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To stop automation waste, enterprises must deploy interaction infrastructure that physically governs how independent AI agents operate. AI agents now populate corporate networks, reasoning through tasks and executing decisions with increasing autonomy. Yet, when these independent actors attempt to coordinate work, exchange context, or operate across varied cloud environments, the interaction framework degrades quickly. Human operators find themselves acting as the manual glue between disconnected systems, managing fragile integrations while the rules dictating permissions and data sharing remain implicit. Band, a startup based in Tel Aviv and San Francisco, has exited stealth mode with a $17 million seed round to address this infrastructure problem. The funding backs CEO Arick Goomanovsky and CTO Vlad Luzin in their effort to build a dedicated interaction layer for autonomous corporate systems. The concept mirrors earlier computing evolutions, wherein application programming interfaces required dedicated gateways and microservices necessitated a service mesh to function at scale. As distributed systems multiply under the ownership of different internal teams, adding more business logic fails to resolve the underlying instability. Rather, interaction reliability requires a distinct infrastructure layer. Market dynamics have changed in three key ways. First, autonomous actors have graduated from experimental deployments into active runtime participants managing engineering pipelines, customer support queries, and security operations. Enterprise usage is no longer a future consideration; it is an active operational state. The pressing issue involves managing what occurs when these distinct actors must collaborate. Second, the operational environment is entirely heterogeneous. Engineering teams build distinct tools across varied frameworks. These models execute on competing cloud platforms, utilise varying communication protocols, and report to separate business owners. No single vendor maintains control, and no uniform framework encapsulates the entire ecosystem. This fragmentation represents the permanent shape of the enterprise market. Third, a foundational standards layer is taking shape. Initiatives like the Model Context Protocol (MCP) afford models a uniform method for accessing external tools. Similarly, A2A communications efforts are establishing baseline conversational parameters. Yet, while protocols define the handshake, they fail to manage the production environment. Standardised protocols do not administer routing, error recovery, authority boundaries, human oversight, or runtime governance. They cannot manifest the shared operational space necessary for reliable interaction. Band intends to fill this infrastructure void. The financial liability of unmanaged automation Deploying independent models across business units creates compounding integration challenges. If point-to-point integrations must be hand-wired by internal development teams, the maintenance burden will drag down profit margins and delay product releases. The financial risk extends beyond simple integration costs. When autonomous actors pass instructions between themselves without a central governor, organisations face ballooning compute expenses. Multi-agent inference requires continuous API calls to expensive large language models. A failure in routing or a looping error between two confused entities can consume substantial cloud budgets within hours. Autonomous multi-agent workflows threaten this predictability if left unmanaged. An unmonitored negotiation between an internal procurement model and an external vendor model could trigger hundreds of inference cycles, inflating token usage costs beyond the value of the underlying transaction. Infrastructure layers must therefore implement hard financial circuit breakers, terminating interactions that exceed pre-defined token budgets or computational thresholds. Hardening the multi-agent execution layer Integrating these intelligent nodes with legacy corporate architecture demands intense engineering resources. Financial institutions and healthcare providers operate upon heavily fortified on-premises data warehouses, mainframe computation clusters, and customised enterprise resource planning applications. Without a hardened interaction infrastructure, the risk of data corruption multiplies with every automated step. A billing model might initiate a transaction while a compliance model simultaneously flags the same account, creating a database lock or conflicting entries. The interaction layer prevents these collisions. By enforcing capability limits, the infrastructure guarantees an autonomous entity cannot force unapproved modifications to primary source systems. Vector databases, which house the contextual memories required for retrieval-augmented generation, present a similar challenge. These storage systems are frequently configured in isolated environments tailored to individual use cases. If a technical support bot must transfer an ongoing customer interaction to a specialised hardware diagnostic bot, the contextual data must pass between isolated vector environments accurately. Data degradation happens when models are forced to interpret summarised outputs from other models rather than accessing the original, cryptographically verified data logs. Halting this degradation requires rigid contextual borders and a central interaction mesh capable of tracing the complete lineage of all shared information. The risk of data contamination creates liability issues. If a customer service model accidentally ingests highly classified financial data from an internal audit model during a contextual exchange, the compliance violation could trigger severe regulatory penalties. Establishing a secure communication mesh allows data officers to enforce highly specific access controls at the interaction layer rather than attempting to reconstruct the logic of individual models. Every digital interaction requires cryptographic logging to ensure regulatory bodies can trace automated decisions back to their exact origination point. Treating the communication mesh as a security perimeter The platform’s design rejects the notion of a monolithic model managing the entire enterprise. Instead, it anticipates teams of specialised participants holding different strengths and fulfilling distinct roles, operating synchronously without requiring identical architectures. Operating as a framework-agnostic and cloud-agnostic platform, the system acknowledges the value of existing tools. The market already possesses functional development frameworks. Band focuses on the operational phase, engaging when models leave the laboratory and enter the physical enterprise network as distributed entities. Governance constitutes the core of this strategy. A frequent error in enterprise technology deployments involves treating governance as a secondary feature, patched onto the system after initial deployment. This approach fails when applying it to autonomous enterprise actors. These systems delegate tasks, transfer context, and execute actions across organisational lines. If authority rules remain implicit and data routing lacks transparency, the operation will lack the necessary trust, even if it functions technically. To mitigate this risk, the underlying mesh must function as a security boundary. Organisations require mechanisms to inspect delegation chains, enforce strict authority limits, and retain comprehensive audit trails detailing runtime actions. Human participation must be integrated deeply into the execution layer. Collaboration mechanisms and governance controls must occupy the same infrastructure level. Without this foundation, the transition from single-model usage to a networked enterprise implementation will stall, hindered by compounding system failures and compliance violations. The companies that successfully deploy scalable operations will be those investing heavily in the underlying interaction infrastructure rather than simply accumulating impressive software demonstrations. See also: The billion-dollar startup with a different idea for AI 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 Why AI agents need interaction infrastructure appeared first on AI News. View the full article

AI systems are increasingly built around data that does not really pause. Financial markets are an obvious example, where inputs keep updating, not arriving in fixed batches. In that kind of setup, something like the BNB price stops being a single figure and starts to look more like a stream that keeps changing. Cryptocurrency markets tend to exaggerate that effect. Movement is not always smooth and patterns do not always repeat in a clean way. For AI models, that makes things harder, but also more useful in a way, because there is more to interpret. It is not always clear what matters straight away, which is part of the challenge. Why real-time cryptocurrency data is valuable for ai systems A lot of traditional datasets are static. They are collected, cleaned and then reused. Real-time market data does not behave like that. It keeps arriving and models have to deal with it as it comes in. That kind of input is useful when the goal is to spot changes and not rely on fixed assumptions. Instead of comparing against something from weeks ago, the system is working with what just happened. In some cases, even small shifts can be enough to trigger a response. And in many cases, the challenge is not collecting data but processing it quickly enough to be useful, especially in systems that rely on continuous updates from multiple sources. The scale matters as well. Binance insights note that Ethereum has seen daily transactions reach around 3 million, with active addresses exceeding 1 million. That level of activity points to the kind of high-frequency data environment these systems are working with. There is also just more data to deal with now. By the end of 2025, the total cryptocurrency market cap was sitting around $3 trillion after briefly crossing $4 trillion earlier in the year. Growth at that scale tends to show up as increased trading activity, more transactions and a larger volume of real-time inputs moving through these systems. Interpreting market signals in non-linear environments One of the main difficulties is that market behaviour is not especially tidy. Prices do not move in straight lines and cause and effect can blur together. Binance insights have highlighted conditions where market makers operate in negative gamma environments, where price movements can amplify themselves not settle. Different assets have been seen moving in similar directions but with varying intensity. For an AI system, that adds another layer to deal with. It is not about following one signal but understanding how several of them interact, even when the relationship is not stable. In practice, that can make short-term interpretation inconsistent. Data bias and signal weighting in AI models Another thing that shapes how models behave is the way data is distributed. Not all assets appear equally often in the data. Binance insights show that Bitcoin dominance has held at around 59%, while altcoins outside the top ten account for roughly 7.1% of the total market. That kind of distribution tends to influence how datasets are built and which signals appear most often. Smaller assets are still included, but their signals can be less steady. That makes them harder to use in systems that depend on regular updates. Sometimes they are included for coverage, not consistency. It is not always obvious at first, but this introduces a kind of bias. The model reflects what it sees most frequently and that can shape how it interprets new information later on. Infrastructure demands for AI-driven market analysis As more AI systems start working with this type of data, the underlying infrastructure becomes more important. It is not about collecting data but keeping it consistent over time. This is becoming easier to notice as more institutional players enter the space. Expectations tend to change with that. Data needs to be more consistent and there is less room for gaps or unclear outputs. As Richard Teng, Co-CEO of Binance, noted in February 2026, “we’re seeing more institutions entering the space and these institutions demand high standards of compliance, governance and risk management.” That kind of pressure shows up in how systems are put together. Pipelines cannot be unreliable and results need to make sense beyond just the model itself. It is not really enough for something to run if no one can explain what it is doing or why it reached a certain output. From market data to real-world AI applications Real-time pricing data is not only used for analysis. It is starting to show up in systems that operate continuously, where inputs feed directly into processes without much delay. Some setups focus on monitoring, others on identifying changes as they happen. In both cases, AI is used more to interpret than to decide. It sits somewhere in between raw data and action. There are also signs that this data is connecting more directly to real-world activity. Binance insights show that cryptocurrency card volumes rose five-fold in 2025 and reached around $115 million in January 2026, still small compared to traditional payment systems but growing steadily. AI models working with this kind of input are part of a broader environment where digital and traditional systems overlap. The boundaries are not always clear, which adds another layer of complexity. Real-time data on its own does not explain much. It just reflects what is happening. The role of AI is to make sense of it in a way that is consistent enough to be useful, even when the behaviour itself is uneven. As systems continue to develop, the way something like the BNB price is used will likely change as well. Not because the data changes, but because the way it is interpreted does. The post How AI models use real-time cryptocurrency data to interpret market behaviour appeared first on AI News. 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A billion dollars in startup funding for a company that employs 12 people is an indication that investors still have faith in AI. But the founder of the startup in question – AMI Labs’ Yann LeCun – believes that the breed of technology we currently term AI (large language models) is not the way through which it will develop meaningful and long-term results. Yann LeCun left his post as chief AI scientist at Meta late last year and founded Advanced Machine Intelligence Labs (AMI Labs) which, he asserts, will remain a research organisation not expected to produce a saleable product for maybe five years. The team at AMI Labs are concentrating not on huge, general-purpose language-based models, but AIs that comprise of collections of modular components, trained for and operating in specific use-cases. LeCun’s proposed system of artificial intelligence would comprise of the following types of elements: a world model specific to the domain in which the AI would operate. This might be industry-specific, or perhaps more likely, role-specific, an actor that proposes steps to take next, based on classical reinforcement learning, a critic that analyses the different options drawn from the world model and based on short-term memory, and assess the proposed steps according to hard-coded rules, a perception system that would be specific to the AI’s use: video or audio data, text, images, and so on using, for example, deep learning vision recognition algorithms, a short-term memory, a configurator that would orchestrate the movement of information between each of the above. Unlike large language models that have been trained on only one source of information (the text scraped from the internet), each instance of LeCun’s AI would be given directed data relevant only to their environment and purpose. In each version, the importance of each module might be set differently. For example, the critic module would be more comprehensive in areas that operate with sensitive information, or the perception module would be paramount in systems that need to react to real-world events quickly. Each module would be trained in ways that relevant to the AI’s particular field. There have been several successful instances of this in the past, such as machine-learning systems that can teach themselves how to play a video or board game, for example. These are in contrast to the large language models that underpin the vast majority of what we currently talk about when we talk about AI. LLMs are trained as generalists, creating best-guess answers based on what they have ingested, which are then subject to tweaking either by prompt engineering via software wrappers (Claude Code being the most well-known recently), or at a deeper level by means of reasoning models (the ‘thinking out loud’ portion of basic responses fed back into the AI’s prompt before the user sees the final answers.) The financial implications of AIs produced by the type of methods proposed by AMI Labs will be interesting to the current AI industry – assuming Yann LeCun’s ideas produce fruitful and viable results. Large language models from big technology providers (Anthropic, Meta, OpenAI, Google et al.) have consumed more resources with each iteration over the last five years. In addition to early-stage model size growth, the recursive prompting necessary to improve outputs from their later versions means that training and running large models becomes increasingly expensive, and only huge enterprises can afford to run them at a financial loss. The smaller, focused modules inside AMI Labs’ proposed solution could be run on fraction of the GPU power currently necessary for giant LLMs, or even on-device. Instead of the hundreds of billions of parameters models used by ChatGPT, for example, specialist models – that don’t need to be generalists – should need only a few hundred million parameters. This, and an assumption that the cost of computing will generally fall, mean that local, cheap, and inherently more accurate AI may be only a short step away. A startup with a new idea garnering enormous amounts of financial backing is nothing new in technology’s recent history. But at least part of LeCun’s strategy is based on his belief that current large language models cannot improve significantly enough to realise the aspirational claims made by their creators. AMI Labs seems to be offering investors a way that AI can perform successfully at some stage in the near future with an manageable cost, using a different architecture from the current norm. It’s a different proposition from what’s currently on the table from today’s AI behemoths, but the message of future potential is similar. (Image source: “Perspective on Modular Construction” by sidehike is licensed under CC BY-NC-SA 2.0.) 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 billion-dollar startup with a different idea for AI appeared first on AI News. View the full article

