Category Archives: Technology

From Soil to Smart Farming: How Technology Is Reshaping India’s Agricultural Economy

Artificial intelligence, agricultural drones, digital marketplaces and smart irrigation are opening new possibilities for Indian agriculture, helping farmers improve productivity, manage risks and discover fresh sources of income.

India’s agricultural landscape is undergoing a quiet but significant transformation. Across the country’s villages and farming communities, technology is creating new ways to grow crops, manage resources and connect agricultural produce with markets. While traditional knowledge and farmers’ experience remain indispensable, digital tools are increasingly helping them make more informed decisions in a changing economic and climatic environment.

The shift is about more than modernising cultivation. It is about strengthening the entire agricultural value chain—from choosing the right crop and managing water to reducing post-harvest losses, accessing buyers and creating new rural businesses.

Agriculture and allied activities contribute around 18 per cent of India’s Gross Value Added (GVA), making the sector an important pillar of the economy. Foodgrain production reached an estimated 357.73 million tonnes in the 2024–25 crop year, highlighting India’s substantial production capacity and the importance of farming to national food security.

Yet higher production does not automatically translate into higher farm incomes. Unpredictable weather, rising input costs, limited storage and fluctuating market prices continue to challenge farmers. Technology offers practical ways to address some of these pressures while opening new opportunities for rural entrepreneurship and economic growth.

Artificial Intelligence Brings Timely Advice to Farmers

For a farmer, a timely decision can make a considerable difference to the season’s outcome. Knowing when to sow, how to respond to a pest outbreak or when to irrigate can influence both crop quality and cultivation costs.

Artificial intelligence and mobile-based advisory services are making agricultural information more accessible. Farmers can increasingly use digital platforms to obtain weather updates, crop management guidance, pest alerts and information about government support programmes.

Government initiatives such as the Kisan e-Mitra chatbot help farmers access information about agricultural schemes in regional languages. Bharat-VISTAAR, an AI-based agricultural advisory initiative, represents a further step towards bringing agricultural information and digital guidance together in a more accessible format.

The potential is considerable. When reliable agricultural data is combined with local weather information, crop requirements and expert knowledge, digital tools can help farmers make more informed decisions.

However, the success of AI in agriculture will depend on the quality of its recommendations. Advice must reflect local growing conditions and remain understandable to farmers with different levels of digital literacy. Technology works best when it complements farmers’ experience and the guidance of agricultural experts.

Agricultural Drones Turn Innovation into Rural Opportunity

Agricultural drones are introducing greater speed and precision to selected farm operations. They can help monitor crops, identify areas requiring attention and apply agricultural inputs where appropriate.

For farmers, these services can reduce the time and labour required for certain tasks. Their effectiveness, however, depends on crop conditions, equipment quality, operator training and safe application practices.

The financial model is equally important. Many small and marginal farmers may find it difficult to purchase and maintain drones individually. Shared services and affordable rental models can make the technology more accessible without placing a heavy financial burden on individual cultivators.

This creates opportunities for rural entrepreneurs, trained drone operators and women’s self-help groups to provide agricultural services and earn additional income. In this way, a technology designed to support cultivation can also become a source of employment and enterprise in rural communities.

Digital Markets Help Farmers Look Beyond the Local Mandi

Growing a good crop is only half the challenge; selling it at a fair price is equally important.

Farmers have traditionally depended on local mandis and established trading networks. These markets remain essential, but limited price information, transport costs and restricted buyer access can sometimes reduce farmers’ bargaining power.

Digital platforms such as the electronic National Agriculture Market (e-NAM) aim to broaden market access by connecting agricultural markets through online trading and information services.

By March 2026, e-NAM had integrated 1,656 mandis across 23 states and four Union Territories, with more than 1.80 crore farmers registered, according to reported government figures.

For farmers, digital market access can make it easier to compare prices and explore trading opportunities. For agricultural businesses, it can improve connections with producers and support more organised procurement.

Nevertheless, digital access must be backed by physical infrastructure. Transparent grading, accurate weighing, efficient transportation, suitable storage and timely payments remain critical to ensuring that farmers can turn market information into better commercial outcomes.

Smart Irrigation Makes Every Drop Count

Water availability is becoming an increasingly important consideration for agricultural planning. In areas affected by irregular rainfall or water stress, efficient irrigation can help farmers manage one of their most valuable resources.

Drip irrigation delivers water close to plant roots, while soil-moisture sensors help assess when irrigation may be needed. Automated systems can support more precise water management, reducing unnecessary use when properly designed and maintained.

When combined with weather forecasts and crop-specific guidance, these technologies can help farmers plan irrigation more effectively. The benefits vary according to soil conditions, crop type, local climate and the cost of installation.

