# Enterprise Published articles for Enterprise. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Экосистема Digital Q от "Диасофт" вошла в число лидеров рейтингов CIO Navigator благодаря AI-driven подходу к разработке DevFeed: [Экосистема Digital Q от "Диасофт" вошла в число лидеров рейтингов CIO Navigator благодаря AI-driven подходу к разработке]() Original publisher: [Read original article]() Author: diasoft (Диасофт) Published: 2026-09-17T08:08:22Z Content type: news Language: ru Sources: [Tagir Valeev]() Topics: [Low code](), [Artificial Intelligence](), [Development](), [sdlc](), [Code]() Tags: [2026](), [agents](), [ai](), [ai-driven-ab7423f43dcb](), [development](), [digital-q](), [enterprise](), [low-code](), [sdlc](), [specification-driven-development](), [tag-077d33a42465](), [tag-2c039dce53be](), [tag-463bcbb8c0fe](), [tag-73efb20f7e33](), [tag-7b800b2da0b8]() ### AI overview Diasoft's Digital Q development ecosystem led the 2026 CIO Navigator ranking of Russian low-code solutions with AI features and placed second overall among 14 platforms. The article describes its AI-driven approach, including AI agents across the software development lifecycle and the use of machine-readable specifications to generate development artifacts. ### Source excerpt Компания "Диасофт" вошла в число лидеров сразу двух рейтингов российских low-code платформ 2026 года, опубликованных Санкт-Петербургским Клубом ИТ-директоров CIO Navigator. Экосистема разработки Digital Q возглавила рейтинг low-code решений с ИИ-функциями и заняла второе место в общем рейтинге российских low-code платформ. Лидерство экосистемы для разработчиков Digital Q в рейтинге российских low-code платформ с функциями ИИ стало возможным по мнению организаторов рейтинга благодаря AI-driven подходу, при котором искусственный интеллект используется на всех этапах создания и развития программного обеспечения. Участников исследования оценивали по более чем 170 критериям, охватывающим возможности искусственного интеллекта, архитектуру, инструменты разработки и другие характеристики, значимые для корпоративного применения. В общем рейтинге российских low-code платформ Digital Q заняла второе место среди 14 представленных решений. Исследование включало более 180 критериев - по функциональности, архитектуре, безопасности, интеграционным возможностям, инструментам управления жизненным циклом разработки и ИИ-функциям. CIO Navigator характеризует Digital Q как корпоративную low-code экосистему для создания и развития микросервисных информационных систем уровня enterprise, которая развивается в направлении AI-driven платформы для управляемой ИИ-разработки. В основе развития Digital Q лежит переход от использования ИИ как отдельного помощника разработчика к модели AI-Native SDLC, в которой искусственный интеллект становится полноценным участником жизненного цикла создания программного обеспечения. ИИ-агенты включаются в работу с требованиями, проектирование, разработку, тестирование и последующее сопровождение решений. Для их оркестрации в экосистеме используется платформа Digital Q.Agents. Именно сквозное применение ИИ на протяжении всего цикла разработки CIO Navigator выделяет как одно из ключевых отличий Digital Q. Читать далее ## MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers DevFeed: [MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-16T15:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Benchmark](), [Inference Performance](), [Retrieval Augmented Generation (RAG)](), [agentic-coding](), [GPU](), [Vera Rubin NVL72](), [Nvidia](), [Vera Rubin]() Tags: [agentic-coding](), [ai](), [benchmark](), [enterprise](), [gpt-oss](), [gpu](), [inference](), [llama-cpp](), [numbers](), [nvidia](), [qwen3](), [rag]() ### AI overview MLCommons published MLPerf Inference v6.1 with record participation, two new inference tests, and peer-reviewed results for several newly covered accelerators. The release reports a 5.7x improvement in the best per-accelerator DeepSeek-R1 server result compared with v5.1. ### Source excerpt MLCommons has published MLPerf Inference v6.1, and the round sets a participation record with 30 submitting organizations and 486 datacenter and edge results. Two new tests join the suite: an End-to-End RAG pipeline for the datacenter and an Edge Agentic Inference benchmark for single-user devices, and the results carry the first peer-reviewed numbers for NVIDIA's The post MLPerf Inference v6.1: 5.7x Per-Accelerator Gains, a 512-GPU Run, and Vera Rubin's First Peer-Reviewed Numbers appeared first on StorageReview.com. ## Micron Shows off 512GB DDR5 RDIMM: 12TB per Dual-Socket Server at 9,200 MT/s, Volume Production in 2H 2027 DevFeed: [Micron Shows off 512GB