aimed at cutting dependence on US semiconductors. 

The policy contradiction is not accidental. That is the point.

What Huawei gained while diplomats talked

The days around the summit produced several data points that matter more for the long term than Trump’s parting comment. DeepSeek confirmed its latest model had been optimised to run on Huawei processors. Tencent’s chief strategy officer said Chinese GPU supply would increase progressively through 2026, and an Alibaba executive said its T-Head proprietary GPUs had achieved scaled mass production. 

This follows the April launch of DeepSeek V4, which adapted the model for Huawei’s Ascend chips – the first major Chinese frontier model to do so in training, not just inference. What the summit week confirmed is that the shift is no longer experimental. It is now a supply-chain policy. Nvidia’s China revenue has fallen to roughly 5% in recent quarters, down from above 20% before export controls tightened. The company’s own guidance for the current quarter assumes zero revenue from China. 

Huang’s last-minute inclusion in the delegation – Trump called him directly after seeing media coverage that he had not been invited – suggested urgency. The outcome suggested the limits of what CEO diplomacy can achieve when the obstruction is structural, not procedural.

The read for the AI industry

The stalemate matters beyond bilateral optics. Chinese AI platforms are now operating under a domestic mandate to build on Huawei’s compute stack. The question of which AI hardware architecture becomes dominant in the world’s second-largest AI market is being answered not by technical benchmarks but by government directive.

Beijing steering platforms toward Huawei Ascend chips rather than Nvidia H200S is not just a trade posture. It is a structural bet that the performance gap will close fast enough that being locked into the domestic stack is manageable. DeepSeek V4’s results suggest it may be right, at least for inference workloads. 

Trump said something could happen. Greer said the decision is sovereign for China. Both are true, and neither changes the current position: the H200 deal is approved, licensed, and frozen, with Huawei filling the space it leaves behind.

(Image source: The White House)

See Also: Can China’s chip stacking strategy really challenge Nvidia’s AI dominance?

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 co-located with other leading technology events. Click here for more information.

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

The post The Nvidia H200 China deal survived the Trump-Xi summit–just not in the way anyone expected appeared first on AI News.

Although visitors to an event like TechEx North America will always want to see the cutting edge front and centre stage, the nuance and detail brought to the show by the speakers and exhibitors mean that it’s sometimes the smaller considerations that need to play big – at least, in the minds of enterprise decision-makers.

Across the different tracks of Edge Computing, IoT, Data Centre Congress, and Cyber Security, the question was about what needs to be built around AI before it takes its place in the physical, business-oriented world?

The Edge Computing track, with its roots in traditional industries, looked at latency, deployment discipline, and cybersecurity for IIoT/IT amalgams. The day-one programme positioned edge computing as a place where companies can reassess the value of their data assets, look at how decisions are made by autonomous equipment, and the required speed of processing.

Sessions looked at scaling edge deployments (in multi-site businesses, for example), agentic network operations, distributed inference – on-prem, in-cloud or hybrid – immutable edge infrastructure, and how zero-trust cybersecurity lessons can be applied to control systems.

Ed Doran of the Edge AI Foundation chaired a programme that had as its starting point that the edge is a demanding place in which to operate. The track included reps from Akamai, Spectro Cloud, Scylos, TÜV Rheinland, the OPC Foundation, and Germany’s Schneider Electric. Discussions covered issues in manufacturing and IoT, and delved into industrial automation and connected control and attenuation devices.

Moving intelligence closer to the machine changes risk profiles (in which direction was a matter for debate), and faster local decisions may reduce latency and dependence on central cloud services, but where do observability and control in decision-makers’ minds?

The IoT Tech Expo day-one track on Industrial IoT and Digital Twins looked at manufacturing, with sessions covering smart factory trends, AI beyond Industry 4.0, asset management, practical road-maps for escaping pilot purgatory (more on that below), physical AI in everyday ops, and digital twins.

Similar to debates on AI deployment in the knowledge sector, it was the gap between demo and deployment that was the area subject to most scrutiny. Industrial and back office AI both might work well in a presentation, but can stall when they meet old machines (or legacy software).

The alliterative pilot purgatory held considerable weight in several sessions on the various presentation stages and on the show floor, day one. The Rockwell Automation and Ford session on physical AI and connected asset intelligence looked especially hard at scaling projects that seem to work well in concept, but may fail when hitting the real world. How does intelligence enter daily operations without becoming another dashboard that nobody owns?