Making efficient irrigation affordable for smaller farms will be important to expanding adoption. Access to financing, technical support and suitable maintenance services can help farmers use these systems more effectively over time.

Agriculture Is Creating a New Generation of Rural Businesses

Technology’s influence extends well beyond the field. India’s agricultural economy offers opportunities in machinery rentals, drone services, soil testing, food processing, cold storage, packaging and logistics.

Young entrepreneurs can build businesses that solve practical problems for farmers, from arranging equipment during peak cultivation periods to helping transport produce to processing units and buyers.

Food processing is particularly important because it can increase the value of agricultural produce before it reaches consumers. Better storage and cold-chain facilities can also help preserve perishable crops and reduce losses after harvest.

Farmer-producer organisations and cooperatives can strengthen these opportunities by helping cultivators purchase inputs collectively, share equipment, negotiate with buyers and access services that might otherwise be too expensive for individual farmers.

This broader approach creates the possibility of a more diversified rural economy—one in which livelihoods depend not only on growing crops but also on the businesses and services that support agriculture.

Closing the Gap Between Technology and the Small Farmer

Despite its promise, agricultural technology is not equally accessible everywhere. Small landholdings, limited internet connectivity, affordability concerns and gaps in digital literacy can slow adoption.

Farmers also need confidence that the tools they use are reliable and relevant to their circumstances. AI recommendations require dependable data, drones need trained operators, and digital marketplaces work best when supported by efficient transport and storage.

Addressing these challenges will require collaboration among government institutions, agricultural universities, technology companies, financial institutions and farmer organisations. Regional-language services, practical demonstrations, affordable equipment and accessible financing can help ensure that technological progress reaches small and marginal farmers as well as larger agricultural enterprises.

The objective should not be to replace traditional knowledge with machines, but to combine generations of farming experience with tools that improve decision-making and reduce avoidable risks.

The Future of Farming Lies in Better Returns for Farmers

India’s agricultural transformation will ultimately be judged by its impact on farmers’ livelihoods. Higher production matters, but so do lower costs, reduced crop losses, stronger bargaining power and access to dependable markets.

Artificial intelligence can improve access to information, drones can support efficient farm operations, digital platforms can connect producers with buyers, and smart irrigation can help conserve water. Meanwhile, rural enterprises in processing, storage and logistics can create additional employment and help retain more economic value within farming communities.

The next stage of progress will depend on making these solutions affordable, practical and widely accessible. Technology must serve the farmer’s needs, rather than becoming another expense that is difficult to recover.

The real promise of smart farming is not simply producing more food—it is building an agricultural economy in which farmers make better decisions, rural businesses thrive and a greater share of the value created by every harvest reaches the people who grow it.

Partnership to Scale AI and Multi-Cloud Workloads Across Singapore, Australia and New Zealand

A partnership that enables centralized, observable, and scalable environments for AI and multi-cloud workloads across Singapore, Australia, and New Zealand 

• Orange Business APAC and Aviatrix join forces in the Asia-Pac region to expand the partnership beyond Europe by combining strengths to help enterprises connect, secure, and manage their cloud infrastructure.

• This partnership leverages the connectivity, managed services, and cybersecurity expertise of Orange Business, and the cloud-native networking and security capabilities of Aviatrix. 

Organizations within Asia Pacific namely Singapore, Australia, and New Zealand will soon benefit from the worldwide capabilities offered by Orange Business and Aviatrix following their collaboration in this region. This latest partnership sees the two companies delivering a joint value proposition that combines Orange Business’s global connectivity and managed services with Aviatrix’s cloud-native networking and security expertise, beginning with the three key markets above.  

The expansion further builds on the global partnership that Orange Business and Aviatrix announced earlier this year. Additionally, Aviatrix will also grant Orange Business first right of refusal for opportunities in China, Hong Kong, India, Malaysia, South Korea, and Taiwan. 

Orange Business is a global operator, digital solutions integrator and platform player and the enterprise division of the Orange Group, with more than 20 years of presence across Asia-Pacific. Aviatrix, founded in 2014, delivers the only secure networking architecture built specifically for the cloud, providing centralized security and visibility across every cloud environment from a single platform. 

In an increasingly AI-based landscape where organizations are drawn to innovate faster, scale more efficiently, and deliver new services to customers, AI workloads also expand the attack surface and increase the complexity of cloud networking. AI environments span multiple clouds, regions, containers, Kubernetes clusters, and AI agents. According to a study on 2026 global threat landscape (source: CrowdStrike 2026 Global Threat Report: Evasive Adversary Wields AI), a reported 82% of detections are now malware-free—adversaries use valid credentials and trusted identity flows to move through authorized pathways, bypassing traditional detection entirely. This is why organizations need secure, high-performance connectivity with consistent visibility, centralized governance, and policy enforcement that holds on whether a compromise has been detected.  

