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.

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The companies said the approach targets modelling and deployment in semiconductors and large-scale AI infrastructure, including robotic systems that Nvidia describes as physical AI.

Cadence is integrating its multi-physics simulation and system design tools with Nvidia’s CUDA-X libraries, AI models, and Omniverse-based simulation environment. The tools model thermal 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 like networking and power systems. The combined platform lets engineers simulate system behaviour before physical deployment. The companies said system performance depends on how compute, networking 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 in 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 not 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 in multiple design stages. The system can interpret design requirements and automatically execute tasks in different stages of the design process.

Cadence reported productivity gains of up to 10 times in early deployments in 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 trade-offs, evaluate performance scenarios, and optimise configurations in software.

They added that the cost and complexity of large-scale data centre 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.

“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. The comprehensive event is part of TechEx and co-located with other leading technology events. Click here for more information.

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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.

Pranay Ahlawat, Chief Technology and AI Officer at Commvault, said: “In agentic environments, agents mutate state across data, systems, and configurations in ways that compound fast and are hard to trace. When something goes wrong, teams need to recover not just data, but the full stack – applications, agent configurations, and dependencies – back to a known good state.”

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.

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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 https://creativecommons.org/licenses/by-nc-nd/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.

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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 bigger 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 https://creativecommons.org/licenses/by-sa/4.0)

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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 Chinese 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.

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.

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 closes amid responsible AI concerns appeared first on AI News.

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.

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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.)

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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.

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.

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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.

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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 ahead of autonomy

The current wave of AI tools in business is often described as a step toward autonomous agents. Systems may eventually plan tasks 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. Many organisations have yet to scale AI in the enterprise, showing a disconnect between initial use and broader deployment.

Instead, AI helps with tasks like summarising documents or answering queries, but it does not act independently.

S&P Global Market Intelligence’s tools let 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 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 and monitored, with attention to fairness and accountability.

AI 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 not 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.

Toward future systems

The difference 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 in defined limits are more likely to be trusted.

Autonomous agents may one day handle tasks like financial analysis or supply chain planning with minimal input. But without clear control mechanisms, their use will remain limited.

The 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 like AI governance and the use of AI in regulated industries.

Balancing ability 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.

Enterprise users are asking the question of 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. This 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.

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

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

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

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

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

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

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

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

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

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

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

Why open-source AI is essential for operational resilience

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

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

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

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

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

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

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

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

The future of enterprise AI demands transparent governance

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

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

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

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

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

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

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

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

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