DDR5 RDIMM: 12TB per Dual-Socket Server at 9,200 MT/s, Volume Production in 2H 2027]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T20:18:17Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [ddr5](), [servers](), [intel](), [cpu]() Tags: [capacity](), [ddr5](), [enterprise](), [generation](), [intel](), [memory](), [modules](), [performance](), [production](), [release](), [server](), [speed](), [volume]() ### AI overview Micron demonstrated a 512GB DDR5 RDIMM rated for up to 9,200 MT/s. The module can provide 12TB of memory in a 24-slot dual-socket server, with volume production scheduled for the second half of 2027. AMD and Intel are validating it for next-generation server platforms. ### Source excerpt Micron has demonstrated a 512GB DDR5 RDIMM running on multiple server platforms, which it calls the world's first module at that capacity, and says AMD and Intel are both validating it for their next-generation server platforms. The module is rated for speeds up to 9,200 MT/s, and in a 24-slot dual-socket server it puts 12TB The post Micron Shows off 512GB DDR5 RDIMM: 12TB per Dual-Socket Server at 9,200 MT/s, Volume Production in 2H 2027 appeared first on StorageReview.com. ## Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck DevFeed: [Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-15T17:23:54Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Data Infrastructure](), [AI Infrastructure](), [idc](), [Inference](), [Retrieval-Augmented Generation](), [synthetic-data]() Tags: [ai](), [compute](), [data-infrastructure](), [datasets](), [enterprise](), [genai](), [hdd](), [idc](), [inference](), [reports](), [research](), [retrieval](), [storage](), [synthetic-data]() ### AI overview Seagate and WD published separate studies indicating that AI is increasing enterprise storage requirements and extending data retention. Although their headline percentages differ because they asked different questions, both reports point to storage becoming a larger part of AI infrastructure planning alongside growing archive and retrieval needs. ### Source excerpt Seagate and WD published separate AI storage studies within days of each other; the headline numbers: Seagate says 99% of enterprises expect AI to increase their storage requirements over the next three years, while WD's IDC research puts the comparable figure at 74%. Read the fine print, and both reports land in the same directional The post Seagate and WD AI Storage Research Finds Enterprises Rank Storage Above Compute as the AI Bottleneck appeared first on StorageReview.com. ## OWC Acquires OpenDrives, Adding Atlas, Astraeus, and Edge to the Jellyfish Shared Storage Line DevFeed: [OWC Acquires OpenDrives, Adding Atlas, Astraeus, and Edge to the Jellyfish Shared Storage Line]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T16:55:08Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data-architecture](), [cloud-infrastructure](), [Hardware](), [data-processing]() Tags: [acquisition](), [cloud-native](), [company](), [enterprise](), [enterprise-storage](), [hybrid-cloud](), [infrastructure](), [products](), [storage]() ### AI overview Other World Computing (OWC) has acquired OpenDrives, bringing the Atlas, Astraeus, and Edge platforms into its Jellyfish shared storage portfolio. The companies say the deal expands OWC's capabilities in enterprise data management, hybrid cloud orchestration, and edge workflows; financial terms were not disclosed. ### Source excerpt Other World Computing (OWC) has acquired OpenDrives, the Los Angeles software-defined storage company whose Atlas platform has sat behind Hollywood studios, post houses, and live broadcast networks since 2011. The deal brings OpenDrives' Atlas, Astraeus, and Edge platforms into OWC's shared storage portfolio alongside the Jellyfish line, and OWC says it extends that line into The post OWC Acquires OpenDrives, Adding Atlas, Astraeus, and Edge to the Jellyfish Shared Storage Line appeared first on StorageReview.com. ## NVIDIA Adds CUDA-Q Logical for Fault-Tolerant Quantum Application Design DevFeed: [NVIDIA Adds CUDA-Q Logical for Fault-Tolerant Quantum Application Design]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T16:47:58Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [CUDA](), [Quantum Computing](), [Nvidia](), [Orchestration](), [Open Source](), [Algorithm]() Tags: [ai](), [algorithm](), [applications](), [cuda](), [enterprise](), [nvidia](), [open-source](), [orchestration](), [quantum]() ### AI overview NVIDIA added CUDA-Q Logical to its open-source CUDA-Q platform for designing applications on fault-tolerant quantum computers. Early-access reports say Fermilab reduced an algorithm design