Orange Business and Aviatrix address this challenge by providing a secure, scalable foundation for multi-cloud and AI workloads:

• Securing distributed AI environments: Aviatrix secures the network traffic connecting AI data sources, models, and applications across multi-cloud environments. Built-in containment ensures that security policies hold as to whether a breach has been detected, with high-performance encryption at up to 100 Gbps cloud-to-cloud and up to 1 Tbps data-center-to-cloud.

• Simplifying multi-cloud networking: A centralized, software-defined platform for managing, monitoring, and securing cloud connectivity, thereby replacing fragmented, cloud-by-cloud approaches.

• Visibility and enforcement for AI workloads: See how AI applications communicate across clouds, identify risks, and enforce policy on AI-to-AI and AI-to-data connections—without adding architectural complexity.  

Additionally, the partnership seeks to benefit organizations in different areas. Firstly, organizations can expect to reduce the total cost of ownership by optimizing network usage and avoiding unnecessary costs. Real-time visibility into cloud resources and consumption supports more accurate financial planning through efficient traffic management and Aviatrix’s cloud-native capabilities that support real-time visibility into cloud resources and consumption.  

Secondly, the joint proposition offers secure connectivity within the cloud and to the cloud. This is achieved through the Orange Business Evolution Platform – a proven, high-performance network infrastructure that can integrate AI workloads with existing offices, data centers, SD-WAN, and security environments. Also, key to note: the Orange Evolution Platform infrastructure hosts Aviatrix in over 40 locations globally that allows Aviatrix to be deployed without additional hardware complexity, and with the flexible advantage of monthly billing option that supports different commercial and operating models, thereby easing an organization’s process in connecting and managing their cloud environments.  

Finally, the partnership also extends traffic visibility and policy enforcement across connected SD-WAN, LAN, and cloud networking environments as a standard managed service – helping organizations see what their workloads are doing, enforce consistent policy, and improve resilience across multi-cloud connectivity. Orange Business and Aviatrix support the full cloud networking lifecycle – from design and deployment through operation and optimization. Organizations can thus choose from self-service, co-managed, or fully managed operating models based on their business needs.

Haiqu Unveils AgenticOS, a System of Agents Solving Some of Physics’ Toughest Problems

R&D Teams Can Build Complete Quantum Projects from the Idea to the Experiment Itself, Verifying AI Results Every Step of the Way; Available Now

NEW YORK — October 9, 2026 — Existing AI models have advanced to the point that they can contribute to real science, but they have their limits. An AI agent can produce convincing math and working code, but it might alter the underlying science while doing so, leaving researchers with results that look right but aren’t. Today, Haiqu is addressing that by unveiling AgenticOS, giving quantum researchers a team of AI agents that are built for scientific exploration, and designed to catch mistakes before they happen.

In complex scientific calculations, general-purpose AI agents can make small changes no one asked for, like shifting a molecular geometry or slipping in an unapproved approximation. The calculation then answers a different question, or returns a result that is simply incorrect. With the Haiqu AgenticOS, researchers can now design an entire quantum project — from the initial question to an experiment that’s ready for quantum hardware — and assign multiple teams of AI agents to validate and confirm the research findings every step of the way. The team overseeing the experiment can then inspect each assumption and conclusion to determine how to proceed.

“Turning a promising research idea into a real experiment demands years of specialized, hands-on expertise in highly technical methods, all of which takes time and resources,” said Mykola Maksymenko, co-founder and CTO of Haiqu. “AgenticOS takes on much of that labor-intensive work, freeing up researchers so they can spend their time working on the right questions, reaching results much faster without sacrificing accuracy.”

AgenticOS builds on the Haiqu platform announced in May. A researcher starts with a question, and the system assigns teams of specialized AI agents to review the literature, work through first-principles mathematical derivations, assess quantum feasibility and check the results. Haiqu’s AgenticOS shows its investigative process as a map of connected research tasks. Results are checked against classical baselines, tests, critic agents and human review, with formal verification. Scientists decide what gets locked, which tasks run on their own and where a human signs off.

During a recent chemistry study accepted at the NeurIPS 2026 workshop in Paris focused on AI for Scientific Discovery, Haiqu demonstrated the need for its platform by giving leading AI agents the same research brief, with and without AgenticOS. On their own, four of 10 agent runs ignored an explicit setup instruction, resulting in widely different answers. Using Haiqu’s AgenticOS, the same models held to the approved protocol from start to finish and produced a simulated quantum result within approximately 3.4% of the exact diagonalization reference.

“Being able to organize, ground and check an AI agent’s work matters as much for scientific research as improving the models themselves,” said Richard Givhan, co-founder and CEO of Haiqu. “Every lab will soon have access to the same powerful AI. The ones that pull ahead will be the ones that can trust what it produces, and that’s exactly what we built AgenticOS to do.”