At the Google Cloud Next conference, Google and NVIDIA outlined their hardware roadmap designed to address the cost of AI inference at scale. The companies detailed the new A5X bare-metal instances, which run on NVIDIA Vera Rubin NVL72 rack-scale systems. Through hardware and software codesign, this architecture aims to deliver up to ten times lower inference cost per token compared to previous generations, while concurrently achieving ten times higher token throughput per megawatt. Connecting thousands of processors requires massive bandwidth to prevent processing delays. The A5X instances address this hardware challenge by pairing NVIDIA ConnectX-9 SuperNICs with Google Virgo networking technology. This configuration scales to 80,000 NVIDIA Rubin GPUs within a single site cluster, and up to 960,000 GPUs across a multisite deployment. Operating at this scale requires sophisticated workload management, as routing data across nearly a million parallel processors demands exact synchronisation to avoid idle compute time. Mark Lohmeyer, VP and GM of AI and Computing Infrastructure at Google Cloud, said: “At Google Cloud, we believe the next decade of AI will be shaped by customers’ ability to run their most demanding workloads on a truly integrated, AI‑optimised infrastructure stack. “By combining Google Cloud’s scalable infrastructure and managed AI services with NVIDIA’s industry‑leading platforms, systems and software, we’re giving customers flexibility to train, tune, and serve everything from frontier and open models to agentic and physical AI workloads—while optimising for performance, cost, and sustainability.” Sovereign data governance and cloud security requirements Beyond raw processing capabilities, data governance remains a primary issue for enterprise deployments. Highly regulated sectors, including finance and healthcare, often stall machine learning initiatives due to data sovereignty requirements and the risks of exposing proprietary information. To address these compliance mandates, Google Gemini models running on NVIDIA Blackwell and Blackwell Ultra GPUs are entering preview on Google Distributed Cloud. This deployment method allows organisations to retain frontier models entirely within their controlled environments, alongside their most sensitive data stores. The architecture incorporates NVIDIA Confidential Computing. This hardware-level security protocol ensures that training models operate within a protected environment where prompts and fine-tuning data remain encrypted. The encryption prevents unauthorised parties, including the cloud infrastructure operators themselves, from viewing or altering the underlying data. For multi-tenant public cloud environments, a preview of Confidential G4 VMs equipped with NVIDIA RTX PRO 6000 Blackwell GPUs introduces these same cryptographic protections, giving regulated industries access to high-performance hardware without violating data privacy standards. This release represents the first cloud-based confidential computing offering for NVIDIA Blackwell GPUs. Operational overhead in agentic AI training Building multi-step agentic systems requires connecting large language models to complex application programming interfaces, maintaining continuous vector database synchronisation, and actively mitigating algorithmic hallucinations during execution. To streamline this heavy engineering requirement, NVIDIA Nemotron 3 Super is now available on the Gemini Enterprise Agent Platform. The platform provides developers with tools to customise and deploy reasoning and multimodal models specifically designed for agentic tasks. The broader NVIDIA platform on Google Cloud is optimised for various models – including Google’s Gemini and Gemma families – giving developers the tools to construct systems that reason, plan, and act. Training these models at scale introduces heavy operational overhead, particularly when managing cluster sizing and hardware failures during long reinforcement learning cycles. Google Cloud and NVIDIA introduced Managed Training Clusters on the Gemini Enterprise Agent Platform, which includes a managed reinforcement learning API built with NVIDIA NeMo RL. This system automates cluster sizing, failure recovery, and job execution, allowing data science teams to concentrate on model quality rather than low-level infrastructure management. CrowdStrike actively utilises NVIDIA NeMo open libraries, including NeMo Data Designer and NeMo Megatron Bridge, to generate synthetic data and fine-tune models for domain-specific cybersecurity applications. Operating these models on Managed Training Clusters with Blackwell GPUs accelerates their automated threat detection and response capabilities. Legacy architecture integration and physical simulations The integration of machine learning into heavy industry and manufacturing presents a different class of engineering challenges. Connecting digital models to physical factory floors requires exact physical simulations, massive compute power, and standardisation across legacy data formats. NVIDIA’s AI infrastructure and physical AI libraries are now available on Google Cloud, providing the foundation for organisations to simulate and automate real-world manufacturing workflows. Major industrial software providers – such as Cadence and Siemens – have made their solutions available on Google Cloud, accelerated by NVIDIA infrastructure. These tools power the engineering and manufacturing of heavy machinery, aerospace platforms, and autonomous vehicles. Manufacturing firms often run on decades-old product lifecycle management systems, making the translation of geometry and physics data difficult. By utilising NVIDIA Omniverse libraries and the open-source NVIDIA Isaac Sim framework via the Google Cloud Marketplace, developers can bypass some of these translation issues to construct physically accurate digital twins and train robotics simulation pipelines prior to physical deployment. Deploying NVIDIA NIM microservices, such as the Cosmos Reason 2 model, to Google Vertex AI and Google Kubernetes Engine enables vision-based agents and robots to interpret and navigate their physical surroundings. Together, these platforms help developers advance from computer-aided design directly to living industrial digital twins. Impacts across the accelerated compute ecosystem Translating these hardware specifications into quantifiable financial returns requires inspecting how early adopters utilise the infrastructure. The broad portfolio includes options scaling from full NVL72 racks down to fractional G4 VMs offering just one-eighth of a GPU. This allows customers to precisely provision acceleration capabilities for mixture-of-experts reasoning and data processing tasks. Thinking Machines Lab scales its Tinker API on A4X Max VMs to accelerate training. OpenAI uses large-scale inference on NVIDIA GB300 and GB200 NVL72 systems on Google Cloud to handle demanding workloads, including ChatGPT operations. Snap transitioned its data pipelines to GPU-accelerated Spark on Google Cloud to cut the extensive costs associated with large-scale A/B testing. In the pharmaceutical sector, Schrödinger leverages NVIDIA accelerated computing on Google Cloud to compress drug discovery simulations that previously took weeks into a matter of hours. The developer ecosystem scaling these tools has expanded quickly. Over 90,000 developers joined the joint NVIDIA and Google Cloud developer community within a year. Startups like CodeRabbit and Factory apply NVIDIA Nemotron-based models on Google Cloud to execute code reviews and run autonomous software development agents. Aible, Mantis AI, Photoroom, and Baseten build enterprise data, video intelligence, and generative imagery solutions using the full-stack platform. Together, NVIDIA and Google Cloud aim to provide a computing foundation designed to advance experimental agents and simulations into production systems that secure fleets and optimise factories in the physical world. See also: Reversing enterprise security costs with AI vulnerability discovery 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 NVIDIA and Google infrastructure cuts AI inference costs appeared first on AI News. View the full article

An autonomous table tennis robot developed by Sony AI has competed against and defeated high-level human players in regulated matches, according to Reuters. The system is part of a broader category often referred to as “physical AI,” where artificial intelligence is applied to machines operating in real-world environments. The robot, named Ace, was designed to operate in a competitive sport environment that requires rapid decision-making and precise motor control. According to the project team, it combines high-speed perception systems with AI-driven control to execute shots under match conditions. Ace competed in matches conducted under International Table Tennis Federation rules and officiated by licensed umpires. In trials documented in April 2025, the system won three out of five matches against elite players and lost two against professional-level opponents. Sony AI reported that subsequent matches in December 2025 and early 2026 included wins against professional players. Previous table tennis robots have existed since the 1980s, but they were not able to match the performance of advanced human players. “Unlike computer games, where prior AI systems surpass human experts, physical and real-time sports like table tennis remain a major open challenge,” said Peter Dürr, director at Sony AI Zurich and lead of the project. AI systems have achieved strong results in digital environments like chess and video games, where conditions are fully simulated, Dürr said. Dürr said the system was developed to study how robots can respond with speed and accuracy in dynamic environments. The work was detailed in a study published in the journal Nature. The sport presents technical challenges due to the speed and variability of the ball, including complex spin and changing trajectories, which require rapid sensing and coordinated movement in tight time constraints, Dürr said. Ace’s architecture includes nine synchronised cameras and three vision systems, which track the ball’s movement and spin. The system processes visual data at a speed sufficient to capture motion that is difficult for the human eye to resolve. “This is fast enough to capture motion that would be a blur to the human eye,” Dürr said. The robotic platform uses eight joints to control the racket. Three control positioning, two control orientation, and three manage shot force and speed. The configuration was designed to meet the minimum mechanical requirements for competitive play. Unlike many AI systems trained through human demonstration, Ace was trained in simulation. The approach allowed it to develop its own strategies, resulting in play patterns that differ from human opponents. Dürr said the system “learns to play not from watching humans” but through self-training in simulated environments. Professional player Mayuka Taira, who lost a match to the system, said the robot was difficult to predict because it shows no visible cues during play. Rui Takenaka, an elite player who both won and lost against Ace, said it handled complex spins well but was more predictable on simpler serves. Taira said the system’s lack of emotional signals made it harder to anticipate its responses. “Because you can’t read its reactions, it’s impossible to sense what kind of shots it dislikes or struggles with,” she said. Dürr said the system demonstrates strong ability in reading ball spin and reacting quickly, while ongoing work focuses on improving adaptability during matches. The project team said similar perception and control techniques could be applied to areas like manufacturing and service robotics. Humanoid robots tested in long-distance race At the 2026 Beijing E-Town Humanoid Robot Half Marathon, humanoid robots competed over a 21-kilometre course in Beijing. The event included more than 100 robots and approximately 12,000 human participants, who ran on separate tracks. A robot named Lightning, developed by Honor, completed the race in 50 minutes and 26 seconds. The time was faster than Olympic runner Jacob Kiplimo’s 57 minutes and 20 seconds recorded at the Lisbon Half Marathon in March. Lightning collided with a barricade during the race but continued and finished first. Honor robots also placed second and third in the competition. Performance improved compared to the previous year’s event, where the fastest robot completed the course in two hours, 40 minutes and 42 seconds. Organisers said the event was intended to test humanoid robots in large-scale, real-world conditions. According to Associated Press, another Honor robot completed the course in 48 minutes under remote control. However, race rules prioritised autonomous navigation, and Lightning was recognised as the official winner. Honor engineers said technologies developed for the robot, including structural reliability and liquid-cooling systems, could be applied in industrial scenarios. (Photo by Mattias Banguese) See also: Cadence expands AI and robotic partnerships with Nvidia, Google Cloud 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 Sony AI robot beats players as humanoid robot wins Beijing race appeared first on AI News. View the full article

Automated AI vulnerability discovery is reversing the enterprise security costs that traditionally favour attackers. Bringing exploits to zero was once viewed as an unrealistic goal. The prevailing operational doctrine aimed to make attacks so expensive that only adversaries with functionally unlimited budgets could afford them, thereby disincentivising casual use. However, the recent evaluation by the Mozilla Firefox engineering team – using Anthropic’s Claude Mythos Preview – challenges this accepted status quo. During their initial evaluation with Claude Mythos Preview, the Firefox team identified and fixed 271 vulnerabilities for their version 150 release. This followed a prior collaboration with Anthropic using Opus 4.6, which yielded 22 security-sensitive fixes in version 148. Uncovering hundreds of vulnerabilities simultaneously puts a heavy strain on a team’s resources. But in today’s strict regulatory climate, doing the heavy lifting to prevent a data breach or ransomware attack easily pays for itself. Automated scanning also drives down costs; because the system continuously checks code against known threat databases, firms can cut back on hiring costly external consultants. Overcoming compute expenditure and integration friction Integrating frontier AI models into existing continuous integration pipelines introduces heavy compute cost considerations. Running millions of tokens of proprietary code through a model like Claude Mythos Preview requires dedicated capital expenditure. Enterprises must establish secure vector database environments to manage the context windows needed for vast codebases, ensuring proprietary corporate logic remains strictly partitioned and protected. Evaluating the output also demands rigorous hallucination mitigation. A model generating false-positive security vulnerabilities wastes expensive human engineering hours. Therefore, the deployment pipeline must cross-reference model outputs against existing static analysis tools and fuzzing results to validate the findings. Automated security testing relies heavily on dynamic analysis techniques, particularly fuzzing, run by internal red teams. While fuzzing is highly effective, it struggles with certain parts of the codebase. Elite security researchers overcome these limitations by manually reasoning through source code to identify logic flaws. This manual process is time-consuming and constrained by the scarcity of elite human expertise. The integration of advanced models eliminates this human constraint. Computers, completely incapable of this task just months ago, now excel at reasoning through code. Mythos Preview demonstrates parity with the world’s best security researchers. The engineering team noted they have found no category or complexity of flaw that humans can identify which the model cannot. Also encouragingly, they haven’t seen any bugs that could not have been discovered by an elite human researcher. While migrating to memory-safe languages like Rust provides mitigation for certain common vulnerability classes, halting development to replace decades of legacy C++ code is financially unviable for most businesses. Automated reasoning tools offer a highly cost-effective method to secure legacy codebases without incurring the staggering expense of a complete system overhaul. Eliminating the human discovery constraint A large gap between what machines can discover and what humans can discover heavily favours the attacker. Hostile actors can concentrate months of costly human effort to uncover a single exploit. Closing the discovery gap makes vulnerability identification cheap, eroding the long-term advantage of the attacker. While the initial wave of identified flaws feels terrifying in the short term, it provides excellent news for enterprise defence. Vendors of vital internet-exposed software have dedicated teams aiming to protect users. As other technology firms adopt similar evaluation methods, the baseline standard for software liability will change. If models can reliably find logic flaws in a codebase, failing to use such tools could soon be viewed as corporate negligence. Importantly, there is no indication that these systems are inventing entirely new categories of attacks that defy current comprehension. Software applications like Firefox are designed in a modular fashion to allow human reasoning about correctness. The software is complex, but not arbitrarily complex. Software defects are finite. By embracing advanced automated audits, technology leaders can actively defeat persistent threats. The initial influx of data demands intense engineering focus and reprioritisation. However, teams that commit to the required remediation work will find a positive conclusion to the process. The industry is looking toward a near future where defence teams possess a decisive advantage. See also: Anthropic walks into the White House and Mythos is the reason Washington let it in 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 Reversing enterprise security costs with AI vulnerability discovery appeared first on AI News. View the full article