cycle from five months to three weeks, while Iceberg Quantum modeled a Diraq spin-qubit architecture using about 150,000 physical qubits for 1,000 logical qubits. ### Source excerpt NVIDIA has added CUDA-Q Logical to its open-source CUDA-Q platform, an orchestration layer for building applications that run on fault-tolerant quantum computers, and it arrives with two numbers that are interesting. Fermilab says the tool cut a fault-tolerant algorithm design cycle from five months to three weeks, and Iceberg Quantum used it to show that The post NVIDIA CUDA-Q Logical Debuts With a 7x Fermilab Speedup and a 10x Cut in Diraq's Qubit Estimate appeared first on StorageReview.com. ## Australia's Essential Eight replacement shifts cybersecurity compliance toward continuous exposure management DevFeed: [Australia's Essential Eight replacement shifts cybersecurity compliance toward continuous exposure management]() Original publisher: [Read original article]() Author: Ben Mudie Published: 2026-09-15T13:32:00Z Content type: article Language: en Sources: [Tenable Blog]() Topics: [Exposure Management](), [Cybersecurity](), [Security](), [Cloud](), [App]() Tags: [australia](), [ciso](), [cloud](), [compliance](), [cybersecurity](), [enterprise](), [essential-eight](), [exposure-management](), [identity](), [operational](), [organization]() ### AI overview The article describes Australia's replacement of the Essential Eight with an outcomes-focused cybersecurity framework covering enterprise IT, cloud, operational technology, and potentially agentic AI. It argues that organizations will need continuous evidence of their security posture, and presents exposure management as a way to identify and prioritize weaknesses and support current posture validation. ### Source excerpt Australia's move from the Essential Eight to an outcomes-based cybersecurity model will push organizations from conducting periodic point-in-time, checklist compliance assessments to having continuous evidence of a solid security posture. Key takeaways The Australian Signals Directorate (ASD) is moving from the Essential Eight cybersecurity framework to a new outcomes-focused Essentials series covering enterprise IT, cloud, operational technology (OT), and potentially agentic AI. The Essential Eight itself only ever covered on-premises enterprise IT, built around eight named technical controls, such as application control and patching. It never extended to the security of cloud, identity, or OT. The shift challenges the traditional checklist approach to cybersecurity, where organizations demonstrate compliance through periodic assessments and point-in-time reports. In dynamic environments spanning IT, cloud, identity, and OT, security posture can change quickly and repeatedly between assessments. Exposure management can help organizations continuously understand where they are exposed, prioritize the most critical weaknesses, and provide evidence of their current security posture. ASD's strategic shift to active security posture validation Can you prove your security posture is solid, right now, on demand? That's the question the Australian Signals Directorate (ASD) has effectively put in front of every Australian organization's board, CISO, and C-suite. ASD's decision to retire the Essential Eight signals a fundamental move away from point-in-time, checklist-based security toward an outcomes-focused model where organizations will need to demonstrate continuous compliance. It's no longer enough to show that your organization had a control in place at the time of the last assessment. In a technology environment that changes continuously across IT, cloud, identity, and operational technology (OT), organizations must be able to answer a much more immediate question: Ho ## Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers DevFeed: [Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [Inference](), [Computer vision](), [servers](), [dell](), [RISC-V]() Tags: [ai](), [computer-vision](), [dell](), [enterprise](), [inference](), [risc-v](), [servers]() ### AI overview Axelera AI is shipping Europa, a second-generation AI Processing Unit, in bare-chip and PCIe card configurations. The company says the 45W device delivers 629 TOPS and supports on-premises inference workloads including generative AI, vision-language models, and computer vision. The Edge 232p card is shipping in validated Dell XE5 and Supermicro 111AD systems. ### Source excerpt Axelera AI is shipping Europa, the second-generation AI Processing Unit (AIPU) it has been previewing since last year, and it's launching with validated servers from Dell and Supermicro attached. The Eindhoven company's pitch is inference