AgenticOS has already been deployed to work on one of the hardest problems in physics. The Hubbard model describes electrons hopping across a grid and repelling each other when they meet. Haiqu targeted a 2D “doped, frustrated with diagonal hopping” version of the model, which is used to study high-temperature superconductivity in copper-based materials known as cuprates. Starting from a scientist’s idea, AgenticOS built the experiment on a 3-by-3 grid, small enough to cross-check against an exact classical solution, and ran it on an IBM quantum computer. It reduced circuit depth by 75% and proposed a calibration that brought the hardware result in line with what an error-free quantum computer would produce using the same method. That shows a way to apply the same approach to larger simulations that classical computers can’t solve exactly. 

Haiqu’s platform serves enterprise teams testing individual use cases, university research labs and scientists from other fields looking to take their first step into quantum computing. Agents can help users determine if a specific quantum approach is worth pursuing, while mapping out algorithm options, identifying the assumptions and identifying the classical benchmarks needed to judge the result.

When a research plan is ready, AgenticOS hands it straight to Haiqu’s quantum software stack. The Haiqu SDK squeezes more out of today’s noisy hardware with data loading, circuit compression and error mitigation. The Haiqu Runtime then manages execution on simulators and quantum processors from IBM, IonQ, IQM, AWS and others, cutting time and cost by up to 1,000x. The same platform that helps a researcher frame the question can take the answer all the way to a quantum computer.

Availability

AgenticOS is included with the Haiqu operating system, and is available now to enterprise R&D teams upon request. Companies can request access and book a demo now.

Academic researchers can apply for free access to the Haiqu platform, including AgenticOS, through the company’s Academic Program. Participating labs get the Haiqu SDK. They can also apply for AWS credits to cover classical and quantum computing workloads.

Airrived Named a Cool Vendor in the 2026 Gartner® Coolest Vendor Innovations in Agentic AI Security

DUBLIN, Calif.–(BUSINESS WIRE)–Airrived, the company behind an enterprise Agentic OS designed to make every business user an AI expert, has been named a Cool Vendor in the Gartner Coolest Vendor Innovations in Agentic AI Security report published on October 2, 2026.

Tetris debuts in North American cars at NY Comic Con 2026 with VW, AirConsole

 Tetris® makes its North American in-car debut at New York Comic Con with AirConsole and Volkswagen

• New York Comic Con visitors can step inside the new 2027 Volkswagen Atlas and experience AirConsole in-car gaming firsthand at the Pop Insider booth, number 1935
• Fans can play multiplayer Tetris® on the vehicle’s infotainment display, using their smartphones as controllers
• The all-new 2027 Atlas becomes part of the game as its ambient lighting reacts to Tetris gameplay in real time
• The activation runs October 8–11 at the Javits Center, giving more than 250,000 expected NYCC visitors the opportunity to experience AirConsole

New York, US – October 8, 2026; AirConsole, the in-car gaming platform by N-Dream, is teaming up with Volkswagen of America and The Tetris Company to showcase their new collaboration at New York Comic Con 2026. At the center of the activation is the all-new 2027 Volkswagen Atlas hosting a Tetris® experience, built around AirConsole’s signature light integration. Giving fans a hands-on look at how AirConsole transforms everyday drives into shared gaming experiences.

“Bringing AirConsole and Tetris to the new Volkswagen Atlas at the New York Comic Con is a perfect match.” said Anthony Cliquot, CEO of N-Dream, the company behind AirConsole. “The event brings together exactly the kind of fans who will love what AirConsole does. It’s a place where people come to celebrate the stories and games that shaped them, while always looking forward to what’s next. Having a timeless and globally recognized title like Tetris here makes it the perfect setting to showcase how we’re bringing entertainment to the car—an environment where people are spending more and more of their time.”

“New York Comic Con brings together communities built around shared passions and experiences,” said Rachael Zaluzec, Chief Marketing Officer, Volkswagen of America. “That makes it the perfect place to showcase how AirConsole can help turn time spent in a vehicle into something social, engaging, and fun. We’re excited to give fans a chance to experience Tetris in the Atlas and see firsthand how Volkswagen is creating new ways for people to connect.”

The showcase builds on AirConsole’s recent expansion with Volkswagen, which introduced AirConsole in-vehicle gaming to select model year 2027 Volkswagen vehicles in the U.S. — including the Atlas, Atlas Cross Sport, Tiguan, Golf GTI, Golf R, and ID. Buzz — making Volkswagen the first volume automotive brand in the U.S. to offer the platform.