In an interview with Artificial Lawyer, Paris-based AI-native consulting firm owner, Olivier Chaduteau, set out a three-part account of the current state of AI in the legal sector. At first, lawyers dismissed AI as irrelevant to expert work. In the second, organisations bought licences to LLMs to signal activity to partners and/or clients, but little else. He says the market has now entered a third stage, in which firms understand it’s time to engage with the AI tools at their disposal. Chaduteau said to engage with AI on an operational level, firms should focus on change management, choosing the right operating models, and reforming their business models. It’s necessary to rewrite workflows, re-train the lawyers on the books, set standards for AI use, and decide where human review needs to be in the workflow. These are, he acknowledged, political questions that are much more challenging than stage one’s decision, which comprised largely of which large language model or law-specific AI service to buy into. For law practices, the presence of AI in the workflow may instigate a farewell to cost-plus pricing and hourly billing, with firms adopting what he terms value pricing instead – something that many firms have considered and in some cases, already adopted independently of technological issues. There are questions posed about fundamental billing methods, after all, if firms use AI to reduce the time spent drafting papers or reviewing documents, and can undertake research more quickly. The correlation between a lawyer’s time and income is weakened, and law companies may have to start thinking differently. Senior managers at law firms have two choices, therefore. AI can be used inside existing billing models for as long as possible, optimising the ratio of cost to revenue. The other is to redesign the firm’s service and prices in line with an AI-enabled, streamlined workflow, and offer clients services based on a new billing model that’s reflective of the automation in play at the law office. Chaduteau’s view is that clients will eventually force the issue – someone, somewhere will begin to offer better value based as a result of their increased efficiencies (most likely a new company unencumbered by traditional billing practices) and the rest of the market will be forced to respond and offer the same. This is a classic case of technological disruption. Chaduteau said corporate legal departments are increasingly pressured to show how they are implementing AI in workflows, in line with other business functions in the enterprise, a pressure that’s is likely to matter more, in the long run, than any amount of internal enthusiasm. Demands for evidence of competence and efficiency are not unique to internal law departments – the same is happening right across enterprises that have invested significantly in AI. Chaduteau said that he thinks AI capability will become part of panel selection, pitch processes, and ongoing client scrutiny during the selection process for work. Practices may have to give details on which tasks are supported by AI, what safeguards are in place, how client confidentiality is protected in the context of those systems, and what measurable effect the tools have on the firm’s speed and quality of service. Chaduteau did not frame the technology solely as a cost-reduction tool, but one that lets lawyers have more room in their working days for more interesting work. Lawyers, like any profession, are more likely to engage seriously with a technology reduces the amount of routine tasks that give little back in terms of job satisfaction. In large firms, that points to practice-level uses and basic supervision, and every use-case will be different. Large law firms are moving from symbolic adoption towards changes to their operations because of AI’s abilities, Chaduteau claimed. The firms that benefit are likely to be the ones that treat AI as a management decision before it becomes an issue pressed upon them. That means disciplined implementation, client-facing proof of value, careful treatment of confidentiality and sovereignty, and a willingness to examine whether the billing model still fits the work, he said. (Image source: 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 AI in law firms entering its closing summaries appeared first on AI News. View the full article

Artificial intelligence has become a defining force in financial markets. And currency trading is no exception. The rise of the AI-powered forex bot reflects a change toward automated systems capable of processing vast amounts of market data and identifying patterns beyond the reach of manual analysis. As global foreign exchange markets operate around the clock and generate enormous streams of information, traders increasingly rely on intelligent tools that can analyse, interpret and act on market signals in real time. Modern forex robots are not limited to rigid rule-based algorithms. Instead many incorporate artificial intelligence techniques that allow them to adapt to changing market conditions, evaluate risk more effectively and improve performance through continuous learning. Understanding how AI is shaping these systems offers insight into the future of automated trading and the evolving relationship between human decision-makers and machine intelligence. From rule-based automation to intelligent systems Early forex robots were primarily built on static trading strategies. Developers programmed them with predefined rules like entering a trade when a moving average crossed a certain threshold or exiting when a price reached a specific level. While this approach automated basic tasks, it struggled whenever market conditions shifted. Artificial intelligence introduced a new level of flexibility. Instead of relying solely on fixed rules, AI models can analyse historical market behaviour and identify complex relationships between variables like price movements, volatility levels and macroeconomic indicators. This makes trading systems far more adaptable. Some of the biggest differences between traditional forex robots and AI-driven systems include: Data-driven learning: AI models train on historical datasets and identify patterns without relying entirely on manually coded rules. Adaptability: Machine learning systems can adjust strategies as new data becomes available. Pattern recognition: AI can identify subtle relationships between variables that traditional algorithms often miss. Continuous improvements: Models can be retrained regularly. This allows the trading system to evolve with market changes. These abilities have expanded what automated trading systems can achieve. Core AI technologies used in forex robots Several artificial intelligence techniques now contribute to the development of advanced forex trading systems. Each technology plays a different role in analysing market conditions and supporting trading decisions. Common AI technologies used in modern forex robots include: Machine learning models: These analyse historical currency data to identify patterns and generate predictive signals. Natural language processing: This allows trading systems to scan financial news, economic reports and central bank announcements to identify sentiment shifts that could influence currency prices. Deep learning architectures: Neural networks with multiple layers can evaluate complex relationships between technical indicators and price movements. Reinforcement learning: Algorithms learn through trial and error, improving strategies based on rewards or penalties tied to trading outcomes. Together these tools let trading systems process large volumes of information and respond quickly to changing market dynamics. Enhancing risk management and decision making One of the most valuable contributions of artificial intelligence in forex robot development is strong risk management. Currency markets can be volatile, and experienced traders struggle to evaluate every possible risk factor. AI-driven systems are designed to monitor multiple signals at the same time. They can evaluate price movements, volatility patterns, liquidity changes and correlations between currency pairs. The broader view allows automated systems to identify potential warning signs earlier than traditional methods. For example AI-based trading tools can: Analyse volatility spikes that might indicate unstable market conditions Detect unusual correlations between currency pairs Adjust position sizes based on current market risk Automatically exit trades when predefined risk thresholds are reached These abilities have made the AI-powered forex bot an increasingly sophisticated tool for traders who want both efficiency and improved decision support. Challenges and considerations Despite their advantages, AI-driven forex robots are not perfect. Markets can behave unpredictably. Especially during unexpected economic events or geopolitical developments. Several factors still require careful attention when using AI-based trading systems: Data quality: Machine learning models depend on accurate and well-structured datasets. Poor data can lead to misleading predictions. Overfitting risks: Models trained too heavily on historical data may perform well in testing but struggle in real market conditions. Regulatory oversight: As automated trading becomes more advanced, regulators continue to examine how algorithmic systems operate in currency markets. Human supervision: Even advanced systems benefit from regular monitoring and adjustments. Understanding these limitations helps traders and developers use AI tools more effectively. The future of AI in forex trading Artificial intelligence will likely continue transforming how forex robots are designed and used. Improvements in machine learning models, computing power and data processing are making automated trading systems more capable each year. Developers are already experimenting with hybrid AI models that combine multiple learning techniques to improve predictive accuracy. Broader data integration may allow trading systems to analyse an even wider range of information sources, including global economic indicators and cross-market signals. While human expertise remains important, intelligent automation is clearly changing currency trading. As AI technology continues to evolve, the role of automated systems in forex markets will likely become even more significant. The post The role of AI in modern forex bot development appeared first on AI News. View the full article

Snowflake is expanding its Snowflake Intelligence and Cortex Code offerings in the hope of bringing users deploying and developing artificial intelligence inside the Snowflake portfolio. Snowflake Intelligence is framed as a tool for generalised business users, while Cortex Code is destined for developers and technical teams’ desks. A press release from the company lists additional features on both platforms, including an increase in the number of integrations they have with third-party software. It also details new automation features and simpler, web-based methods of building agentic AI workflows. White collar and beyond Snowflake Intelligence, aimed at non-technical staff, is among the platforms on the market today that advertise an ability to execute tasks inside existing business workflows. Users can describe to the LLM what they’d like to see happen in natural language, and it execute given tasks. Snowflake lists preparing presentations, running multi-step analyses, and sending follow-up messages as some of the uses it envisages. Data can be drawn from an organisation’s internal and linked digital assets, including structured and unstructured data, with external sourcese connected by various protocols and pre-built connectors. User queries and ensuing workflows will be carefully limited in terms of access permissions and organisational governance, helping to prevent incidents of data loss and non-compliance. New interfaces using MCP (Model Context Protocol) are available, and the company has named the Google business suite, Jira, and Salesforce (including Slack) as among the systems Snowflake Intelligence can now interface with. Also in the works is an iOS app for Snowflake Intelligence which will enter public preview “soon”. Snowflake says its Intelligence platform becomes more personalised over time, learning from user behaviour. Users will be able to save and share workflows so that work can be reused. Longer context windows – personalisation – mean that users addressing the large language model should not have to repeat long, contextualised prompts to get the results they want. The updates have come about as a result of feedback from Project SnowWork, a research project launched last month to showcase the platform and garner users’ preferences as to what features they’d like to see from an AI platform. Snowflake in the development toolkit Cortex Code is designed for software development teams in the enterprise, an area in which AI algorithms can prove successful at lower level tasks. Cortex Code is described in company press release as a coding and orchestration “layer” with new options for integration with external data sources, now supporting AWS Glue, Databricks, and Postgres. Cortex Code can also connect to other language models via MCP and ACP (agent communication protocol), the more commerce-driven protocol that emerged around the same time as the Anthropic-stewarded MCP. VS Code users will soon see Cortex Code as an extension (it’s currently in private preview), and a Snowflake plugin for Claude Code is currently under development. Snowflake’s Agent Software Development Kit for Python and TypeScript is available, so teams can embed Cortex Code functions in their own applications. Cloud Agents, also in private preview, are to appear in Snowsight, Snowflake’s browser-based interface. Plan Mode lets users preview and approve workflows before AI execution, and the company is working on a facility by which end-users can see detail of longer research processes the LLM undertakes to vet the veracity of its processes. Snowflake says more than 9,100 customers use its AI products weekly. Since its launch six months ago, Snowflake says more than half of its customers are using Snowflake Intelligence and Cortex Code. The company’s dual-pronged approach – mainstream business users and software development teams – doubles down on the company’s core technical market, but widens its the platform’s adoption among general business function users. The new software connectors, mobile app, and browser-based options will create a broader market of users, and the additional support for existing systems will widen its appeal among enterprises with embedded workflows and software platforms. Sameer Vuyyuru, chief AI and product officer at Capita, said: “Snowflake helps us deploy AI securely and with the right governance across highly regulated, citizen-facing services where performance, compliance and trust are critical.” (Image source: “The snow” by telafree is licensed under CC BY-NC 2.0.) 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 Snowflake expands its technical and mainstream AI platforms appeared first on AI News. View the full article

Siemens has introduced the Eigen Engineering Agent, an AI system designed to plan and validate automation engineering tasks in operational environments. The system uses multi-step reasoning and self-correction to carry out tasks autonomously and operates directly inside engineering platforms, letting it to complete workflows from initial design through to validation. Autonomous engineering workflows The agent is designed to interpret project requirements, generate automation code, configure industrial systems, and refine outputs until predefined performance targets are achieved. This includes tasks like programmable logic controller (PLC) programming, human-machine interface (HMI) setup, and device configuration. It is built to meet industrial requirements for correctness and reliability during execution. The system connects to Siemens’ Totally Integrated Automation Engineering platform, TIA Portal, letting it access project-specific data like structures and component relationships. This lets it generate outputs aligned with existing system configurations, including legacy or undocumented environments. It can reference control logic, system hierarchies, and component dependencies in a project, allowing outputs match existing engineering standards without requiring manual translation. The system executes tasks through a workflow that breaks down engineering problems into steps, processes them sequentially, and evaluates results against project requirements. It iterates until outputs meet the specified criteria before presenting them for an engineer’s review. Industry estimates point to a global shortfall of up to seven million manufacturing workers by 2030, with some sectors reporting that around one in five engineering roles remain unfilled. According to Siemens, the system executes tasks two to five times faster than manual workflows while maintaining accuracy. Deployment in industrial workflows In pilot deployments involving more than 100 companies in 19 countries, the Siemens system was applied to standard automation engineering processes. Participating organisations included ANDRITZ Metals, CASMT, and Prism Systems. Prism Systems used the system to generate and import structured control language (SCL) code, reducing execution time for these tasks. In another case, CASMT applied the system to automate device configuration, code generation, and HMI visualisation in production line development. CASMT reported reduced specialist hand-offs in engineering disciplines and shorter delivery timelines. The Eigen Engineering Agent is integrated into Siemens’ Totally Integrated Automation Engineering platform, TIA Portal, which has more than 600,000 users. It is available as part of the company’s Xcelerator portfolio and can be accessed digitally. Industry constraints and workforce gaps Surveys of manufacturing organisations indicate that while most companies report having large volumes of operational data, data quality and contextualisation remain important barriers. In addition to general labour shortages, manufacturers also face a shortage of workers with the technical skills needed to run AI systems in industrial environments. Initial deployments focus on automation engineering workflows, but the system is structured to extend into other areas of the industrial value chain. Siemens positions the development as part of broader efforts to embed AI into industrial operations and software systems. The release follows Siemens’ previously announced €1 billion investment in industrial AI. The company reports having more than 1,500 AI specialists and over 2,000 AI-related patent families globally, supporting ongoing development of AI-based engineering and operational tools. (Photo by Homa Appliances) See also: Cadence expands AI and robotic partnerships with Nvidia, Google Cloud 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 Siemens introduces AI system for automation engineering appeared first on AI News. View the full article