Digital twins received similar appraisal. The better version of the digital twin isn’t a visual replica used for demons – although they do have their uses. Instead, several speakers called for, and presented, operational models that can actually help a factory, city, or municipal facility. In addition to pre-testing decisions and improving maintenance, what should the modern digital twin be designed to achieve?

The TechEx programme linked ideas between speakers from Siemens, Korea’s LG CNS, Boston Dynamics, and others across the different show strands. The takehome everywhere was that smart systems, be they deeply embedded in engineering sites or the back office, need to be designed in concord with the people or machines that they’re designed to benefit.

Day-one sessions at the Data Centre Congress track looked at the big issues facing the sector today: construction, power, procurement, cooling, water, and the network spine needed for AI DCs. Keynote speakers and round-table discussion guests talked about construction chaos and power issues, with the event’s early visitors hearing from TechEx’s host city, Santa Clara, about its own data centre journey.

The DC issue remains central to the wider AI debate. As a technology, AI depends on compute, and dense compute at that. This in turn depends on power, cooling, land, and permits. A recurring theme in the infrastructure-focused talks was how AI economics affects the infrastructure stack, with the former rapidly changing, the latter taking years to mature.

In many ways, the TechEx event is unique, in that it brings the issues affecting a whole industry under one roof; a place where the bigger picture can be visualised. In the Data Center Congress, we learned that water and power constraints can cut through the rhetoric around the scale of AI. Sessions under the AI and Big Data roof helped temper the idea of a ‘stampede’ to AI productivity, citing their own reasons why unplanned and disorganised implementations of technology don’t fit the modern enterprise. The data centre is now one of the places where AI strategy becomes physical; the enterprise board room’s considerations are practical.

The Cyber Security and Cloud Expo track put its own take on deployment forward. Here, the day-one programme dealt with security culture, compliance, speed, ransomware, shadow AI, data exfiltration, legacy systems, open-source dependency issues, and the CISO relationship with the C-suite. There was a general consensus around AI adoption increasing a company’s attack surface, and a much-repeated message that existing security weaknesses don’t diminish when the business wants faster, smarter tools.

Sessions on shadow AI and data exfiltration were especially relevant to the wider event. Many companies’ staff use AI services inside business workflows, sometimes without approval, and usually with no facility for logging their activities. That makes data governance and cyber governance effectively the same conversation.

The benefits of one conference playing host to complementary tracks were manifest in several cases. For instance, the cybersecurity track’s concerns around legacy systems were echoed on the IoT and Edge stages, where issues were raised about modern, smart intelligence meeting older plant systems. Security in any context can sometimes become an afterthought, but critical infrastructure in the form of transport or energy means that cybersecurity has to play a central role.

The TechEx North America day-one tracks that were concerned with infrastructure gave the conference a dose of reality, at least in some respects. AI may be discussed in terms of agentic automation, but deployments depend on networks, data centre capacity, and cybersecurity. Edge and IoT sessions showed how intelligence reaches machines, and how carefully and considerately it needs to be applied. The data centre-focused sessions showed the material limits of physical construction, while the cybersecurity sessions showed how a desire for speed can be the enemy.

The day showed the thousands of attendees that putting AI in production isn’t a case of switching the software on. There’s a reliance on the mundane matters of buildings and grids, networks, and security. Companies that understand these issues are more likely to deploy the latest in technology successfully. Getting the bigger picture is what this event is all about.

(Image source: TechForge)

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 co-located with other leading technology events. Click here for more information.

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

The post AI is a matter of power, infrastructure and security: TechEx North America appeared first on AI News.

Amazon has introduced Alexa for Shopping, combining its Rufus shopping chatbot with Alexa+ across its app, website, and Echo Show devices.

The assistant can answer product questions, compare items, track prices, and support shopping reminders. It can also handle scheduled shopping actions and eligible automated purchases.

The company said Alexa for Shopping combines Rufus’ product expertise with Alexa+’s personalised assistant context. Amazon said Rufus helped more than 300 million customers in 2025 research, compare, and buy products.

GeekWire reported that Amazon is retiring the Rufus name from its shopping interface, while Rufus will continue to power parts of the experience behind the scenes.

GeekWire also reported that Amazon CEO Andy Jassy said Rufus monthly active users rose more than 115%, while engagement increased nearly 400% year over year.

Alexa for Shopping is available through the Amazon Shopping app, Amazon’s website, and Echo Show devices. The feature is rolling out to US customers. Signed-in Amazon customers can use it for free, without a Prime membership, Echo device, or Alexa app.