TETRIS MULTIPLAYER AND IMMERSIVE

With Tetris, one of the most recognizable video games of all time comes to the car. Players can enjoy the timeless puzzle gameplay across multiple modes with up to 4 players and experience the action with immersive vehicle integration when parked.

Game features include:

• Sprint – Race to clear 40 lines as quickly as possible, either solo or against other players, and compete for the best time.
• Marathon – Enjoy the classic Tetris experience and chase the highest score, now available for both solo and multiplayer play.
• Battle – Go head-to-head with other players, clearing lines to put pressure on your opponents and gain the upper hand.
• Online Battle – Compete against other Tetris car players from around the world or create a private room to battle your friends.
• Immersive ambient lighting – As players clear lines, the vehicle’s ambient lighting responds in real time — syncing to the color of the cleared line for an arcade-like experience that extends beyond the infotainment screen and into the cabin itself.

“Tetris is one of the most recognized and iconic video game brands in the world, and this is the latest step in how it keeps evolving alongside new players and new technology,” said Maya Rogers, CEO of The Tetris Company. “Bringing Tetris into Volkswagen vehicles with AirConsole turns the Atlas into a multiplayer arcade for fans to enjoy. We can’t wait for fans at New York Comic Con to try it for themselves.”

HOW IT WORKS

AirConsole is included in Volkswagen’s In-Vehicle Premium plan, a connected services package enabled by an active myVW+ subscription. Three months of In-Vehicle Premium is included with the purchase of select new 2027 models. Here’s how new owners can start playing:

1. Make sure the vehicle has been enrolled in the myVW app and it has an active In-Vehicle Premium plan.

2. Once enrolled, the AirConsole app should be visible in the infotainment system’s app menu. To launch the app, tap the AirConsole icon. (vehicle must be parked, some models may require the parking brake to be engaged).

3. Users will then follow the on-screen prompts and scan QR code with their smartphone to connect it as a controller.

4. Once connected, they can use their smartphone to choose a game and start playing.

This intuitive experience makes AirConsole the easiest way to enjoy quick, social games inside the car—whether it’s with passengers, family, or solo during a charging break.

Scality launches AI Inference Factory to bring enterprise AI on-premises

SAN FRANCISCO, October 8, 2026 – Scality, a global leader in data infrastructure software for AI storage at scale, today announced the availability of Scality AI Inference Factory, an open-code software stack for deploying and operating AI inference on enterprise-owned infrastructure. The solution gives enterprises, government agencies and neo-cloud providers a supported alternative to cloud-based AI services, without having to assemble, integrate and maintain the entire software stack themselves.

As AI moves from experimentation into production, organizations are looking for more predictable costs, greater control over model lifecycles and increased data sovereignty. For most organizations, the answer will be hybrid, with the most critical AI processes running on-premises. Running inference locally can address these requirements while keeping sensitive data within infrastructure that organizations own and operate.

Scality AI Inference Factory brings together validated open-weight models, a disaggregated inference-serving layer, a control plane for authentication, metering, routing and scheduling, and Scality AI Data Infrastructure (ADI), which uses policy-governed autonomous operations to manage massive datasets across the AI lifecycle. Scality validates, ships and maintains the integrated stack.

“With AI moving into mission-critical production environments, organizations need greater control over where inference runs, how their models are managed and what happens to their data,” said Jérôme Lecat, CEO at Scality. “For 15 years, Scality has built data infrastructure that thousands of customers around the globe rely on to operate 24/7. AI Inference Factory brings that experience to on-premises AI, giving organizations the reliability and sovereignty they need to run critical AI workloads on their own terms.”

Cloud AI challenges drive interest in on-premises inference 

Enterprises scaling AI in production are confronting challenges with the cloud-based inference model. With per-token pricing, the more useful an AI workflow becomes, the more it costs, producing bills that are difficult to forecast and impossible to cap. Model versions and quantization can change at the provider’s discretion, affecting the workflows built on them. Organizations with sensitive or regulated data face sovereignty concerns over where their prompts, documents and proprietary code are processed, under which jurisdiction, and who can restrict access to them.

Scality AI Inference Factory is designed for organizations that want to run critical AI workloads on infrastructure they control while retaining flexibility across models, hardware and software. The stack supports specialized chatbots, AI-assisted software development, agentic applications and other inference-intensive workloads.

Scality AI Inference Factory combines:

• Validated open-weight models maintained as part of the supported stack;
• Disaggregated inference serving that separates prefill from decode so each can scale independently;
• A control plane that authenticates and meters requests, routes them to the appropriate GPUs holding the relevant context and schedules workloads against SLA targets; and
• Scality ADI, which provides high-performance object storage for models, enterprise data and inference state.

Scality validates and maintains the complete stack as a supported solution, helping organizations keep pace with rapidly changing models, serving technologies and security requirements without having to integrate and maintain the individual components themselves.