AI platform, Bobyard, has unveiled Bobyard 2.0, its latest platform update delivering accelerated takeoff workflows and a unified AI workbench, designed to keep pace with the estimators (those responsible for calculating project budgets) who use it every day throughout the construction and landscaping industry. By speeding up takeoff operations, a important part of estimating a project where details of materials and quantities needed for a construction job are analysed, contractors can reduce the risk of the errors possible with manual measurements. Ultimately, faster takeoffs can lead to fewer costly surprises during the construction process. Bobyard 2.0 features Bobyard 2.0 integrates materials and their costs with a ‘measure first, price later’ model, designed to save time and reduce errors from takeoff to finalised bids. A new Multi-Measure feature lets estimators to draw once, and not have to create separate lines or shapes to calculate an area, perimeter, or total volume. The system generates all related measurements simultaneously, reducing the need for measurements. Bobyard has also connected its AI tools in an AI Workbench that includes a Review Workflow option to let estimators to check and decide whether to trust or adjust certain AI outputs before adding them to their takeoff. Another updated feature providing flexibility is Legend Manager, that gives users a “dedicated space to create and run symbol and pattern legends.” Text Count lets users turn words or labels in drawings into count measurements. According to Bobyard, “once your takeoff is done, you shouldn’t have to rebuild anything to get to a finalised estimate.” Moreover, importing pricing and assemblies into Bobyard 2.0’s Estimate Table is “simpler and more streamlined” letting users “move from takeoff to a production-ready estimate without exporting anything to Excel or redoing anything just to see it in an estimate context.” In other words, it places itself as a big update for estimators. Improvements to navigation means greater ease of use, with cross-page search introduced as a feature and a generally streamlining of the workflow from takeoff to final, ready-to-use estimates, without needing to leave the platform or switch tools. Bobyard raised $35 million Series A funding last year, led by 8VC with Pear VC and Caffeinated Capital. The company says its platform currently automates up to 70% of the quantity and material takeoff process with contractors using Bobyard reporting an average reduction in takeoff times of 65%. Estimators are reportedly submitting three to five times more bids while enjoying more accurate results with improved margins and win rates. Michael Ding, founder and CEO of Bobyard, said: “Every change we made was driven by what our customers told us they needed to move faster, stay in control, and spend less time doing work that software should be doing for them. This is our answer to that, and it’s only the beginning.” Marty Grunder, founder of The Grow Group and Grunder Landscaping, said: “The AI tools are on another level. We’re talking cutting takeoff time in half on real jobs. If you’re trying to level up your estimating this season, there’s nothing else like this on the market. *******.” Launched on April 8 for landscaping contractors, Bobyard 2.0 is expected to be available for additional construction trades in late April. (Image source: “Contractors continue renovations on Vicenza conference center” by USACE Europe District is licensed under CC BY 2.0. To view a copy of this license, visit [Hidden Content]) 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 Bobyard 2.0 offers improved takeoffs and unified AI for estimators appeared first on AI News. View the full article

For all the possibilities AI gives us, there is always a chance of the technology malfunctioning or becoming compromised. In the event of an AI system crisis, new research from ISACA has found that the majority of organisations surveyed couldn’t explain how quickly they could stop an AI system emergency, or even report on what caused the issue. According to ISACA’s report, 59% of digital trust professionals didn’t understand how quickly their organisation could interrupt and halt an AI system during a security incident. Just 21% reported that they could meaningfully step in in half an hour. The indicates a landscape where corrupted AI systems can continue to operate unchecked, leading to a risk of irreversible damage. Ali Sarrafi, CEO & Founder of Kovant, an autonomous enterprise platform, said, “ISACA’s findings point to a major structural issue in the way that organisations are deploying AI. Systems are being embedded into critical workflows without the governance layer needed to supervise and audit their actions. If a business cannot quickly halt an AI system, explain its behaviour, or even identify who is to be held accountable, the business is not in control of that system.” AI failures and risks In all, only 42% of respondents expressed any confidence in their organisation being able to analyse and clarify serious AI incidents, thus leading to possible operational failures and security risks. Moreover, without explaining these incidents to regulators and leadership, businesses may face legal penalties and public backlash. Proper analysis is needed to learn from mistakes. Without a clear understanding, the likelihood of repeated incidents only increases. It’s important is to manage AI responsibly, with effective AI governance, yet ISACA’s findings indicate this is often missing. Accountability is another fuzzy area with 20% reporting that they do not know who would be responsible if an AI system caused damage. Just 38% identified the Board or an Executive as ultimately responsible. Sarrafi noted that slowing down AI adoption is not the answer; instead, rethinking how it is managed is key. “AI systems need to sit in a structured management layer that treats them as digital employees, with clear ownership, defined escalation paths, and the ability to be paused or overridden instantly when risk thresholds are crossed. The way, agents stop being mysterious bots and become systems you can inspect and trust. As AI becomes more deeply embedded in core business functions, governance cannot be an afterthought. It has to be built into the architecture from day one, with visibility and control designed in at every level. The organisations that get this right will not reduce risk, they will be the ones that can confidently scale AI in the business.” There is some reassurance, however, with 40% of respondents saying humans approve almost all AI actions before being deployed, and a further 26% evaluate AI outcomes. That being said, without an improved governance infrastructure, human oversight is unlikely to be enough to identify and resolve issues before escalating. ISACA’s findings point towards a major structural issue in how AI is being deployed in different sectors. With over a third of organisations not requiring their employees to disclose where and when AI is used in work products, the potential for blind spots increases. Despite more stringent regulations that make senior leadership more accountable, organisations are failing to implement and use AI safely and effectively. It seems many businesses are treating AI risk as a technical problem, not as something that requires careful management in the entire organisation. Change to how the integration and actions of AI are handled is essential. Without proper governance and accountability, businesses are not in control of their AI systems. Without control, even the smallest errors could cause reputational and financial harm that many businesses may not recover from. (Image by Foundry Co 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 How to prepare for and remediate an AI system incident appeared first on AI News. View the full article

When we covered Project Glasswing earlier this month, the story was about a model too dangerous to release publicly and what Anthropic decided to do with it instead. That story has moved. On Friday, Anthropic CEO Dario Amodei walked into the West Wing for a meeting with White House Chief of Staff Susie Wiles. Treasury Secretary Scott Bessent was also in the room. The White House called the talks “productive and constructive.” Anthropic said the same. When a reporter asked President Trump about the visit on a runway in Phoenix, he responded “Who?” and said he had “no idea” Amodei was there. That detail aside, the meeting itself is one of the more striking political reversals in recent AI history. Just weeks ago, the Trump administration had declared Anthropic a supply chain risk – a designation ordinarily reserved for foreign adversaries – and Trump himself said the administration would “not do business with them again.” A federal judge in San Francisco has since blocked the enforcement of that directive, keeping Anthropic eligible to work with non-military agencies while the litigation plays out. The Pentagon dispute remains very much alive. What changed the calculus – at least at the White House level – was Anthropic Mythos AI cybersecurity ability. Specifically, the fact that agencies are purportedly watching Mythos do things no other tool can, and are not willing to sit that out. The model and the politics As we reported when Anthropic unveiled Project Glasswing, Mythos Preview was not trained specifically for security work. Its ability to autonomously identify and exploit software vulnerabilities emerged from general improvements in reasoning and code, and what it has found since deployment has been striking. During internal testing, Mythos located thousands of previously unknown, high-severity vulnerabilities in every major operating system and web browser, including a 27-year-old bug in OpenBSD and a 16-year-old flaw in FFmpeg that had passed automated testing five million times without detection. Rather than ship it publicly, Anthropic released it only to a select group of organisations through Project Glasswing – a coalition that includes AWS, Apple, Cisco, Google, Microsoft, Nvidia, CrowdStrike, and JPMorganChase, among others – backed by up to US$100 million in use credits. The model is being used offensively, in a controlled sense: finding the vulnerabilities before someone else does. The US government has been watching that coalition operate and wanting in. Intelligence agencies and the Cybersecurity and Infrastructure Security Agency are already testing Mythos, and the Treasury Department has also expressed interest, according to Axios. Treasury and other government agencies have expressed interest in joining the Glasswing list, and before Friday’s White House meeting, two sources told Axios a deal along those lines could be struck soon. In a separate Axios report, a concern brought up is that Mythos and other cutting-edge AI tools could allow hackers to breach the US financial system. Alternatively, the report reckoned companies and government agencies could use Mythos to harden their cyber defences before bad actors get access. That dual-use tension is now squarely a political problem. National Cyber Director Sean Cairncross is set to lead a group of federal officials to identify security vulnerabilities in critical infrastructure and strengthen government systems against AI exploitation. Where the standoff stands The Friday meeting was engineered to separate two conversations that had become entangled. Going into the session, both sides sought to wall off the Pentagon fight from how the rest of the government engages with Anthropic and next steps are expected to be about how other departments access Mythos Preview, per sources familiar with the negotiations. One Trump adviser told Axios: “This is a big problem. Everyone’s complaining. There’s all this drama. So this got elevated to Susie to hear Dario out, determine what is bull and start to plot a way forward.” An administration official summarised the current dynamic succinctly: “There’s progress with the White House. There’s no progress with [the Department of] War.” That split is telling. Civilian agencies like the Departments of Energy and Treasury are responsible for safeguarding critical sectors, like the electric grid and the financial system. Their concerns are not about autonomous weapons or surveillance. They want the ability Mythos offers, and they are not willing to be collateral damage in a fight between the Pentagon and an AI company. The DOD has not commented on Mythos but has continued using Anthropic’s Claude models in the war with Iran. That footnote is worth sitting with. Publicly, Anthropic has also been making moves that signal it understands how Washington works. Public filings show Anthropic recently hired lobbying firm Ballard Partners – where Wiles worked for years – specifically for advocacy regarding Department of War procurement. What comes next The litigation has not ended. A federal appeals court denied Anthropic’s request to temporarily block the Pentagon’s blacklisting; a San Francisco judge granted a preliminary injunction in a separate case. Anthropic remains barred from DoD contracts but can continue working with the rest of the government while both cases run their course. The White House said it plans to continue dialogue with Anthropic and other AI companies, and the Office of Management and Budget is already preparing to give agencies access to Mythos to assess their defences, according to Bloomberg. That is meaningful progress, even if the Pentagon remains the unresolved piece. One source close to the negotiations put it plainly: “It would be grossly irresponsible for the US government to deprive itself of the technological leaps that the new model presents. It would be a gift to China.” That framing – less about Anthropic’s legal standing, more about what the US cannot afford to give up – is what brought Amodei into the West Wing. Whether the Pentagon ever follows is a different question. See also: Anthropic’s refusal to arm AI is exactly why the *** 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. This 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 Anthropic walks into the White House and Mythos is the reason Washington let it in appeared first on AI News. View the full article