Amazon reported US$426.3 billion in North America net sales and US$161.9 billion in international net sales in 2025. Amazon also reported online stores and third-party seller services as separate revenue categories in its 2025 annual report.

Amazon adds shopping questions to search

The assistant allows customers to ask shopping-related questions through Amazon’s main search bar instead of using a separate chatbot window. Users can ask for product recommendations or purchase history. They can also ask for advice related to specific shopping needs.

Examples shared by Amazon include questions such as “What’s a good skincare routine for men?” and “When did I last order AA batteries?” Amazon said the assistant uses information from its platform to answer these questions.

Amazon said Alexa for Shopping uses information from a customer’s Amazon activity and Alexa interactions. That includes shopping history, browsing, purchases, and conversations. Amazon said the information is used to recommend products and support shopping actions.

Alexa for Shopping can compare products side by side and provide AI-generated summaries on product pages. It can also show AI-generated overviews in search results with category information.

Price tracking and automated shopping

Alexa for Shopping can monitor price drops for selected items for up to one year. Customers can view a full year of price history on product detail pages or by asking the assistant.

The assistant can create shopping guides for larger purchases. These guides compare product features and prices. They also include reviews from Amazon and the web.

Amazon said customers can use the assistant to set scheduled shopping actions, including restocking household items. Amazon said the assistant can also handle birthday reminders and gift suggestions.

Scheduled actions can also be tied to conditions. For example, the assistant can add an item to the cart if it reaches a target price and has not been purchased within a set period.

The assistant can search past orders and add frequently purchased items to a customer’s cart through conversational prompts.

Amazon said customers can view and update personal details used by Alexa for Shopping. These details can include family members, pets, interests, and dietary needs.

Alexa for Shopping can also surface products from other online stores through Shop Direct. For eligible products, Amazon said its Buy for Me agentic AI feature can complete purchases using a customer’s primary address and payment method.

Echo Show gets full shopping access

Amazon is also adding full-store shopping access to Echo Show. Users can browse, search, and shop using voice, touch, or both.

The Echo Show shopping experience is available for Alexa+ customers on Echo Show 15 and Echo Show 21, with support for other devices to follow.

Amazon also cited AI investments in its first-quarter 2026 results. The company said free cash flow fell to US$1.2 billion for the trailing 12 months. It attributed the decline mainly to a US$59.3 billion increase in property and equipment purchases, primarily reflecting AI investments.

Rajiv Mehta, Amazon’s vice president of conversational shopping, said the assistant can carry customer preferences, past purchases, and conversations across phones, laptops, and Echo devices.

Users can access the assistant by updating the Amazon Shopping app and selecting the Alexa icon in the bottom navigation bar. On the desktop, the feature appears at the top of the screen.

(Photo by Anirudh)

See also: Google tests Remy AI agent for Gemini as focus turns to user control

Want to learn more about AI and big data from industry leaders? Check outAI & 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, click here for more information.

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

The post Amazon launches Alexa for Shopping as Rufus moves behind the scenes appeared first on AI News.

Enterprise leaders must progress past generative applications and scale “autonomous intelligence” to capture real growth.

Generating text or summarising internal communications offers localised productivity improvements, yet these abilities rarely alter the core cost or revenue structure of a large organisation. Enterprises are now focused on deploying systems capable of independent execution. Leaders are demanding applications that can traverse internal networks, execute multi-step logic, and finalise transactions without constant human prompting.

Prakul Sharma, principal and AI & Insights Practice Leader at Deloitte Consulting LLP, said: “At Deloitte, we view this as the third stage on an intelligence maturity curve, from ‘assisted intelligence,’ in which AI and analytics help people interpret information, through ‘artificial intelligence,’ with machine learning augmenting human decisions, to ‘autonomous intelligence,’ where AI decides and executes in defined boundaries.

“Today’s GenAI-era abilities – like chatbots and conversational AI – sit in the middle of that curve. Agentic AI acts as the bridge into autonomy, and it is where the centre of gravity is changing now. The difference we are seeing is agency: GenAI produces an answer, while autonomous intelligence pursues an outcome by reasoning over a goal, invoking tools and data, and adapting as conditions change, with humans setting guardrails not driving every step.

“We’re seeing this show up in industries, and in every case, the unlock isn’t the agent itself, but the surrounding governance architecture of identity and human-in-the-loop checkpoints, making autonomy safe to scale.”

The post Deloitte: Scale ‘autonomous intelligence’ for real growth appeared first on AI News.