“As AI adoption expands and AI agents become more prevalent, organizations are evaluating which IT environments are best suited to run inference. On-premises deployments are coming into focus for a variety of reasons, including cost optimization, data security and privacy, and regulatory requirements. IDC research shows that running inference on-premises is a credible option for a growing set of enterprise and public sector workloads, provided the infrastructure can hold and serve model state efficiently at scale. Scality is among the vendors addressing that requirement across the inference and storage layers.” — Nataliya Yezhkova, Vice President, Storage and Data Management, Enterprise Infrastructure, IDC

Scality ADI extends AI inference beyond GPU memory constraints

Scality ADI serves as the shared storage layer for AI Inference Factory, extending key-value (KV) cache beyond limited GPU high-bandwidth memory (HBM). This allows inference infrastructure to preserve and retrieve model context instead of requiring GPUs to recompute it, improving GPU utilization and reducing inference costs.

This is particularly important for reasoning models and agentic workloads, where KV cache can quickly exceed available GPU memory. Scality ADI provides a shared, multi-petabyte cache that GPUs read at latency the same order of magnitude as GPU memory, allowing them to retrieve existing context and remain focused on inference.

Scality testing demonstrated that ADI can reduce GPU consumption while maintaining the same level of performance:

• 1.9-second load time for Gemma-3 27B, almost 10x faster than local NVMe, with the model streamed in parallel across the cluster over RDMA;
• 166 ms warm time-to-first-token on a 14K-token context restore from ADI, only 83 ms behind HBM;
• 14x faster KV cache retrieval than recomputation on a 14K-token context and up to 72x faster on a 439K-token context;
• A KV cache more than 80x larger than a single GPU’s memory, keeping up to 1,000 concurrent sessions resumable without recomputation;
• No measurable impact on token generation, as context is restored before the first token; and
• 97% of network line rate for data transfer between GPUs and storage.

The architecture separates prefill from decode, with ADI acting as the shared cache between GPU pools. This allows available decode GPUs to access existing context without tying it to a specific GPU server. Separating prefill from decode has documented gains: DistServe (OSDI 2024) measured up to 7.4 times more requests served within the same latency targets. Adding a shared KV cache pool on top, Moonshot AI’s Mooncake reported 75 percent more requests on Kimi’s production traffic.

As Giorgio Regni, Scality CTO, puts it: “The KV cache on Scality ADI is fast enough to sit in the serving path. Restoring a context from ADI is the same order of magnitude as GPU memory, and 14 times faster than recomputing it, with the GPUs staying busy the whole time. Storage is no longer the reason to keep the KV cache inside the GPU server.”

Regni continues: “That opens the door to disaggregated serving. One pool of GPUs handles prefill, processing the prompt and writing the KV cache to ADI. A second pool handles decode, reading the cache back and generating tokens. With Scality ADI as the shared cache, any decode GPU can pick up any context, and there is essentially no size limit. Prefill no longer interrupts decode, and both pools run at full load.”

For a technical deep dive into the AI Inference Factory architecture and test results, visit Scality’s companion blog.

Open architecture provides flexibility across models, frameworks and hardware

Scality AI Inference Factory is designed to preserve customer choice across the AI infrastructure stack. It is compatible with open-source harnesses and agentic frameworks such as OpenCode, Hermes, Goose, LangGraph and Pydantic AI, and runs validated open-weight models such as Mistral, Gemma, gpt-oss, Qwen, Kimi, GLM, and DeepSeek. The software deploys on standard servers from Dell, HPE, Lenovo and Supermicro.

Every software component Scality ships is delivered as open code, enabling customers to inspect how inference state is stored and moved, and to submit contributions for Scality review. For organizations running sensitive, regulated or sovereign AI workloads, this provides transparency into the software handling their models and data rather than requiring them to rely on a closed external service. 

Scality ADI integrates with standard AI stacks and deploys at multi-petabyte scale on NVMe, with RDMA access to HDD for virtually unlimited KV cache capacity. A single namespace automatically tiers data across TLC, HDD and, optionally, tape, allowing organizations to align storage performance and economics with different stages of the AI data lifecycle.

Availability

Scality AI Inference Factory is available now as a software license or as a fully managed service. Scality is demonstrating the solution and its reference architecture at Scality Day on October 8 in Paris. To learn more, visit the Scality AI Inference Factory web page and detailed companion blog.

TÜV SÜD on the New Management System Standard for Sustainability Goals

Munich. ISO/UNDP 53001 was published at the end of September. It provides  organizations with a new international framework for integrating the UN Sustainable  Development Goals into their management structures. Developed by the International  Organization for Standardization (ISO) and the United Nations Development Program  (UNDP), the ISO/UNDP 53001 standard is the world’s first management system standard  of its kind. It provides a structured approach for integrating the UN Sustainable  Development Goals (SDGs) into corporate governance, strategy, and operational  processes. TÜV SÜD provides information on the newly published standard. 