Cloud migration becomes much harder when teams are not moving workloads, but also trying to make every environment reproducible and deployment-ready through Infrastructure as Code. The challenge is not limited to copying applications from one platform to another. It includes validating architecture decisions, controlling drift, enforcing policies, coordinating approvals, and making sure deployment logic can scale in teams and cloud accounts. In that kind of environment, cloud migration software needs to support both planning and execution. That is where platforms like Infros stand out. Infros is built around cloud architecture design and validation, helping teams model and evaluate optimised cloud architectures before changes are committed to downstream delivery workflows. That makes it especially relevant for organisations that want migration projects to be guided by architecture intelligence not corrected after deployment problems appear. The 5 top cloud migration software tools for Infrastructure as Code (IaC) deployment 1. Infros Infros is the best overall cloud migration software tool for Infrastructure as Code deployment because it addresses a problem many teams discover too late: migration failures often begin at the architecture stage, not the provisioning stage. The platform is designed to help organisations design and validate optimised cloud architectures aligned to business and technical priorities before rollout decisions are finalized. That makes it especially useful for migration teams that need more than automation and want architectural confidence before deployment pipelines begin executing changes. What separates Infros from more execution-oriented platforms is its emphasis on decision quality. In cloud migration projects, teams frequently have to evaluate tradeoffs around workload placement, performance, cost and environment design. If those decisions are made too quickly or without enough structure, IaC deployment may remain technically consistent while still moving the wrong architecture into production. Infros is compelling because it helps teams prove architecture choices earlier, which can reduce downstream rework, rollback pressure, and costly redesign cycles. That overall positioning is consistent with the way it is described in current product materials and third-party coverage. Key features Cloud architecture design and validation workflows Optimisation aligned to cost and operational priorities Support for evaluating cloud architecture decisions before deployment Strong fit for migration planning in hybrid and multi-cloud scenarios Better alignment between architecture intent and downstream execution Useful for teams that want design-stage confidence not reactive correction 2. Spacelift Spacelift is one of the strongest choices for cloud migration programmes that depend on disciplined IaC orchestration. It is built to coordinate infrastructure workflows in tools like Terraform, OpenTofu, Ansible, and related automation frameworks, giving teams a structured way to manage planning, approvals and governance from a central platform. That makes it especially useful when migration efforts span multiple environments, multiple contributors, and multiple infrastructure codebases. In an IaC-based migration, the challenge is often not writing code but operating it safely at scale. Teams need clear workflows for stack execution, policy enforcement, pull request review, drift awareness, and role separation. Spacelift is well suited to those needs because it focuses on orchestration and governance not only infrastructure definition. That means it can help bring control to migration projects where many moving parts have to be coordinated in a repeatable way. It is particularly relevant for organisations that already have a defined IaC practice but need stronger operational controls as cloud migration grows more complex. Key features Orchestration for Terraform, OpenTofu, Ansible, and other IaC workflows Centralised governance and approval controls Support for policy-driven infrastructure operations Strong workflow fit for multi-environment migration programmes Designed for secure, repeatable infrastructure delivery Good option for teams scaling IaC beyond ad hoc execution 3. env0 env0 is a practical cloud migration software option for Infrastructure as Code deployment because it helps teams standardise the way environments are provisioned and managed using existing IaC frameworks. It supports common tools like Terraform, Terragrunt, and Pulumi, which makes it attractive to organisations that do not want to replace their current IaC approach but do want better structure around how migration-related changes move through development and production. One reason env0 belongs on this list is that migration programmes often break down when teams have inconsistent environment workflows. A plan might work in one account, one region, or one business unit, yet become difficult to reproduce elsewhere. env0 helps by creating more consistent workflow patterns for provisioning, updates and environment lifecycle management. That can be especially useful when cloud migration is happening incrementally and different application teams are moving at different speeds. The platform is often positioned as framework-agnostic, which is valuable for organisations with mixed stacks or evolving standards. Key features Supports Terraform, Terragrunt, and Pulumi-based workflows Structured environment lifecycle management Useful for repeatable deployment patterns in teams Framework-agnostic approach for mixed IaC stacks Helps standardise provisioning and update workflows Good fit for operational consistency during staged migrations 4. Firefly Firefly earns a place on this list because cloud migration rarely starts with a perfectly codified environment. Many organisations begin with fragmented cloud estates, unmanaged resources, partial documentation, and infrastructure that has drifted far from the intended model. Firefly focuses on cloud asset management and helps teams gain control over their entire cloud footprint, including turning unmanaged resources into codified infrastructure. That makes it especially relevant when migration work is blocked by poor visibility not lack of tooling. For IaC-driven migration, visibility matters just as much as deployment logic. If teams do not understand what already exists, what is unmanaged, and where drift has accumulated, they risk migrating bad assumptions into a more automated form. Firefly is valuable because it helps surface those blind spots. Instead of only managing future deployments, it helps teams reconcile the real-world cloud environment with the governed state they want to create. That can make migration initiatives more accurate, especially when legacy resources, shadow infrastructure, or inconsistent ownership patterns have built up over time. Current Firefly materials and partner descriptions emphasise this control and codification angle clearly. Key features Cloud asset management in existing infrastructure Support for turning unmanaged resources into codified assets Useful for discovering drift and hidden infrastructure gaps Strong visibility layer for messy or partially documented estates Helps connect cloud reality to governed IaC workflows Valuable in migration programmes with legacy sprawl 5. Pulumi Pulumi stands out as a cloud migration software option for Infrastructure as Code deployment because it gives teams a developer-centric way to define and manage infrastructure using general-purpose programming languages. For migration efforts led by software engineers not only infrastructure specialists, that can make automation easier to integrate with existing application development practices. It is particularly useful when teams want reusable logic, richer abstractions, and tighter alignment between infrastructure workflows and software delivery habits. In the context of migration, Pulumi can be effective because not every environment change fits neatly into static templates. Complex cloud transitions often involve conditional logic, reusable components, and environment-specific workflows that benefit from code expressiveness. Pulumi appeals to teams that want infrastructure automation to feel more like software engineering. That can speed up adoption in organisations where developers play a major role in platform modernisation and cloud rollout. The tradeoff is that this flexibility may require stronger internal engineering discipline, especially if teams are used to more opinionated workflow controls from orchestration platforms. Key features Infrastructure defined through general-purpose programming languages Strong fit for developer-led cloud automation Useful for reusable abstractions and complex deployment logic Supports modern software engineering practices in infrastructure delivery Helpful when migration workflows require custom logic Well suited to teams modernizing platform operations Where IaC-driven cloud migration projects usually break down Many cloud migration projects appear well planned at the beginning. There is usually a target environment, a preferred cloud model, and a roadmap that looks clear at a high level. Problems tend to emerge later, once teams begin translating architecture into deployable code and coordinating real implementation in departments. That is the point where Infrastructure as Code exposes every weak assumption that was hidden during early planning. One common breakdown happens when the target architecture is defined in terms but not in enough detail to support deployment. Teams may know where an application should move, but not how networking, access controls, data dependencies, or failover requirements should be handled in code. Another issue appears when infrastructure definitions are technically valid but not operationally realistic in multiple environments. A stack may work in a test environment but become much harder to manage once regional differences, team permissions, or compliance rules come into play. Migration projects also struggle when ownership is unclear. Architects may define the future state, platform engineers may manage IaC pipelines, operations teams may oversee reliability, and security teams may enforce governance requirements. If the migration software does not help bring those layers together, the result is often a deployment process that feels automated but remains brittle underneath. The most common failure points include: undocumented dependencies between workloads and data flows environment drift between dev and production late-stage security or compliance reviews that force redesign inconsistent infrastructure patterns in teams or business units unclear rollback planning if migration steps fail poor visibility into legacy cloud assets that still affect the target state manual exceptions that weaken otherwise standardised IaC workflows The important lesson is that Infrastructure as Code does not remove migration complexity. It organises it. If the underlying planning is weak, the code will simply reproduce that weakness more consistently. That is why effective cloud migration software has to support coordination and control, not deployment automation. What good cloud migration software looks like in an IaC environment The best cloud migration software for Infrastructure as Code deployment is not defined by one feature alone. It is defined by how well it helps teams move from planning to execution without losing structure, context, or control. In an IaC environment, software has to support repeatability, but it also has to support better decision-making before repeatability becomes a liability. A strong platform should help teams understand what they are migrating, how the target infrastructure should be modeled, and how those decisions will be governed as code moves through deployment pipelines. It should reduce the gap between architectural intent and operational reality. That is especially important in cloud migration because the move itself is usually only the first step. After cutover, teams still need to maintain and extend the infrastructure they have just deployed. What separates stronger solutions from weaker ones is their ability to support the full migration lifecycle. That does not mean every tool has to do everything. But it does mean the software should contribute meaningfully to planning quality, deployment consistency, environment control, or infrastructure visibility. The most valuable qualities usually include: Architecture awareness The software should help teams think through target-state design, workload placement and operating assumptions before they commit those choices to code. IaC framework compatibility Good tools should work with established Infrastructure as Code workflows not forcing teams to abandon Terraform, OpenTofu, Pulumi, or adjacent tooling. Governance and policy controls Migration carries risk, so platforms need approval paths, role separation, policy enforcement, and change tracking. Environment lifecycle management Teams should be able to create, update and retire environments in a controlled way instead of handling them through scattered scripts and exceptions. Drift detection and infrastructure visibility If teams cannot see what already exists, they cannot build a reliable migration strategy around it. Multi-cloud and hybrid support Many enterprises are not moving into a single clean environment. They are dealing with AWS, Azure, GCP, Kubernetes, on-prem components, or a hybrid combination. Operational scalability The platform should still work well when more teams, more deployments, and more governance requirements are added over time. Good cloud migration software in an IaC setting is not about making deployment faster. It is about creating a path where infrastructure becomes easier to reason about, easier to govern, and easier to evolve after migration is complete. The real benefits of using cloud migration software for IaC deployment It is easy to assume the main benefit of cloud migration software is speed. Speed does matter, but it is rarely the most important long-term advantage. The real value comes from making cloud migration more structured, more predictable, and more sustainable inside an Infrastructure as Code operating model. When teams try to migrate without a strong platform, they often rely on a mixture of architecture documents, scripts, ticketing workflows, ad hoc approvals, and deployment tools that were never designed to work together as one system. That usually leads to confusion around ownership, inconsistent environment behaviour, and too much manual intervention at exactly the moments when the process should be most controlled. Cloud migration software helps solve that by connecting different parts of the migration lifecycle. It brings more discipline to the way infrastructure changes are planned and applied. That is especially important in IaC environments, because once infrastructure is codified, errors can spread quickly if governance and visibility are weak. Some of the biggest benefits include: Less rework after deployment because critical decisions are surfaced earlier More consistent infrastructure behaviour in environments and teams Reduced manual configuration drift during phased migration efforts Better collaboration between architects, platform engineers and security teams Stronger auditability for infrastructure changes and approvals Improved rollback readiness when migrations need to be adjusted More scalable deployment practices as cloud adoption grows Cleaner post-migration operations because infrastructure is easier to maintain and optimise There is also a benefit that many teams underestimate. Migration software does not help with the move itself. It often helps define the quality of the cloud operating model that follows. If the migration is done through fragmented, poorly governed workflows, those weaknesses continue after cutover. If it is done through structured, architecture-aware, code-driven processes, the organisation is better positioned for long-term efficiency and change management. That is why the best cloud migration software is not simply a project tool. In many cases, it becomes part of the broader foundation for how cloud infrastructure is deployed and governed going forward. How to choose cloud migration software for Infrastructure as Code (IaC) deployment Choosing cloud migration software becomes much easier when teams stop asking which platform has the most features and start asking which platform fits the actual migration challenge in front of them. Different organisations need different things. Some need architecture intelligence before they codify anything. Others already know their target state and mainly need stronger orchestration, governance, or environment management. Others are still dealing with infrastructure sprawl and cannot move effectively until visibility improves. A good buying process begins with internal clarity. Teams should understand whether their biggest problem is planning, execution, governance, visibility, or post-migration manageability. If they skip that step, they often end up choosing tools based on market category labels instead of operational fit. When comparing options, it helps to evaluate them through a few practical questions: What stage of migration are we in right now? Early-stage planning calls for different abilities than mature rollout and governance. How much of our infrastructure is already codified? Some organisations need help standardising existing IaC workflows, while others still need to reconcile unmanaged assets. Do we need architecture support, execution support, or both? That distinction often determines whether a platform will create long-term value. How complex is our cloud footprint? A multi-cloud or hybrid environment usually demands better visibility and stronger coordination. Who will actually use the tool? Architects, platform engineers, developers, security teams, and operations teams may all have different needs. What governance requirements do we have? Policy controls, approval workflows and access management matter more in some environments than others. Will the tool still be useful after migration is finished? Long-term value is a better indicator of fit than short-term implementation convenience. The strongest choices are usually the ones that match the team’s operating model, not the immediate migration project. A platform may look impressive in a demo, but if it does not fit how infrastructure decisions are made and governed internally, it can add complexity instead of reducing it. That is why choosing cloud migration software for Infrastructure as Code deployment should be treated as an operational strategy decision, not only a tooling decision. What teams should compare before making a final decision Once the shortlist is down to a few serious options, the comparison process should go deeper than feature lists. Tools that seem similar at a high level can create value in very different ways. One platform may excel at architecture validation, another at IaC orchestration, and another at turning unmanaged cloud resources into governed infrastructure. Choosing well requires teams to compare tools against the real demands of their migration program. The most useful comparison areas are usually the ones that affect both present execution and future manageability. Teams should look at whether the platform improves planning quality, supports deployment discipline, and continues to be useful after the initial migration wave is complete. Key factors to compare include: Primary use case Is the tool strongest in planning, orchestration, visibility, codification, or developer-led automation? Infrastructure as Code compatibility Does it work well with existing IaC frameworks and workflows? Governance depth How strong are the approval models, access controls, audit trails, and policy checks? Migration readiness Can the software handle phased migrations, shared ownership, and nontrivial infrastructure transitions? Cloud and environment coverage Does it support the cloud providers and deployment models the organisation actually uses? Operational maturity fit Is the tool appropriate for the team’s current level of process maturity, or will it create friction? Post-migration value Will the platform remain useful for optimisation and future infrastructure changes? A practical comparison process should also include qualitative questions. For example: Will this tool help different teams work from the same assumptions? Does it reduce the number of manual decisions required during migration? Will it improve confidence before deployment, or only help after deployment starts? Can it support both the migration itself and the operational model that follows? The best final decisions usually come from this kind of grounded evaluation. Instead of asking which platform is the most advanced in general, teams ask which one is best aligned with their architecture, their workflows, and their cloud operating goals. Choosing the right cloud migration software for long-term IaC success Cloud migration software for Infrastructure as Code deployment should never be evaluated as if migration ends on cutover day. The better question is whether the platform helps create a cloud environment that remains manageable and adaptable after the move is complete. In mature organisations, that is what ultimately determines whether a migration was successful. The strongest solutions are the ones that improve both how teams move infrastructure and how they operate it afterward. That means helping with architecture quality, deployment consistency, policy enforcement, environment control, and infrastructure visibility in ways that remain useful beyond the initial project window. A strong long-term platform usually contributes to: better architecture decisions before provisioning more reliable deployment workflows less drift and fewer manual exceptions cleaner collaboration in technical teams more sustainable governance as cloud complexity grows better readiness for future optimisation and modernisation Infrastructure as Code raises the bar for migration quality because it turns cloud operations into a repeatable system not a one-time exercise. The right migration software supports that shift. It helps teams build an environment that can be deployed with confidence, managed with discipline, and improved continuously as business requirements evolve. That is why the final decision should not come down to who can provision infrastructure fastest. It should come down to which platform gives the organisation the strongest foundation for long-term cloud success. The post 5 top cloud migration software for Infrastructure as Code (IaC) appeared first on AI News. View the full article