To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure.

When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a platform to foundational infrastructure, altering the governing rules entirely.

At the initial product stage, exerting tight corporate control often feels highly advantageous. Closed development environments iterate quickly and tightly manage the end-user experience. They capture and concentrate financial value within a single corporate entity, an approach that functions adequately during early product development cycles.

However, IBM’s analysis highlights that expectations change entirely when a technology solidifies into a foundational layer. Once other institutional frameworks, external markets, and broad operational systems rely on the software, the prevailing standards adapt to a new reality. At infrastructure scale, embracing openness ceases to be an ideological stance and becomes a highly practical necessity.

AI is currently crossing this threshold within the enterprise architecture stack. Models are increasingly embedded directly into the ways organisations secure their networks, author source code, execute automated decisions, and generate commercial value. AI functions less as an experimental utility and more as core operational infrastructure.

The recent limited preview of Anthropic’s Claude Mythos model brings this reality into sharper focus for enterprise executives managing risk. Anthropic reports that this specific model can discover and exploit software vulnerabilities at a level matching few human experts.

In response to this power, Anthropic launched Project Glasswing, a gated initiative designed to place these advanced capabilities directly into the hands of network defenders first. From IBM’s perspective, this development forces technology officers to confront immediate structural vulnerabilities. If autonomous models possess the capability to write exploits and shape the overall security environment, Thomas notes that concentrating the understanding of these systems within a small number of technology vendors invites severe operational exposure.

With models achieving infrastructure status, IBM argues the primary issue is no longer exclusively what these machine learning applications can execute. The priority becomes how these systems are constructed, governed, inspected, and actively improved over extended periods.

As underlying frameworks grow in complexity and corporate importance, maintaining closed development pipelines becomes exceedingly difficult to defend. No single vendor can successfully anticipate every operational requirement, adversarial attack vector, or system failure mode.

Implementing opaque AI structures introduces heavy friction across existing network architecture. Connecting closed proprietary models with established enterprise vector databases or highly sensitive internal data lakes frequently creates massive troubleshooting bottlenecks. When anomalous outputs occur or hallucination rates spike, teams lack the internal visibility required to diagnose whether the error originated in the retrieval-augmented generation pipeline or the base model weights.

Integrating legacy on-premises architecture with highly gated cloud models also introduces severe latency into daily operations. When enterprise data governance protocols strictly prohibit sending sensitive customer information to external servers, technology teams are left attempting to strip and anonymise datasets before processing. This constant data sanitisation creates enormous operational drag. 

Furthermore, the spiralling compute costs associated with continuous API calls to locked models erode the exact profit margins these autonomous systems are supposed to enhance. The opacity prevents network engineers from accurately sizing hardware deployments, forcing companies into expensive over-provisioning agreements to maintain baseline functionality.

Why open-source AI is essential for operational resilience

Restricting access to powerful applications is an understandable human instinct that closely resembles caution. Yet, as Thomas points out, at massive infrastructure scale, security typically improves through rigorous external scrutiny rather than through strict concealment.

This represents the enduring lesson of open-source software development. Open-source code does not eliminate enterprise risk. Instead, IBM maintains it actively changes how organisations manage that risk. An open foundation allows a wider base of researchers, corporate developers, and security defenders to examine the architecture, surface underlying weaknesses, test foundational assumptions, and harden the software under real-world conditions.

Within cybersecurity operations, broad visibility is rarely the enemy of operational resilience. In fact, visibility frequently serves as a strict prerequisite for achieving that resilience. Technologies deemed highly important tend to remain safer when larger populations can challenge them, inspect their logic, and contribute to their continuous improvement.

Thomas addresses one of the oldest misconceptions regarding open-source technology: the belief that it inevitably commoditises corporate innovation. In practical application, open infrastructure typically pushes market competition higher up the technology stack. Open systems transfer financial value rather than destroying it.

As common digital foundations mature, the commercial value relocates toward complex implementation, system orchestration, continuous reliability, trust mechanics, and specific domain expertise. IBM’s position asserts that the long-term commercial winners are not those who own the base technological layer, but rather the organisations that understand how to apply it most effectively.

We have witnessed this identical pattern play out across previous generations of enterprise tooling, cloud infrastructure, and operating systems. Open foundations historically expanded developer participation, accelerated iterative improvement, and birthed entirely new, larger markets built on top of those base layers. Enterprise leaders increasingly view open-source as highly important for infrastructure modernisation and emerging AI capabilities. IBM predicts that AI is highly likely to follow this exact historical trajectory.