Sustainability has become an integral part of corporate strategy in many companies. However,  consistently embedding environmental, social, and economic goals into existing management  structures remains a challenge. ISO/UNDP 53001 addresses this need through a management  system approach that helps companies define responsibilities, set goals, and monitor progress  as part of a continuous improvement process. Since the standard follows the harmonized  structure used in all ISO management system standards, it can be integrated into existing  quality, environmental, or energy management systems. 

“Sustainability can only be effective if it is integrated into a company’s daily decision-making  and management processes,” says Sara Brandimarti, Global Head of Sustainability – Business  Assurance Division at TÜV SÜD. “ISO 53001 provides a structured framework for this. It helps  organizations systematically consider sustainability aspects and derive long-term changes  from them that go beyond individual projects or reporting requirements.” 

Response to Rising Stakeholder Expectations 

The expectations of customers, investors, and regulatory authorities regarding sustainability  continue to rise. Transparent and systematic management of sustainability issues is becoming  increasingly important, particularly for companies operating in complex international supply  chains. With ISO/UNDP 53001 as a certifiable standard, an independent certification body  such as TÜV SÜD can now assess whether an organization’s SDG management system meets the standard’s requirements. Organizations that take this approach demonstrate that  they manage their sustainability-related activities within a structured and transparent  management framework. 

AAEON Heads to Scanautomatic 2026 to Deliver New Platforms, Demos, and More

Join AAEON at the premier meeting point for industrial automation and digital transformation.

Taipei, Taiwan – Oct 09: AAEON (Stock Code: 6579), a leading Edge AI platform provider, will present a range of new products at Booth #B06:29 of Scanautomatic 2026, held from October 20 to October 22, 2026.

Date: October 20 – October 22, 2026

Booth: #B06:29

Venue: Mässans Gata/Korsvägen Göteborg, Sweden

With the show acting as a cross-industry meeting point, AAEON will join over 250 exhibitors from across the larger automation and industrial digitalization ecosystem. Over the three days, Scanautomatic will also play host to over 60 seminars and feature a broad showcase of over 600 product categories.

Among an array of new products on show at booth #B06:29, visitors will also note a live physical AI-guided defect detection demonstration developed with Awentia’s AVT Physical AI Core. Running on the BOXER-8622AI, a Compact Fanless Embedded AI System powered by NVIDIA Jetson Orin Nano with Super Mode, the application utilizes 3D reconstruction to map product defects in real-time, illustrating the potential that combining machine vision with physical AI has for precision manufacturing quality control.

Throughout the show, AAEON will host a static lineup of products conducive to bringing new capabilities to the automation sphere, including the upcoming UP Display ASL15, the first smart display kit to be released by AAEON’s UP brand. The product, which features an Intel Processor N-series computing base with a 15.6″ projected capacitive multi-touch panel and fully integrated industrial I/O, was released earlier this month and positioned for human-machine interfaces and edge-based industrial automation solutions.

Another particularly interesting platform on show will be the EATX-W890A, a workstation motherboard powered by Intel Xeon 600 processors and equipped with up to seven 16-lane PCIe Gen 5 slots. The board, which will be geared towards more server-grade industrial applications, is expected to be launched during H1, 2027.

AAEON will also present a wealth of additional platforms from across its single-board computer, embedded AI system, and developer board ranges equipped with technologies from leading chip providers such as Intel, NVIDIA, MediaTek, NXP, and Qualcomm.

Visitors are encouraged to join AAEON at Booth #B06:29, where representatives will be available to discuss AAEON’s product roadmap and provide details of the products on display.

Kore.ai Launches Autoloop, the Optimization Engine That Keeps AI Agents Performing at Production Scale

SAN MATEO, Calif.—October 7, 2026 – Kore.ai, the global leader in enterprise AI platform and agentic applications, today announced general availability of Autoloop^TM, the optimization engine of the Kore.ai Agent Platform, Artemis edition. Enterprises set the goals. Autoloop builds the agents, measures them against those goals, and keeps optimizing them automatically, from first draft through production. The launch marks a shift in how enterprise AI is run: from agents tuned by hand to those that self-improve against the goals set by the business.

Today, most enterprises maintain agents through a cycle of fixing individual failures, and a fix for one problem often creates another. The cost of that approach is now measurable: the 2026 Kore.ai Agent Productivity Index found that 79% of enterprises have reversed an action taken by an AI agent, and 70% have faced a failure their teams could not trace. However, the industry is shifting, and Gartner predicts that by 2030, autonomous learning techniques will be present in a majority of AI agents, up from less than 5% in 2026.