OpenAI is introducing sandbox execution that allows enterprise governance teams to deploy automated workflows with controlled risk. Teams taking systems from prototype to production have faced difficult architectural compromises regarding where their operations occurred. Using model-agnostic frameworks offered initial flexibility but failed to fully utilise the capabilities of frontier models. Model-provider SDKs remained closer to the underlying model, but often lacked enough visibility into the control harness. To complicate matters further, managed agent APIs simplified the deployment process but severely constrained where the systems could run and how they accessed sensitive corporate data. To resolve this, OpenAI is introducing new capabilities to the Agents SDK, offering developers standardised infrastructure featuring a model-native harness and native sandbox execution. The updated infrastructure aligns execution with the natural operating pattern of the underlying models, improving reliability when tasks require coordination across diverse systems. Oscar Health provides an example of this efficiency regarding unstructured data. The healthcare provider tested the new infrastructure to automate a clinical records workflow that older approaches could not handle reliably. The engineering team required the automated system to extract correct metadata while correctly understanding the boundaries of patient encounters within complex medical files. By automating this process, the provider could parse patient histories faster, expediting care coordination and improving the overall member experience. Rachael Burns, Staff Engineer & AI Tech Lead at Oscar Health, said: “The updated Agents SDK made it production-viable for us to automate a critical clinical records workflow that previous approaches couldn’t handle reliably enough. “For us, the difference was not just extracting the right metadata, but correctly understanding the boundaries of each encounter in long, complex records. As a result, we can more quickly understand what’s happening for each patient in a given visit, helping members with their care needs and improving their experience with us.” OpenAI optimises AI workflows with a model-native harness To deploy these systems, engineers must manage vector database synchronisation, control hallucination risks, and optimise expensive compute cycles. Without standard frameworks, internal teams often resort to building brittle custom connectors to manage these workflows. The new model-native harness helps alleviate this friction by introducing configurable memory, sandbox-aware orchestration, and Codex-like filesystem tools. Developers can integrate standardised primitives such as tool use via MCP, custom instructions via AGENTS.md, and file edits using the apply patch tool. Progressive disclosure via skills and code execution using the shell tool also enables the system to perform complex tasks sequentially. This standardisation allows engineering teams to spend less time updating core infrastructure and focus on building domain-specific logic that directly benefits the business. Integrating an autonomous program into a legacy tech stack requires precise routing. When an autonomous process accesses unstructured data, it relies heavily on retrieval systems to pull relevant context. To manage the integration of diverse architectures and limit operational scope, the SDK introduces a Manifest abstraction. This abstraction standardises how developers describe the workspace, allowing them to mount local files and define output directories. Teams can connect these environments directly to major enterprise storage providers, including AWS S3, Azure Blob Storage, Google Cloud Storage, and Cloudflare R2. Establishing a predictable workspace gives the model exact parameters on where to locate inputs, write outputs, and maintain organisation during extended operational runs. This predictability prevents the system from querying unfiltered data lakes, restricting it to specific, validated context windows. Data governance teams can subsequently track the provenance of every automated decision with greater accuracy from local prototype phases through to production deployment. Enhancing security with native sandbox execution The SDK natively supports sandbox execution, offering an out-of-the-box layer so programs can run within controlled computer environments containing the necessary files and dependencies. Engineering teams no longer need to piece this execution layer together manually. They can deploy their own custom sandboxes or utilise built-in support for providers like Blaxel, Cloudflare, Daytona, E2B, Modal, Runloop, and Vercel. Risk mitigation remains the primary concern for any enterprise deploying autonomous code execution. Security teams must assume that any system reading external data or executing generated code will face prompt-injection attacks and exfiltration attempts. OpenAI approaches this security requirement by separating the control harness from the compute layer. This separation isolates credentials, keeping them entirely out of the environments where the model-generated code executes. By isolating the execution layer, an injected malicious command cannot access the central control plane or steal primary API keys, protecting the wider corporate network from lateral movement attacks. This separation also addresses compute cost issues regarding system failures. Long-running tasks often fail midway due to network timeouts, container crashes, or API limits. If a complex agent takes twenty steps to compile a financial report and fails at step nineteen, re-running the entire sequence burns expensive computing resources. If the environment crashes under the new architecture, losing the sandbox container does not mean losing the entire operational run. Because the system state remains externalised, the SDK utilises built-in snapshotting and rehydration. The infrastructure can restore the state within a fresh container and resume exactly from the last checkpoint if the original environment expires or fails. Preventing the need to restart expensive, long-running processes translates directly to reduced cloud compute spend. Scaling these operations requires dynamic resource allocation. The separated architecture allows runs to invoke single or multiple sandboxes based on current load, route specific subagents into isolated environments, and parallelise tasks across numerous containers for faster execution times. These new capabilities are generally available to all customers via the API, utilising standard pricing based on tokens and tool use without demanding custom procurement contracts. The new harness and sandbox capabilities are launching first for Python developers, with TypeScript support slated for a future release. OpenAI plans to bring additional capabilities, including code mode and subagents, to both the Python and TypeScript libraries. The vendor intends to expand the broader ecosystem over time by supporting additional sandbox providers and offering more methods for developers to plug the SDK directly into their existing internal systems. See also: Commvault launches a ‘Ctrl-Z’ for cloud AI workloads 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 OpenAI Agents SDK improves governance with sandbox execution appeared first on AI News. View the full article

Cadence Design Systems announced two AI-related collaborations at its CadenceLIVE event this week, expanding its work with Nvidia and introducing new integrations with Google Cloud. The Nvidia partnership focuses on combining AI with physics-based simulation and accelerated computing for robotic systems and system-level design. The companies said the approach targets modelling and deployment across semiconductors, robotics, and large-scale AI infrastructure, including robotic systems that Nvidia describes as physical AI. Cadence is integrating its multiphysics simulation and system design tools with Nvidia’s CUDA-X libraries, AI models, and Omniverse-based simulation environment. The tools model thermal, electrical, and mechanical interactions so engineers can assess how systems behave under real-world operating conditions. They also extend beyond chip design to cover infrastructure components such as networking, cooling, and power systems. The combined platform enables engineers to simulate system behaviour before physical deployment. The companies said system performance depends on how compute, networking, cooling, and power systems operate together. The collaboration also includes robotics development. Cadence’s physics engines, which model how real-world materials interact, are being linked with Nvidia’s AI models used to train AI-driven robotic systems in simulated environments. “We’re working with you across the board on robotic systems,” said Nvidia CEO Jensen Huang during the event. Training robots in simulation reduces the need for real-world data collection. The companies said these datasets must be generated with physics-based models rather than gathered from physical systems. Simulation-generated datasets are used to train models, with outcomes dependent on the accuracy of the underlying physics models. “The more accurate (generated training data) is, the better the model will be,” said Cadence CEO Anirudh Devgan. Nvidia said industrial robotics companies are using its Isaac simulation frameworks and Omniverse-based digital twin tools to test robotic systems before deployment. Companies including ABB Robotics, FANUC, YASKAWA, and KUKA are integrating these simulation tools into virtual commissioning workflows to test production systems in software prior to physical rollout. Nvidia said these systems are used to model complex robot operations and entire production lines using physically accurate digital environments. Chip design automation on cloud Separately, Cadence introduced a new AI agent designed to automate later-stage chip design tasks. The agent focuses on physical layout processes, translating circuit designs into silicon implementations. The release builds on an earlier agent introduced this year for front-end chip design, where circuits are defined in code-like descriptions. That earlier system handles circuit design, while the new agent focuses on translating those designs into physical layouts on silicon. The system will be available through Google Cloud. Cadence said the integration combines its electronic design automation tools with Google’s Gemini models for automated design and verification workflows. The cloud deployment allows teams to run those workloads without relying on on-premise compute infrastructure. Cadence’s ChipStack AI Super Agent platform uses model-based reasoning with native design tools to coordinate tasks across multiple design stages. The system can interpret design requirements and automatically execute tasks across different stages of the design process. Cadence reported productivity gains of up to 10 times in early deployments across design and verification tasks. The company did not disclose specific customer implementations. “We help build AI systems, and then those AI systems can help improve the design process,” Devgan said. The companies said simulation tools are used to validate systems in virtual environments before physical deployment. Digital twin models allow engineers to test design tradeoffs, evaluate performance scenarios, and optimise configurations in software. They added that the cost and complexity of large-scale data center infrastructure limit the use of trial-and-error deployment methods. Quantum models announcement In a separate announcement, Nvidia introduced a family of open-source quantum AI models called NVIDIA Ising. The models are named after the Ising model, a mathematical framework used to represent interactions in physical systems. The models are designed to support quantum processor calibration and quantum error correction. Nvidia said the models deliver up to 2.5 times faster performance and three times higher accuracy in decoding processes used for error correction. The models are intended for hybrid quantum-classical systems. “AI is essential to making quantum computing practical,” Huang said. “With Ising, AI becomes the control plane — the operating system of quantum machines — transforming fragile qubits to scalable and reliable quantum-GPU systems.” (Photo by Homa Appliances) See also: Hyundai expands into robotics and physical AI systems 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. This 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 Cadence expands AI and robotic partnerships with Nvidia, Google Cloud appeared first on AI News. View the full article

Enterprise cloud environments now have access to an undo feature for AI agents following the deployment of Commvault AI Protect. Autonomous software now roams across infrastructure, potentially deleting files, reading databases, spinning up server clusters, and even rewriting access policies. Commvault identified this governance issue and the data protection vendor has launched AI Protect, a system designed to discover, monitor, and forcefully roll back the actions of autonomous models operating inside AWS, Microsoft Azure, and Google Cloud. Traditional governance relies entirely on static rules. You grant a human user specific permissions and that user performs a predictable, linear task. If something goes wrong, there’s clear responsibility. AI agents, however, exhibit emergent behaviour. When given a complex prompt, an agent will string together approved permissions in potentially unapproved ways to solve the problem. If an agent decides the most efficient way to optimise cloud storage costs is to delete an entire production database, it will execute that command in milliseconds. A human engineer might pause before executing a destructive command, questioning the logic. An AI agent simply follows its internal reasoning loop. It loops thousands of API requests a second, vastly outpacing the reaction times of human security operations centres. A new breed of governance tools for cloud AI agents AI Protect is an example of emerging tools that continuously scan the enterprise cloud footprint to identify active agents. Shadow AI remains a massive difficulty for enterprise IT departments. Developers routinely spin up experimental agents using corporate credentials without notifying security teams and connect language models to internal data lakes to test new workflows. Commvault forces these hidden actors into the light. Once identified, the software monitors the agent’s specific API calls and data interactions across AWS, Azure, and GCP. It logs every database read, every storage modification, and every configuration change. The rollback feature provides the safety net. If a model hallucinates or misinterprets a command, administrators can revert the environment to its exact state before the machine initiated the destructive sequence. However, cloud infrastructure is highly stateful and deeply interconnected. Reversing a complex chain of automated actions requires precise, ledger-based tracking. You cannot just restore a single database table if the machine also modified networking rules, triggered downstream serverless functions, and altered identity access management policies during its run. Commvault bridges traditional backup architecture with continuous cloud monitoring to achieve this. By mapping the blast radius of the agent’s session, the software isolates the damage. It untangles the specific changes made by the AI from the legitimate changes made by human users during the same timeframe. This prevents a mass rollback from deleting valid customer transactions or wiping out hours of legitimate engineering work. Machines will continue to execute tasks faster than human operators can monitor them. The priority now is implementing safeguards that guarantee autonomous actions can be instantly and accurately reversed. See also: Citizen developers now have their own Wingman 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 Commvault launches a ‘Ctrl-Z’ for cloud AI workloads appeared first on AI News. View the full article

A vibe-coding application creation company, Emergent, has released Wingman, an autonomous agent that can address and take control of the applications used to manage daily tasks. The company’s press release states: “The best technology should be accessible to everyone”, and cites the difficulty that users without a technical background have in creating software applications. It says that eight million founders of businesses from 190 countries have used its products to create and ship software described as production-ready. Users of Wingman will be able to deploy a team of agents working on their behalf. “Now, anyone can have an always-on team working in the background, not just people who know how to build one,” said Mukund Jha, the co-founder and CEO of Emergent. Wingman differentiates itself from similar platforms by dividing which tasks can be accomplished without human intervention, and which need a human’s OK to proceed with. Therefore, tasks like modifying or deleting data, or sending messages to groups, are suspended until the AI gets the go-ahead from its operator. The company defines these divisions as “trust boundaries.” The platform can work by reading and controlling common applications such as WhatsApp, Telegram and iMessage, and can schedule tasks or have them triggered by preset events. A window of persistence (short-term context) means that users don’t have to repeat contextual instructions to the LLM for similar tasks. Connections to familiar platforms such as email, calendaring, CRMs, and GitHub come out of the box, with additional connections available from the company’s integration hub. In concord with the platform’s easy-to-use ethos, connections between Wingman and other applications are achieved without the need to code elements such as API calls and key exchanges. This type of functionality is handled under the hood, without the users needing to be aware of the technical details. Responses by Wingman can be adjusted in tone, so it feels like “a trusted operator rather than another tool to manage,” Emergent’s press release states. Wingman is powered by a choice of LLMs, including the latest models from ChatGPT and Anthropic, or users can opt for Emergent’s own AI instance to save costs. Sign-up is quick and simple, and users can choose the development of full-stack or mobile apps, or have the AI design web pages. Plans are available for $20 or $200 per month if paid monthly, with introductory discounts available for those wishing to experiment with having an LLM act on their behalf via the applications they currently use every day. Apps are built using modern, web-native technologies for a professional front end to the ensuing code. “Most people aren’t failing at productivity. They’re buried under the smaller tasks that never stop coming,” said Jha. The promise of Emergent’s Wingman and similar offerings is the empowerment of the true ‘citizen developer’, where all that is required on the part of the business founder is the ability to elucidate their needs for software in their native language. The large language model works to achieve its interpretation of those needs using a body of data garnered by scraping the internet for existing code. This is then reproduced, partially randomised, and subtly altered to something close to the user’s goals. Most commonly, further iterations using compute token credits improve the output until satisfactory results are produced. Although tools like OpenClaw and Wingman may be suitable at this stage for hobbyists with particular problems to solve, releasing software created in this manner for wider consumption makes some debatable assumptions about its inherent security and veracity – elements of the final creation that, although readable, will be impenetrable for the platforms’ intended market. Similarly opaque, Wingman’s ‘code review’ feature can be run on any application during the creation process, although the details of said review are best interpreted by technically well-versed users. While individual office workers and entrepreneurs should be able to code something that achieves basic tasks, even with the caveat of human confirmation at possibly risky junctures, it’s difficult to envisage Wingman’s creations being seriously considered alongside software written by experienced software professionals in terms of safety, reliability, repeatability, and maintainability. Wingman is available now. (Image source: “Wingman” by Mr Mo-Fo is licensed under CC BY-NC-ND 2.0. To view a copy of this license, visit [Hidden Content]) 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 Citizen developers now have their own Wingman appeared first on AI News. View the full article