on infrastructure the customer controls: agentic systems, vision-language models, generative AI, and computer vision running in a standard rackmount server The post Axelera Europa Ships: 629 TOPS at 45W Per AIPU, in Validated Dell XE5 and Supermicro Servers appeared first on StorageReview.com. ## Just 4.4% of Enterprise NAS Capacity Is Active, CTERA Cold Data Storage Report Finds DevFeed: [Just 4.4% of Enterprise NAS Capacity Is Active, CTERA Cold Data Storage Report Finds]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-15T12:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [data](), [Filesystems](), [Security](), [Replication](), [Artificial Intelligence]() Tags: [data](), [enterprise](), [enterprise-nas](), [enterprise-storage](), [nas](), [report](), [security](), [storage]() ### AI overview StorageReview reports on CTERA's Cold Data Storage Report, which analyzed 16 petabytes of production data from 856 enterprise NAS file-share discovery scans. The report found that 4.4% of stored capacity was actively used within 90 days, while most files were cold or inactive. The article discusses the resulting storage, data-protection, security, and AI-retrieval implications. ### Source excerpt CTERA has published The Cold Data Storage Report, an analysis of 16 petabytes of live production data across 856 file-share discovery scans in enterprise NAS environments, including regulated industries, and the headline number is that just 4.4% of stored capacity is actively used, which the report page defines as modified within a 90-day window. The The post Just 4.4% of Enterprise NAS Capacity Is Active, CTERA Cold Data Storage Report Finds appeared first on StorageReview.com. ## How Databricks' marketers use data 3x more with Genie, an AI analytics assistant DevFeed: [How Databricks' marketers use data 3x more with Genie, an AI analytics assistant]() Original publisher: [Read original article]() Author: Elizabeth Dobbs; Thomas Russell; Katy Yuan; Sydney Sundell Published: 2026-09-15T00:36:33Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Artificial Intelligence](), [data](), [dashboards]() Tags: [ai](), [best-practices](), [dashboards](), [data](), [databricks](), [enterprise](), [governance](), [marketing](), [metrics]() ### AI overview Databricks describes how its marketing organization unified campaign, sales, CRM, web analytics, advertising, and other data in a governed lakehouse. It built Marge, a Genie Agents-based conversational analytics assistant that answers marketers' natural-language questions using governed enterprise data. The article says this approach helped the marketing department use data three times more often in decisions. ### Source excerpt Most marketing teams aspire to be data-driven. In practice, getting a trusted answer,... ## Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November DevFeed: [Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November]() Original publisher: [Read original article]() Author: Lyle Smith Published: 2026-09-14T18:03:44Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AI Inference](), [AI Infrastructure](), [Inference Performance](), [cpu](), [data centers](), [Confidential Computing](), [Arm](), [Hardware](), [NVLink](), [Nvidia]() Tags: [ai](), [ai-inference](), [ai-infrastructure](), [arm](), [benchmark](), [data-centers](), [enterprise](), [fujitsu](), [hardware](), [nvidia](), [nvlink]() ### AI overview Fujitsu is introducing MONAKA Servers built around its 2nm FUJITSU-MONAKA processor for AI inference in air-cooled data centers. The servers offer up to 144 CPU cores, matrix instructions, SVE2 vector processing, hardware-level confidential computing, and planned NVLink Fusion integration with NVIDIA GPUs. Fujitsu claims higher inference throughput and reduced cooling power consumption, but the article notes that supporting benchmark details are unavailable. ### Source excerpt Fujitsu is bringing its 2nm FUJITSU-MONAKA processor to AI infrastructure with a new server family designed to run AI inference in air-cooled data centers without requiring specialized liquid cooling. The MONAKA Server is designed, developed, and manufactured in Japan, with component and manufacturing traceability for sovereign AI deployments. The first MONAKA Servers will come in The post Fujitsu MONAKA Server Brings 2nm 144-Core CPUs to Air-Cooled AI Inference, On Sale in November appeared first on StorageReview.com. ## FS Pairs 1.6T Scale-Out Optics With 500 km Coherent Modules and a Handheld Toolkit for AI Fabrics DevFeed: [FS Pairs 1.6T Scale-Out Optics With 500 km Coherent Modules and a Handheld Toolkit for AI