“Every enterprise knows what it wants from its agents: finish the job, follow the rules, stay safe, and do it at a sensible cost,” said Raj Koneru, Founder and CEO of Kore.ai. “With Autoloop, your agents keep improving against the goals you set. The companies that scale AI will be the ones using AI to build, govern, and optimize AI.”

Autoloop starts from goals, not prompts. Teams define what success looks like across the dimensions that matter to the business, and Autoloop optimizes against all of them at once:

• Task completion: the agent finishes what the user came to do, handing off to a human only when it should.
• Accuracy and grounding: every answer is correct and backed by the enterprise’s own data, with no fabricated facts.
• Business-rule adherence: complies with policies, eligibility checks, and limits on every interaction.
• Guardrails and safety: no data exposure, off-policy actions, or unsafe responses.
• Consistency: consistent behavior across phrasings, languages, channels, and edge cases.
• End-user experience: fewer turns, less repetition, and lower latency across chat and voice.
• Cost efficiency: the same outcome with fewer, faster, and more affordable model calls and tokens.

Against these goals, Autoloop runs one continuous loop spanning: build, evaluate, diagnose, optimize, and re-verify. Before launch, it builds the agent and its test coverage from the enterprise’s own operating procedures and iterates until the agent meets every goal. In production, real interactions start new optimization cycles. Every change is scored against all goals at once, so a gain on one, such as lower token spend, cannot quietly hinder another, such as task completion or safety.

Automatic optimization requires complete visibility into agent activity and control over how they change. Two Kore.ai innovations give Autoloop these capabilities:

• StateTrace delivers the visibility, evaluating agents against their full production execution context. It traces every handoff, delegation, state, tool call, and piece of context across the agent network, so Autoloop knows where and why a goal was missed. A patent-pending five-layer validation architecture makes most checks deterministic, which keeps continuous optimization affordable at enterprise scale.
• Agent Blueprint Language (ABL) delivers the control by compiling supervision, routing, handoffs, delegation, tools, business rules, and guardrails into an executable state machine. Every step in a trace maps back to the blueprint, so Autoloop changes exactly the part that caused the miss, and nothing else.

Kore.ai runs the same discipline on its own engineering, where AI agents now produce roughly 6,500 commits a month on a production codebase of 2.6 million lines, governed by 68 always-on guardrails.

“You can’t optimize what you can’t see, or fix precisely what you can’t express precisely,” said Prasanna Arikala, Chief Technology Officer and Chief Product Officer at Kore.ai. “StateTrace lets Autoloop see exactly what agents did, and ABL allows it to change anything that needs changing. These are the technologies that make automatic optimization a reality.”

Today’s launch extends the ground Kore.ai has staked out in the market. Much of the industry sells tools that create agents, or control layers added after the fact. Kore.ai builds the harness: a single layer that builds, deploys, manages, and optimizes enterprise AI agents, with governance defined from the first line. Autoloop completes it, because only an agent built and governed in one layer can be optimized in that same layer, automatically and with evidence.

Zapata Quantum Joins IBM Quantum Network to Accelerate Mission-Driven Quantum Application Development

Effort builds on prior collaboration to translate high-value use cases into practical applications running on IBM quantum computers 

“The rapid progress IBM is making on the path to fault-tolerant quantum computing brings the quantum application gap into sharper focus,” said Sumit Kapur, CEO of Zapata Quantum. “These advances must be matched by progress in identifying, evaluating and developing the applications that will deliver real-world value. We are excited for Zapata’s Quantum Application Intelligence™ to translate organizations’ highest-mission priorities into practical quantum applications.”

“IBM’s roadmap to fault-tolerant quantum computing is one of the most credible in the industry, and superconducting qubits remain one of the most capable paths to advantage because their hardware is fast,” said Jonny Olson, VP Strategy and Co-Founder of Zapata Quantum. “Joining the IBM Quantum Network lets us better identify valuable opportunities for organizations that need the fastest systems, and then prepare those organizations for the precise moment that quantum gives them real ROI.”

Joining the IBM Quantum Network builds on a history of joint IBM and Zapata research done on early quantum devices. Zapata used IBM quantum computers in a landmark study published in Nature Biotechnology that produced experimentally validated candidates targeting KRAS and was selected as one of the journal’s Top 10 Articles of 2025.¹ Scientists from the two companies also co-authored research published in npj Quantum Information, which advanced the understanding of variational quantum factoring on a superconducting processor.²

Being part of the IBM Quantum Network also reflects Zapata’s broader strategy of working with technology leaders across the quantum and AI ecosystems. Insights from being part of the IBM Quantum Network will inform Quantum Pilot™, Zapata’s hardware-agnostic platform that helps enterprises identify, prioritize, and develop quantum applications as hardware matures.