Singapore-based DroneDash Technologies and GEODNET have formed a joint venture to be called GEODASH Aerosystems, to build an agricultural spraying drone for large industrial farms. The companies say the near-production drone technology is designed to remove the need to map a field to be treated before each flight, and the need to rebuild flight plans when conditions on the ground have changed. The aircraft will be capable of perceiving its surroundings during flight, adjust behaviour in response to visuals it captures, and undertake crop spraying. Current agricultural spraying drones were adapted from general-purpose models developed outside the industry, which meant that on farms, human operators had to survey and map each field, generate a flight plan for each spraying operation, and repeat the mapping process when canopy conditions altered. The technology is designed to be cost-effective on very large estates, especially palm oil plantations where crops are planted in rows, this necessary preparation and adjustment times can limit how much land a team can cover. GEODASH says its platform is built to remove the need for such preparation stages. The drone will combine DroneDash’s AI vision system with GEODNET’s positioning correction tech to achieve accuracy down to one centimetre. The drones can interpret rows, trees, terrain, and zones of operation while in the air. They are capable of adjusting their altitude and spray rates as conditions vary. The dividing line in smart robotics is whether machines can act in changing environments. Structured spaces – assembly lines, warehouses, etc. – present simpler operating parameters. However, in the case of agriculture, real-time decisions need to be made autonomously. Agricultural land, particularly plantation terrain with mixed-age crops and changing plant growth, means drones have to recognise all relevant physical features and alter flight paths or treatment patterns according to unpredictable conditions. In this sense, the perfect agricultural machine would need to combine the abilities of perception and location, and be able to attenuate its operations according to environmental conditions. Deterministic systems are less suited to these types of use case, as every edge-case of random occurrence can’t be hard-coded. GEODASH Aerosystems’ proposed solution isn’t a fully unsupervised machine that can make its own decisions anywhere on a farm property, but it will be capable of operating without pre-existing maps inside geo-fenced boundaries. It will also be able to log each decision in case of the need for adjustment by operators to get the best results. The nature of agriculture (and the natural world more generally) is that replanting, pruning, soil erosion or a host of other changes can make static maps increasingly less accurate over time. A platform that can be redeployed quickly after environmental changes could be more useful than one that’s only as accurate as its last survey data. The companies say each flight will feed data to DroneDash’s AI Smart Farming backend, providing metrics on canopy density analysis, stresses and anomalies, plant health scores, spray-effectiveness checks, and terrain profiles. Each drone will therefore have a dual-purposes: as a spray applicator, and what’s effectively an aerial sensor platform. Data gathered could be used on an ongoing basis by farm operators, perhaps to informing of the need to change dosages, change treatment timings, flag the need for fertilisation or pest control, and inform replanting schedules. GEODASH is aiming its technology initially at palm oil plantations in Southeast Asia, row-cropping operators in the US, and large estates in South America. The companies say they ran pilot deployments and validation projects throughout 2025 and into early 2026. Commercial deployment by GEODASH Aerosystems is planned for the third quarter of 2026. “Agriculture does not need ******* drones – it needs smarter ones,” said Paul Yam, CEO, DroneDash Technologies and GEODASH Aerosystems. (Image source: “Agriculture drone new technology” by Shreesha Sharma is licensed under CC BY-SA 4.0. To view a copy of this license, visit [Hidden Content]) 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 Drones get smarter for large farm holdings appeared first on AI News. View the full article

The assumption that the US holds a durable lead in AI model performance is not well-supported by the data, and that is just one of the uncomfortable findings in Stanford University’s 2026 AI Index Report, published this week. The report, produced by Stanford’s Institute for Human-Centred Artificial Intelligence, is a 423-page annual assessment of where artificial intelligence stands. It covers research output, model performance, investment flows, public sentiment, and responsible AI. The headline findings are striking. But the more consequential insights sit in the sections most coverage has skipped, particularly on AI safety, where the gap between what models can do and how rigorously they are evaluated for harm has not closed but widened. That said, three findings deserve more attention than they are getting. The US-China model performance gap has effectively closed The framing that the US leads China in AI development needs updating. According to the report, US and ******** models have traded the top performance position multiple times since early 2025. In February 2025, DeepSeek-R1 briefly matched the top US model. As of March 2026, Anthropic’s top model leads by just 2.7%. The US still produces more top-tier AI models – 50 models in 2025 to China’s 30 – and retains higher-impact patents. But China now leads in publication volume, citation share, and patent grants. China’s share of the top 100 most-cited AI papers grew from 33 in 2021 to 41 in 2024. South Korea, notably, leads the world in AI patents per capita. The practical implication is that the assumption of a durable US technological lead in AI model performance is not well-supported by the data. The gap that existed two years ago has closed to a margin that shifts with each major model release. There is a further structural vulnerability the report identifies. The US hosts 5,427 data centres – more than ten times any other country – but a single company, TSMC, fabricates almost every leading AI chip inside them. The entire global AI hardware supply chain runs through one foundry in Taiwan, though a TSMC expansion in the US began operations in 2025. AI safety benchmarking is not keeping pace, and the numbers show it Almost every frontier model developer reports results on ability benchmarks. The same is not true for responsible AI benchmarks, and the 2026 Index documents the gap with some precision. The report’s benchmark table for safety and responsible AI shows that most entries are simply empty. Only Claude Opus 4.5 reports results on more than two of the responsible AI benchmarks tracked. Only GPT-5.2 reports StrongREJECT. Across benchmarks measuring fairness, security and human agency, the majority of frontier models report nothing. Capability benchmarks are reported consistently across frontier models. Responsible AI benchmarks–covering safety, fairness, and factuality–are largely absent. Source: Stanford HAI 2026 AI Index Report This does not mean Frontier Labs is doing no internal safety work. The report acknowledges that red-teaming and alignment testing happen, but that “these efforts are rarely disclosed using a common, externally comparable set of benchmarks.” The effect is that external comparison in AI safety dimensions is effectively impossible for most models. Documented AI incidents rose to 362 in 2025, up from 233 in 2024, according to the AI Incident Database. The OECD’s AI Incidents and Hazards Monitor, which uses a broader automated pipeline, recorded a peak of 435 monthly incidents in January 2026, with a six-month moving average of 326. Documented AI incidents rose to 362 in 2025, up from 233 the previous year and under 100 annually before 2022. Source: AI Incident Database (AIID), via Stanford HAI 2026 AI Index Report The governance response at the organisational level is struggling to match. According to a survey conducted by the AI Index and McKinsey, the share of organisations rating their AI incident response as “excellent” dropped from 28% in 2024 to 18% in 2025. Those reporting “good” responses also fell, from 39% to 24%. Meanwhile, the share experiencing three to five incidents rose from 30% to 50%. The report also identifies a structural problem in responsible AI improvement itself: gains in one dimension tend to reduce performance in another. Improving safety can degrade accuracy, or improving privacy can reduce fairness, for example. There is no established framework for managing such trade-offs, and in several dimensions, including fairness and explainability, the standardised data needed to track progress over time does not yet exist. Public anxiety rises with adoption, and the expert-public gap Globally, 59% of people surveyed say AI’s benefits outweigh its drawbacks, up from 55% in 2024. At the same time, 52% say AI products and services make them nervous, an increase of two percentage points in one year. Both figures are moving upward simultaneously, which reflects a public that is using AI more while becoming more uncertain about where it leads. The expert-public divide on AI’s employment effects is particularly sharp. According to the report, 73% of AI experts expect AI to have a positive impact on how people do their jobs, compared with just 23% of the general public – a 50-point gap. On the economy, the gap is 48 points (69% of experts are positive versus 21% of the public). On medical care, experts are considerably more optimistic at 84%, against 44% of the public. Those gaps matter because public trust shapes regulatory outcomes, and regulatory outcomes shape how AI is deployed. On that dimension, the report flags something striking: the US reported the lowest level of trust in its own government to regulate AI responsibly of any country surveyed, at 31%. The global average was 54%. Southeast Asian countries were the most trusting, with Singapore at 81% and Indonesia at 76%. Globally, the EU is trusted more than the US or China to regulate AI effectively. Among 25 countries in Pew Research Centre’s 2025 survey, a median of 53% trusted the EU to regulate AI, compared to 37% for the US and 27% for China. The report closes its public opinion chapter by noting that Southeast Asian countries remain among the world’s most optimistic about AI. In China, Malaysia, Thailand, Indonesia, and Singapore, more than 80% of respondents say AI will profoundly change their lives in the next three to five years. Malaysia posted the largest increase in this view from 2024 to 2025. See also: IBM: How robust AI governance protects enterprise margins 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 The US-China AI gap closed. The responsible AI gap didn’t appeared first on AI News. View the full article

According to SAP, integrating agentic AI into core human capital management (HCM) modules helps target operational bloat and reduce costs. SAP’s SuccessFactors 1H 2026 release aims to anticipate administrative bottlenecks before they stall daily operations by embedding a network of AI agents across recruiting, payroll, workforce administration, and talent development. Behind the user interface, these agents must monitor system states, identify anomalies, and prompt human operators with context-aware solutions. Data synchronisation failures between distributed enterprise systems routinely require dedicated IT support teams to diagnose. When employee master data fails to replicate due to a missing attribute, downstream systems like access management and financial compensation halt. The agentic approach uses analytical models to cross-reference peer data, identify the missing variable based on organisational patterns, and prompt the administrator with the required correction. This automated troubleshooting dramatically reduces the mean time to resolution for internal support tickets. Implementing this level of autonomous monitoring requires severe engineering discipline. Integrating modern semantic search mechanisms with highly structured legacy relational databases requires extensive middleware configuration. Running large language models in the background to continuously scan millions of employee records for inconsistencies consumes massive compute resources. CIOs must carefully balance the cloud infrastructure costs of continuous algorithmic monitoring against the operational savings generated by reduced IT ticket volumes. To mitigate the risk of algorithmic hallucinations altering core financial data, engineering teams are forced to build strict guardrails. These retrieve-and-generate architectures must be firmly anchored to the company’s verified data lakes, ensuring the AI only acts upon validated corporate policies rather than generalised internet training data. The SAP release attempts to streamline this knowledge retrieval by introducing intelligent question-and-answer capabilities within its learning module. This functionality delivers instant, context-aware responses drawn directly from an organisation’s learning content, allowing employees to bypass manual documentation searches entirely. The integration also introduces a growing workforce knowledge network that pulls trusted external employment guidance into daily workflows to support confident decision-making. How SAP is using agentic AI to consolidate the HCM ecosystem The updated architecture focuses on unified experiences that adapt to operational needs. For example, the delay between a signed offer letter to new talent and the employee achieving full productivity is a drag on profit margins. Native integration combining SmartRecruiters solutions, SAP SuccessFactors Employee Central, and SAP SuccessFactors Onboarding streamlines the data flow from initial candidate interaction through to the new hire phase. A candidate’s technical assessments, background checks, and negotiated terms pass automatically into the core human resources repository. Enterprises accelerate the onboarding timeline by eliminating the manual re-entry of personnel data—allowing new technical hires to begin contributing to active commercial projects faster. Technical leadership teams understand that out-of-the-box software rarely matches internal enterprise processes perfectly. Customisation is necessary, but hardcoded extensions routinely break during cloud upgrade cycles, creating vast maintenance backlogs. To manage this tension, the software introduces a new extensibility wizard. This tool provides guided, step-by-step support for building custom extensions directly on the SAP Business Technology Platform within the SuccessFactors environment. By containing custom development within a governed platform environment, technology officers can adapt the interface to unique business requirements while preserving strict governance and ensuring future update compatibility. Algorithmic auditing and margin protection The 1H 2026 release incorporates pay transparency insights directly into the People Intelligence package within SAP Business Data Cloud to help with compliance with strict regulatory environments like the EU’s directives on pay transparency (which requires organisations to provide detailed and auditable justifications for wage discrepancies.) Manual compilation of compensation data across multiple geographic regions and currency zones is highly error-prone. Using the People Intelligence package, organisations can analyse compensation patterns and potential pay gaps across demographics. Automating this analysis provides a data-driven defence against compliance audits and aligns internal pay practices with evolving regulatory expectations, protecting the enterprise from both litigation costs and brand damage. Preparing for future demands requires trusted and consistent skills data that leadership can rely on across talent deployment and workforce planning. Unstructured data, where one department labels a capability using differing terminology from another, breaks automated resource allocation models. The update strengthens the SAP talent intelligence hub by introducing enhanced skills governance to provide administrators with a centralised interface for managing skill definitions, applying corporate standards, and ensuring data aligns across internal applications and external partner ecosystems. Standardising this data improves overall system quality and allows resource managers to make deployment decisions without relying on fragmented spreadsheets or guesswork. This inventory prevents organisations from having to outsource to expensive external contractors for capabilities they already possess internally. By bringing together data, AI, and connected experiences, SAP’s latest enhancements show how agentic AI can help organisations reduce daily friction. For professionals looking to explore these types of enterprise AI integrations and connect directly with the company, SAP is a key sponsor of this year’s AI & Big Data Expo North America. See also: IBM: How robust AI governance protects enterprise margins 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 SAP brings agentic AI to human capital management appeared first on AI News. View the full article