Looking across the broader vendor ecosystem, leading hyperscalers are adjusting their business postures to accommodate this reality. Rather than engaging in a pure arms race to build the largest proprietary black boxes, highly profitable integrators are focusing heavily on orchestration tooling that allows enterprises to swap out underlying open-source models based on specific workload demands. Highlighting its ongoing leadership in this space, IBM is a key sponsor of this year’s AI & Big Data Expo North America, where these evolving strategies for open enterprise infrastructure will be a primary focus.

This approach completely sidesteps restrictive vendor lock-in and allows companies to route less demanding internal queries to smaller and highly efficient open models, preserving expensive compute resources for complex customer-facing autonomous logic. By decoupling the application layer from the specific foundation model, technology officers can maintain operational agility and protect their bottom line.

The future of enterprise AI demands transparent governance

Another pragmatic reason for embracing open models revolves around product development influence. IBM emphasises that narrow access to underlying code naturally leads to narrow operational perspectives. In contrast, who gets to participate directly shapes what applications are eventually built. 

Providing broad access enables governments, diverse institutions, startups, and varied researchers to actively influence how the technology evolves and where it is commercially applied. This inclusive approach drives functional innovation while simultaneously building structural adaptability and necessary public legitimacy.

As Thomas argues, once autonomous AI assumes the role of core enterprise infrastructure, relying on opacity can no longer serve as the organising principle for system safety. The most reliable blueprint for secure software has paired open foundations with broad external scrutiny, active code maintenance, and serious internal governance.

As AI permanently enters its infrastructure phase, IBM contends that identical logic increasingly applies directly to the foundation models themselves. The stronger the corporate reliance on a technology, the stronger the corresponding case for demanding openness.

If these autonomous workflows are truly becoming foundational to global commerce, then transparency ceases to be a subject of casual debate. According to IBM, it is an absolute, non-negotiable design requirement for any modern enterprise architecture.

See also: Why companies like Apple are building AI agents with limits

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.

The post IBM: How robust AI governance protects enterprise margins appeared first on AI News.

Next-generation AI assistants being developed in the Apple ecosystem and by chipmakers like Qualcomm, but early reports suggest they are being designed with limits in place.

Tom’s Guide has described early versions of these assistants as capable of navigating apps, carrying out bookings, and managing tasks in services. For instance a private beta agentic system completed tasks like booking services or posting content in apps. In one test, it moved through an app workflow and reached a payment screen before asking the user for confirmation.

AI agents are being built with approval checkpoints. Sensitive actions, especially those tied to payments or account changes, require user confirmation before they are completed. The “human-in-the-loop” model lets the system prepare an action, but leaves approval to the user. Research linked to Apple’s AI work has explored ways to ensure systems pause before taking actions users did not explicitly request.

Banking apps already require confirmation for transfers. The same idea is now being applied to AI-driven actions in multiple services.

Limits and control

A control layer comes from restricting what the AI can access. Rather than providing the system full access to apps and data, businesses are establishing limits, such as which apps the AI can interact with and when actions can be triggered.

In practice, this means the AI may be able to draft a purchase or prepare a booking, but not finalise it without approval. It also means the system cannot move freely in all services unless it has been granted permission.

According to Tom’s Guide, the facility is for privacy. If data remains on the device, it eliminates the need to send sensitive information to external servers.

In areas like payments, AI systems are expected to work with partners that already have strict rules in place. In one reported example, payment providers’ services are being integrated to provide secure authentication before transactions are completed, though such safeguards are still under development. The existing systems act as an additional layer of oversight. They can set transaction limits or require extra verification.

Much of the discussion around AI governance has focused on enterprise use. That includes areas like cybersecurity and large-scale automation. The consumer side introduces a different challenge and companies must design controls that work for everyday users. That means clear approval steps and built-in privacy protections.

Autonomy with boundaries

As AI gains the ability to carry out actions, the risks become greater as errors can lead to financial loss or data exposure.

By placing controls at multiple points, including approval and infrastructure, companies are trying to manage those risks.

The approach may shape how agentic AI develops in the near term. Rather than aiming for full independence, companies appear focused on controlled environments where the risks can be managed.

(Photo by Junseong Lee)

See also: Agentic AI’s governance challenges under the EU AI Act in 2026

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 co-located with other leading technology events. Click here for more information.