Scotiabank has launched an AI framework, Scotia Intelligence, for data and AI operations that joins various platforms, data oversight, and software tools into a single instance. According to a press release from the bank, the stated purpose of Scotia Intelligence is to give employees, especially client-facing teams, access to AI under the bank’s existing governance and security rules. Scotiabank has published a short data ethics commitment paper, the existence of which is unique in Canada, the bank says. Tim Clark, Scotiabank’s group head and chief information officer, said Scotia Intelligence is a new approach that combines the bank’s existing infrastructure with AI abilities that connect computing environments, governance, and security so employees can use the technology more confidently. The difficult problem in the financial sector is how to make AI tools available at enterprise scale without creating new operational and regulatory risks for the organisation. Scotiabank’s response comes in the form of Scotia Navigator, the employee-focused component of Scotia Intelligence. It provides assistive AI for staff in multiple business units to in support of decision-making and software development, and is the means by which staff can build and deploy their own AI assistants within the company’s governance rules and stipulations. There’s particular weight on AI software development, with automated coding in play in the bank’s technical teams. Code generation in a regulated environment has to conform to set standards for product quality, so code checking for security and auditability is a business imperative. The bank has presented performance figures it says support the case for greater rollout of AI, citing contact centres where AI now handles more than 40% per cent of client queries, a fact that has led to industry recognition for its efforts in digital transformation. It says AI automatically forwards around 90% of commercial emails addressed to the bank, cutting the manual work of achieving this task by 70%. In digital banking, Scotiabank points to Scotia Intelligence at work giving predictive payment prompts to customers via a mobile app, helping customers manage recurring bills, email money transfers, and transferring money between a customer’s Scotiabank accounts. Phil Thomas, the bank’s Group Head and Chief Strategy & Operating Officer, described the launch as a step in the company’s AI strategy focused on client-centred experiences, and said AI tools would allow the bank’s workforce to spend more time on higher-value work. All AI uses are reviewed internally on grounds of fairness, transparency, and accountability before they are launched. Employees working with Scotia Intelligence get mandatory training and annual attestations. For CIOs, CTOs, and enterprise architecture leaders, Scotiabank’s combination of platform standardisation and formal governance creates the message that controls on AI have to exist as AI moves into production, and that exhibiting the existence of controls is important before incidents make their absence obvious. The scale of AI deployment success will depend at least partly on elements of safety and observability. The examples given by the bank’s statements suggest a programme of AI rollout where every function’s effectiveness can be measured in terms of reduced handling time, high-level automation, and customer engagement. In its public statement, Scotiabank hasn’t given detail regarding architecture, cost, model strategy, or provided evidence of external benchmarks, so total ROI is unclear. However, should its existing AI projects continue to produce cost reductions, more code, and better customer experiences, it seems likely that Scotiabank will apply the technology elsewhere in its business. Scotiabank envisages future use of agents for research and analytics, and says there’s scope for “more autonomous, context-aware, and action-oriented capabilities over time.” (Image source: Pixabay under licence.) 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 Canada’s Scotiabank preps for its AI future appeared first on AI News. View the full article

Hyundai Motor Group is starting to look like a company building machines that act in the real world. The change centres on physical AI: Where AI is placed into robots and systems that move and respond in physical spaces. Current efforts are mainly focused on factory and industrial settings. Hyundai’s move into physical AI systems In an interview with Semafor, chairman Chung Eui-sun said robotics and AI will play a central role in Hyundai’s next phase of growth, pushing the company beyond vehicles and into physical systems. The group plans to invest $26 billion in the US by 2028, according to United Press International, building on roughly $20.5 billion invested over the past 40 years. A large part of that spending is tied to robotics and AI-driven systems that Hyundai is combining into a single approach. Chung described robotics and physical AI as important to Hyundai’s long-term direction, adding that the company is developing robots to work with people not replace them. From automation to collaboration Hyundai is working on systems where robots and humans share tasks in the same space. This includes humanoid robots developed by Boston Dynamics, which Hyundai acquired a controlling stake in 2021. Machines are being prepared for manufacturing use, with deployment planned around 2028. The company expects to scale production to up to 30,000 units per year by 2030, with the goal to improve work on the factory floor. Robots may handle repetitive or physically demanding tasks, while humans focus on oversight and coordination. Chung said this kind of setup could help improve efficiency and product quality as customer expectations change. Exploring uses beyond the factory Current deployments remain focused on industrial settings, though Hyundai is exploring other uses. Potential areas include logistics and mobility services that combine vehicles with AI systems. These may affect deliveries and shared services. Manufacturing as the first use case for physical AI While these uses are still developing, manufacturing remains the main testing ground. Factories remain the place where Hyundai is putting these ideas into practice. The company is already working on software-driven manufacturing systems in its US operations, combining data and robotics to manage production. Physical AI builds on this by adding machines that adjust their actions based on real-time data. Chung said changes in regulations and customer demand are pushing the company to rethink how it operates in regions. Hyundai’s response is a mix of global expansion and local production, with AI and robotics helping standardise processes. Energy and infrastructure The company continues to invest in hydrogen through its HTWO brand, which covers production, storage and use. Chung pointed to rising demand linked to AI infrastructure and data centres as one reason hydrogen is gaining attention. He described hydrogen and electric vehicles as complementary options. The idea is to offer different energy choices depending on how systems are used. As AI moves into physical environments, energy becomes a more visible constraint. What physical AI means for end users Most people will not interact with a humanoid robot in the near term. But they will feel the effects of these systems in other ways. Products may be built faster and services tied to mobility or infrastructure may become more responsive. Hyundai sells more than 7 million vehicles each year in over 200 countries, supported by 16 global production facilities, according to the same UPI report. A gradual transition Hyundai is still a major carmaker, with brands like Hyundai, Kia, and Genesis forming the base of its operations. What is changing is how those vehicles – and the systems around them – are designed and managed. Physical AI represents a change from products to systems. It places AI in the environments where work and daily life take place. That change is still in progress, and many of the systems Hyundai is developing will take years to scale. The company is building toward a future where machines work with people in the real world. (Photo by @named_ aashutosh) See also: Asylon and Thrive Logic bring physical AI to enterprise perimeter security 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. This 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 Hyundai expands into robotics and physical AI systems appeared first on AI News. View the full article

Models like Google Gemma 4 are increasing enterprise AI governance challenges for CISOs as they scramble to secure edge workloads. Security chiefs have built massive digital walls around the cloud; deploying advanced cloud access security brokers and routing every piece of traffic heading to external large language models through monitored corporate gateways. The logic was sound to boards and executive committees—keep the sensitive data inside the network, police the outgoing requests, and intellectual property remains entirely safe from external leaks. Google just obliterated that perimeter with the release of Gemma 4. Unlike massive parameter models confined to hyperscale data centres, this family of open weights targets local hardware. It runs directly on edge devices, executes multi-step planning, and can operate autonomous workflows right on a local device. On-device inference has become a glaring blind spot for enterprise security operations. Security analysts cannot inspect network traffic if the traffic never hits the network in the first place. Engineers can ingest highly classified corporate data, process it through a local Gemma 4 agent, and generate output without triggering a single cloud firewall alarm. Collapse of API-centric defences Most corporate IT frameworks treat machine learning tools like standard third-party software vendors. You vet the provider, sign a massive enterprise data processing agreement, and funnel employee traffic through a sanctioned digital gateway. This standard playbook falls apart the moment an engineer downloads an Apache 2.0 licensed model like Gemma 4 and turns their laptop into an autonomous compute node. Google paired this new model rollout with the Google AI Edge Gallery and a highly optimised LiteRT-LM library. These tools drastically accelerate local execution speeds while providing highly structured outputs required for complex agentic behaviours. An autonomous agent can now sit quietly on a local machine, iterate through thousands of logic steps, and execute code locally at impressive speed. European data sovereignty laws and strict global financial regulations mandate complete auditability for automated decision-making. When a local agent hallucinates, makes a catastrophic error, or inadvertently leaks internal code across a shared corporate Slack channel, investigators require detailed logs. If the model operates entirely offline on local silicon, those logs simply do not exist inside the centralised IT security dashboard. Financial institutions stand to lose the most from this architectural adjustment. Banks have spent millions implementing strict API logging to satisfy regulators investigating generative machine learning usage. If algorithmic trading strategies or proprietary risk assessment protocols are parsed by an unmonitored local agent, the bank violates multiple compliance frameworks simultaneously. Healthcare networks face a similar reality. Patient data processed through an offline medical assistant running Gemma 4 might feel secure because it never leaves the physical laptop. The reality is that unlogged processing of health data violates the core tenets of modern medical auditing. Security leaders must prove how data was handled, what system processed it, and who authorised the execution. The intent-control dilemma Industry researchers often refer to this current phase of technological adoption as the governance trap. Management teams panic when they lose visibility. They attempt to rein in developer behaviour by throwing more bureaucratic processes at the problem, mandate sluggish architecture review boards, and force engineers to fill out extensive deployment forms before installing any new repository. Bureaucracy rarely stops a motivated developer facing an aggressive product deadline; it just forces the entire behaviour further underground. This creates a shadow IT environment powered by autonomous software. Real governance for local systems requires a different architectural approach. Instead of trying to block the model itself, security leaders must focus intensely on intent and system access. An agent running locally via Gemma 4 still requires specific system permissions to read local files, access corporate databases, or execute shell commands on the host machine. Access management becomes the new digital firewall. Rather than policing the language model, identity platforms must tightly restrict what the host machine can physically touch. If a local Gemma 4 agent attempts to query a restricted internal database, the access control layer must flag the anomaly immediately. Enterprise governance in the edge AI era We are watching the definition of enterprise infrastructure expand in real-time. A corporate laptop is no longer just a dumb terminal used to access cloud services over a VPN; it’s an active compute node capable of running sophisticated autonomous planning software. The cost of this new autonomy is deep operational complexity. CTOs and CISOs face a requirement to deploy endpoint detection tools specifically tuned for local machine learning inference. They desperately need systems that can differentiate between a human developer compiling standard code, and an autonomous agent rapidly iterating through local file structures to solve a complex prompt. The cybersecurity market will inevitably catch up to this new reality. Endpoint detection and response vendors are already prototyping quiet agents that monitor local GPU utilisation and flag unauthorised inference workloads. However, those tools remain in their infancy today. Most corporate security policies written in 2023 assumed all generative tools lived comfortably in the cloud. Revising them requires an uncomfortable admission from the executive board that the IT department no longer dictates exactly where compute happens. Google designed Gemma 4 to put state-of-the-art agentic skills directly into the hands of anyone with a modern processor. The open-source community will adopt it with aggressive speed. Enterprises now face a very short window to figure out how to police code they do not host, running on hardware they cannot constantly monitor. It leaves every security chief staring at their network dashboard with one question: What exactly is running on endpoints right now? See also: Companies expand AI adoption while keeping control 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 Strengthening enterprise governance for rising edge AI workloads appeared first on AI News. View the full article

Many companies are taking a slower, more controlled approach to autonomous systems as AI adoption grows. Rather than deploying systems that act on their own, they are focusing on tools that assist human decision-making while keeping tight control over outputs. This approach is especially clear in sectors where errors carry real financial or legal risk. The question is not just what AI can do, but how its behaviour can be managed, checked, and trusted. One example comes from S&P Global Market Intelligence, which builds AI tools into its Capital IQ Pro platform. The system is used by analysts to review company filings, earnings calls, and market data. Its AI features are designed to stay grounded in source material. According to S&P Global Market Intelligence, its AI tools extract insights from structured and unstructured data, including transcripts and reports, while working with verified source data. AI adoption moves ahead of autonomy The current wave of AI tools in business is often described as a step toward autonomous agents. These systems may eventually plan tasks, make decisions, and act without direct human input. But most companies are not there yet. AI adoption is already widespread, with a majority of organisations using AI in at least one part of their business, according to research from McKinsey & Company. At the same time, many organisations have yet to scale AI across the enterprise, showing a disconnect between initial use and broader deployment. Instead, AI helps with tasks such as summarising documents or answering queries, but it does not act independently. S&P Global Market Intelligence’s tools enable users to query large datasets through a chat interface, but the results are tied to verified financial content. In many cases, users can refer back to underlying documents, lowering the risk of errors or unsupported outputs. In its research, the company outlines AI governance as a process in which systems are designed, deployed, and monitored, with attention to fairness, transparency, and accountability. AI adoption in high-risk sectors In finance, small errors can have large consequences. That shapes how AI is built and used. Tools like Capital IQ Pro are designed to support analysts rather than replace them. The system may help surface insights or highlight trends, but final decisions still rest with human users. The gap between adoption and value is becoming clearer. Many organisations report a gap between AI deployment and measurable business outcomes, according to findings from McKinsey & Company. While autonomous systems may be able to handle certain tasks, companies often need clear accountability. When decisions affect investments, compliance, or reporting, there must be a way to explain how those decisions were made. Research from S&P Global notes that organisations are increasingly focused on building governance frameworks to manage AI risks, including data quality issues and model bias. A step toward future systems The gap between today’s controlled AI tools and future autonomous systems remains wide. Interest in more autonomous and agent-driven systems is also growing, even as most organisations remain in early stages of deployment. Systems that can explain their outputs, show their sources, and operate within defined limits are more likely to be trusted. Autonomous agents may one day handle tasks such as financial analysis, customer support, or supply chain planning with minimal input. But without clear control mechanisms, their use will remain limited. These themes will feature at AI & Big Data Expo North America 2026 on May 18–19. S&P Global Market Intelligence is listed as a bronze sponsor of the event. The agenda features topics such as AI governance, ethics, and the use of AI in regulated industries. Balancing capability and control The push toward autonomous AI is unlikely to slow down. Advances in large language models and agent-based systems continue to expand what AI can do. At the same time, enterprise users are asking a different question: how to keep those systems under control. S&P Global Market Intelligence’s approach reflects that concern. By keeping AI grounded in verified data and placing humans at the centre of decision-making, it prioritises trust over autonomy. As systems grow more capable, the ability to govern and control them could become just as important as the tasks they perform. (Photo by Hitesh Choudhary) 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 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 Companies expand AI adoption while keeping control appeared first on AI News. View the full article