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The open-source AI movement has never lacked for options. Mistral, Falcon, and a growing field of open-weight models have been available to developers for years. But when Meta threw its weight behind Llama, something shifted. A company with three billion users, vast compute resources, and the credibility of a tech giant was now building openly, and the developer community responded.

By early 2026, the Llama ecosystem had reached 1.2 billion downloads, averaging about 1 million per day. That is the context for what happened on April 8, 2026. Meta launched Muse Spark, its first major new Meta AI model in a year, and the first product from its newly formed Meta Superintelligence Labs.

It is capable in ways Llama 4 never was, benchmarks well against the current frontier, and is completely proprietary. No free download. No open weights. No building on it unless Meta decides you can.

The companyspentUS$14.3 billion, brought in Alexandr Wang from Scale AI to lead its AI rebuild, then spent nine months tearing down its entire AI stack and starting over. Muse Spark is what came out the other side. The developer community that made Llama what it was is now being asked to wait for a future open-source version that may or may not arrive on any predictable timeline.

What is Muse Spark?

Muse Spark is a natively multimodal reasoning model with tool-use, visual chain of thought, and multi-agent orchestration built in. It now powers Meta AI, which reaches over three billion users in Meta’s apps. Meta rebuilt its technology infrastructure from scratch, letting the company create a model that is as capable as its older midsize Llama 4 variant for an order of magnitude less compute.

That efficiency number is worth noting. At the scale Meta operates, compute costs compound fast, and running a frontier-class Meta AI model at a fraction of the cost of its predecessors changes the economics of deploying it in billions of interactions daily.

On benchmarks, the picture is genuinely mixed. Muse Spark scores 52 on the Artificial Intelligence Index v4.0, placing it fourth overall behind Gemini 3.1 Pro, GPT-5.4, and Claude Opus 4.6. Meta has not claimed to have built the best model in the world, which is itself a departure from the over-claiming that damaged Llama 4’s credibility.

Where Muse Spark leads is health. On HealthBench Hard – open-ended health queries – it scores 42.8, substantially ahead of Gemini 3.1 Pro at 20.6, GPT-5.4 at 40.1, and Grok 4.2 at 20.3. Health is a stated priority for Meta; the company says it worked with over 1,000 physicians to curate training data for the model.

Muse Spark also offers three modes of interaction: Instant mode for quick answers, Thinking mode for multi-step reasoning tasks, and Contemplating mode, which orchestrates multiple agents’ reasoning in parallel to compete with the most demanding reasoning modes from Gemini Deep Think and GPT Pro.

The open-source retreat

This is the part of the Muse Spark story that the benchmark tables do not capture. Unlike Meta’s previous models, which were released as open-weight models – meaning anyone could download and run them on their own equipment – Muse Spark is entirely proprietary. The company said it will offer the model in a private preview to select partners through an API, making Muse Spark even more proprietary than the paid models offered by Meta’s rivals.

Wang addressed the change directly, stating: “Nine months ago, we rebuilt our AI stack from scratch. New infrastructure, new architecture, new data pipelines. This is step one. Bigger models are already in development with plans to open-source future versions.”

The developer community’s response has been sceptical. Some see this as a necessary pivot after Llama 4 failed to gain expected traction. Others view it as Meta closing the gates once it has something worth protecting. That is the community now being asked to wait while competitors without that open-source legacy continue shipping freely available weights.

Distribution over benchmarks

Meanwhile, Meta is not waiting for the developer community to come around. Muse Spark will debut in the coming weeks inside Facebook, Instagram, WhatsApp, and Messenger, as well as in Meta’s Ray-Ban AI glasses. That rollout path is arguably more consequential than any benchmark result. OpenAI and Anthropic sell to developers and enterprises. Meta deploys directly to over three billion people already inside its apps daily.

Meta’s push into health does raise privacy questions worth watching. Muse Spark users will need to log in with an existing Meta account to use it, and while Meta does not explicitly say personal account information will be used by the AI, the company has generally trained on public user data and has positioned Muse Spark as a personal superintelligence product.

Meta stock rose more than 9% on the day of the launch, a signal that investors read the Muse Spark release as proof that the US$14.3 billion bet on Wang and the nine-month rebuild produced something real. Whether the promised open-source versions actually materialise is a question the developer community will press every quarter. The answer will define how this chapter of Meta’s AI story is remembered.

See Also: The Meta-Manus review: What enterprise AI buyers need to know about cross-border compliance risk

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 co-located with other leading technology events. Click here for more information.

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AI agents hold the promise of automatically moving data between systems and triggering decisions, but in some cases, they can act without a clear record of what, when, and why they undertook their tasks.

That has the potential to create a governance problem, for which IT leaders are ultimately responsible. If an organisation can’t trace an agent’s actions and don’t have proper control over its authority, leaders can’t prove that a system is operating safely or even lawfully to regulators.

That’s an issue set to become more important from August this year, as enforcement of the EU AI Act kicks in. According to the text of the Act, there will be substantial penalties for failures of governance relating to AI, especially when used in high-risk areas such as when personally-identifiable information is processed, or financial operations take place.

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Anthropic’s most capable AI model has already found thousands of AI cybersecurity vulnerabilities across every major operating system and web browser. The company’s response was not to release it, but to quietly hand it to the organisations responsible for keeping the internet running.

That model is Claude Mythos Preview, and the initiative is called Project Glasswing.

The launch partners include Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks. 

Beyond that core group, Anthropic has extended access to over 40 additional organisations that build or maintain critical software infrastructure. Anthropic is committing up to US$100 million in usage credits for Mythos Preview across the effort, along with US$4 million in direct donations to open-source security organisations. 

A model that outgrew its own benchmarks

Mythos Preview was not specifically trained for cybersecurity work. Anthropic said the capabilities “emerged as a downstream consequence of general improvements in code, reasoning, and autonomy”, and that the same improvements making the model better at patching vulnerabilities also make it better at exploiting them. 

That last part matters. Mythos Preview has improved to the extent that it mostly saturates existing security benchmarks, forcing Anthropic to shift its focus to novel real-world tasks–specifically, zero-day vulnerabilities. These flaws were previously unknown to the software’s developers. 

Among the findings: a 27-year-old bug in OpenBSD, an operating system known for its strong security posture. In another case, the model fully autonomously identified and exploited a 17-year-old remote code execution vulnerability in FreeBSD–CVE-2026-4747–that allows an unauthenticated user anywhere on the internet to obtain complete control of a server running NFS. No human was involved in the discovery or exploitation after the initial prompt to find the bug. 

Nicholas Carlini from Anthropic’s research team described the model’s ability to chain together vulnerabilities: “This model can create exploits out of three, four, or sometimes five vulnerabilities that in sequence give you some kind of very sophisticated end outcome. I’ve found more bugs in the last couple of weeks than I found in the rest of my life combined.” 

Why is it not being released?

“We do not plan to make Claude Mythos Preview generally available due to its cybersecurity capabilities,” Newton Cheng, Frontier Red Team Cyber Lead at Anthropic, said. “Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout–for economies, public safety, and national security–could be severe.” 

This is not hypothetical. Anthropic had previously disclosed what it described as the first documented case of a cyberattack largely executed by AI–a Chinese state-sponsored group that used AI agents to autonomously infiltrate roughly 30 global targets, with AI handling the majority of tactical operations independently. 

The company has also privately briefed senior US government officials on Mythos Preview’s full capabilities. The intelligence community is now actively weighing how the model could reshape both offensive and defensive hacking operations. 

The open-source problem

One dimension of Project Glasswing that goes beyond the headline coalition: open-source software. Jim Zemlin, CEO of the Linux Foundation, put it plainly: “In the past, security expertise has been a luxury reserved for organisations with large security teams. Open-source maintainers, whose software underpins much of the world’s critical infrastructure, have historically been left to figure out security on their own.”

Anthropic has donated US$2.5 million to Alpha-Omega and OpenSSF through the Linux Foundation, and US$1.5 million to the Apache Software Foundation–giving maintainers of critical open-source codebases access to AI cybersecurity vulnerability scanning at a scale that was previously out of reach.

What comes next

Anthropic says its eventual goal is to deploy Mythos-class models at scale, but only when new safeguards are in place. The company plans to launch new safeguards with an upcoming Claude Opus model first, allowing it to refine them with a model that does not pose the same level of risk as Mythos Preview. 

The competitive picture is already shifting around it. When OpenAI released GPT-5.3-Codex in February, the company called it the first model it had classified as high-capability for cybersecurity tasks under its Preparedness Framework. Anthropic’s move with Glasswing signals that the frontier labs see controlled deployment–not open release–as the emerging standard for models at this capability level.

Whether that standard holds as these capabilities spread further is, at this point, an open question that no single initiative can answer.

See Also: Anthropic’s refusal to arm AI is exactly why the UK wants it

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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A survey carried out by OutSystems, The State of AI Development 2026 [email wall], argues that AI has moved into early production phase for many enterprises, primarily inside the IT function.

The survey was based on the responses of 1,879 IT leaders, and warns that adoption of AI is in danger of running ahead of governance and integration. The shortfall is a gap between what IT leaders want agents to do and what their organisations can safely control. The report’s authors urge companies to address the controls or guardrails on AI systems, and also stress the importance of integrating new, AI technology into an organisation’s existing platforms.

OutSystems says 97% of its respondents are exploring some form of agentic strategy, with 49% of them describing their current abilities as “advanced” or “expert.” Nearly half of those surveyed say that over half of agentic AI projects have moved from pilot into production, with Indian companies most successful in implementing the technology: 50% of Indian companies say their AI projects are 51% to 75% successful.

Companies are considering where agents should be deployed first, and under what controls, but although “cost reduction or efficiency gains” is the most cited expectation for AI’s effects, only 22% found their deployments most effective in that regard. Instead, the most effective area gains in a business stemmed from equipping software developers with AI tools described as “generative AI-assisted.”

The report’s geography and sector data show that transitions to AI agentic workflows are unevenly distributed. India stands out as the market with the highest share of users considering themselves “expert”, while many organisations in Australia, Brazil, Germany, the Netherlands, the UK, and the US still identify as intermediate stage users. France and Germany are the most dubious of AI adoption, with Germany recording the highest share of leaders not using agentic AI in any form.

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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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Exciting times are ahead in the world of enterprise perimeter security with a new partnership between Thrive Logic, an AI agent-driven security and operational intelligence platform, and Asylon, a security robotics company. Together, the companies are to introduce physical AI into the network edge security arena, combining “autonomous perimeter patrols with agentic AI analytics and automated incident workflows.” The goal is to reduce response friction and let security leaders report with confidence in high-security exterior zones.

Physical AI understands real-world situations and is capable of responding actively via a continuous, mobile security presence. This is in comparison to merely recording events as and when they take place, for actions to happen later.

Using Asylon’s robotic patrols and Thrive Logic’s AI agent, the integration will monitor perimeter areas and analyse any incidents that may occur. Security teams might therefore relax a little and let AI detect issues in real time. In this arena, it could soon be ‘AI – 1, Bad Actors – 0.’

24/7 robotic patrol oversight

With pressure rising on security leaders in perimeter-intensive environments (labour volatility and unreliable patrol executions are two examples that spring to mind), Asylon’s Robotic Security Operations Centre (RSOC) helps combat challenges with audit-read security outcomes. Alongside Thrive Logic’s integration, robotic patrols won’t just collect video streams, but will produce alerts and step-by-step response processes. Therefore, security teams can respond more effectively, proving humans and AI can work in harmony.

How it works

Video captured by Asylon’s robotic patrols is securely sent to Thrive Logic’s platform. From here, the Thrive Logic AI agent continues to track connected streams, triggering alerts to relevant staff and stakeholders, and generating automated incident workflows aligned to SOP if or when these are required.

The system allows enterprise security organisations to reduces operational friction, and see improvements in response consistency. The system will generate audit-ready, time-stamped incident records for all sites where the technology operates.

Damon Henry, CEO of Asylon Robotics, said: “Security leaders don’t need more dashboards – they need reliable coverage, consistent response, and defensible reporting. Robotic systems that extend perimeter presence, paired with AI that turns what’s observed into clear actions and documented outcomes. By integrating Asylon’s RSOC-managed robotic patrols with Thrive Logic’s agentic AI analytics and incident workflow automation, we’re giving enterprise teams a practical, scalable way to reduce response friction and elevate operational maturity across sites.”

Nate Green, CEO of Thrive Logic, also emphasised the importance of physical AI. “Physical AI is where security becomes truly operational – persistent real-world visibility paired with intelligence that drives action,” he said. “Asylon’s robotic patrols create a high-value mobile layer across large perimeters. When connected to Thrive Logic’s AI agent and workflow automation, that visibility becomes actionable alerts, guided response, and audit-ready documentation.”

You may have to wait your turn to experience the Asylon-Thrive Logic Physical AI integration as it’s currently only available for enterprise security teams managing high-activity exterior environments, but the companies are hoping for greater availability to all business sizes in the near future.

(Image by ikrzeus style from Pixabay)

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 co-located with other leading technology events. Click here for more information.

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

The post Asylon and Thrive Logic bring physical AI to enterprise perimeter security appeared first on AI News.