Fabrics]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T17:34:25Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [GPU](), [InfiniBand](), [Networks](), [Ethernet](), [datacenter](), [Nvidia](), [data]() Tags: [announcement](), [data-center](), [enterprise](), [ethernet](), [gpu](), [infiniband](), [networking](), [networks](), [nvidia]() ### AI overview FS presents a two-part optics portfolio for AI networking: 400G, 800G, and 1.6T Scale-Out transceivers for links within GPU clusters, plus 400G and 800G Scale-Across coherent modules for connecting clusters across sites up to 500 km. The announcement also introduces the BOX 5 Ultra handheld toolkit for configuring, validating, and monitoring transceivers from 100M to 1.6T. ### Source excerpt FS has organized its AI optics into a two-part portfolio: Scale-Out transceivers at 400G, 800G, and 1.6T for the links inside a GPU cluster, and Scale-Across coherent modules at 400G and 800G for stitching clusters together across sites at distances up to 500 km. The Scale-Out side covers Ethernet, RoCE, and InfiniBand fabrics between GPU The post FS Pairs 1.6T Scale-Out Optics With 500 km Coherent Modules and a Handheld Toolkit for AI Fabrics appeared first on StorageReview.com. ## NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error DevFeed: [NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:43:16Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [lunar foundation model](), [foundation-models](), [ibm](), [Open Source](), [hugging face](), [dataset](), [multimodal](), [Benchmark]() Tags: [ai](), [benchmark](), [data](), [enterprise](), [fine-tuning](), [foundation-models](), [hugging-face](), [ibm](), [lunar-foundation-model](), [machine-learning](), [multimodal](), [nasa](), [open-source](), [space]() ### AI overview IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source on Hugging Face, along with its weights, technical report, and training dataset. Built on TerraMind, the model uses multimodal lunar observations for tasks including ice-deposit mapping, volcanic-feature detection, and crater detection. Reported benchmarks show up to 22% lower ice-mapping error than SwinV2-B, while the accompanying dataset contains roughly 2 million image tiles from nine instruments across four lunar missions. ### Source excerpt IBM and NASA have released the NASA-IBM Lunar Foundation Model as open source, one of the first publicly available foundation models built for scientific study of the Moon. The weights, a technical report, and the machine-learning-ready dataset it was trained on are up on Hugging Face under the Prithvi family, which already covers Earth observation, The post NASA-IBM Lunar Foundation Model Goes Open Source With a 2M-Tile Dataset and 22% Lower Ice-Mapping Error appeared first on StorageReview.com. ## Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts DevFeed: [Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-14T16:23:21Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Inference](), [Inference Performance](), [Orchestration](), [GPU](), [Multi-tenancy](), [sglang](), [TensorRT](), [vllm](), [Security]() Tags: [ai](), [cache](), [concurrent](), [enterprise](), [gpu](), [inference](), [latency](), [nvme](), [orchestration](), [security]() ### AI overview Lightbits Labs is introducing Inferra, a KV cache orchestration engine for AI inference. It virtualizes GPU memory across DRAM and NVMe storage, preserving attention states for long-context and multi-session workloads. Lightbits claims up to 16 times more concurrent sessions, more than 100 times lower latency than recomputation, and context windows of up to 10 million tokens. Inferra supports vLLM, TensorRT, and SGLang and includes tiering, predictive prefetching, tenant isolation, and encrypted data transfer. ### Source excerpt Lightbits Labs, the company that invented NVMe over TCP, is moving into inference software with Inferra, a KV cache orchestration engine that makes its public debut tomorrow, September 15, at the AI Infra Summit in Santa Clara. The software virtualizes GPU memory across DRAM and NVMe storage tiers and turns the KV cache into a The post Lightbits Inferra KV Cache Engine Claims 16x Session Density and 10M-Token Contexts appeared first on StorageReview.com. ## OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards DevFeed: [OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards]() Original publisher: [Read original article]() Author: Kristi Piechnik Published: 2026-09-14T12:00:14Z Content type: news Language: en Sources: [OpenSearch]() Topics: [Open Source](), [AI Infrastructure](), [observability](), [AI search](), [Security](), [Retrieval Augmented Generation (RAG)](), [Databases](), [real-time]() Tags: [2026](), [agentic-ai](), [ai-agents](), [ai-infrastructure](), [announcements](), [awards](), [data](), [enterprise](), [observability](), [open-source](), [opensearch](), [real-time](), [recognition](), [retrieval](), [retrieval-augmented-generation-rag](), [search](), [security]() ### AI overview OpenSearch won the Analytics & Data Intelligence Solutions category in SiliconANGLE Media's 2026 TechForward Awards. The recognition highlights its open source, vendor-neutral platform for enterprise search, observability, security analytics, vector databases, and agentic AI workloads. ### Source excerpt Recognition validates open source momentum, architectural consolidation, and enterprise scale as the project marks five years of community growth The post OpenSearch Wins Analytics & Data Intelligence Solutions Category in the SiliconANGLE TechForward Awards appeared first on OpenSearch. ## Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills DevFeed: [Presentation: Decision Models in Agentic Architectures: From Production to Agent Skills]() Original publisher: [Read original article]() Author: Alex Porcelli Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Agent Skills](), [Artificial Intelligence](), [Large Language Model](), [NeMo]() Tags: [agent](), [agent-skills](), [agentic](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [architectures](), [artificial-intelligence](), [business](), [decision-models-agentic-ai](), [development](), [enterprise](), [enterprise-architecture](), [governance](), [infoq](), [large-language-models](), [llms](), [ml-data-engineering](), [models](), [nemo](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [skills](), [transcripts]() ### AI overview Alex Porcelli explains how DMN decision models can be integrated with LLMs, agent skills, and NeMo guardrails to create auditable and deterministic agentic architectures for high-stakes enterprise decisions. ### Source excerpt Alex Porcelli discusses the critical gap in enterprise AI: non-deterministic output and lack of accountability in high-stakes decisions. He shares how integrating DMN decision models with LLMs, agent skills, and NeMo guardrails creates auditable, deterministic agentic architectures - allowing business leaders to own decision logic while engineers maintain robust architectural governance. By Alex Porcelli ## AI and its main promoters are not enterprise-ready, says Gartner DevFeed: [AI and its main promoters are not enterprise-ready, says Gartner]() Original publisher: [Read original article]() Author: Simon Sharwood Published: 2026-09-14T05:11:19Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Artificial Intelligence](), [ai and ml]() Tags: [ai](), [ai-and-ml](), [enterprise](), [gartner](), [model]() ### AI overview The article reports Gartner's assessment that AI and its main promoters are not ready for enterprise use, citing the rapid pace of model-makers and their lack of concern when changes break things. ### Source excerpt Model-makers move too fast and don't care when they break things ## How to answer the identity questions on an enterprise security questionnaire DevFeed: [How to answer the identity questions on an enterprise security questionnaire]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [Security](), [Single sign-on (SSO)](), [Provisioning](), [OpenID connect (OIDC)](), [App]() Tags: [configuration](), [enterprise](), [how-to](), [identity](), [integration](), [oidc](), [okta](), [provisioning](), [saml](), [security](), [sign-in](), [sso]() ### AI overview A practical guide to answering identity-related questions in enterprise security questionnaires. It explains what reviewers are really assessing behind SAML single sign-on and SCIM provisioning questions, including implementation details such as per-organization configuration, certificate rotation, and reliable deprovisioning. ### Source excerpt Every line has a literal answer and a real question behind it. Here is what the buyer is actually checking, which answers you can buy, and the three you cannot fake. ## AuthKit vs Better Auth for B2B SaaS DevFeed: [AuthKit vs Better Auth for B2B SaaS]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-14T00:00:00Z Content type: comparison Language: en Sources: [WorkOS Blog]() Topics: [Software as a service](), [Single sign-on (SSO)](), [Security](), [log management](), [threat detection](), [Frameworks](), [MFA](), [Passkeys]() Tags: [auth](), [comparison](), [enterprise](), [logs](), [mfa](), [saas](), [security](), [threat-detection](), [vercel]() ### AI overview This comparison examines AuthKit and Better Auth as platforms for B2B SaaS products selling to enterprise IT buyers. It argues that both now provide core capabilities such as SSO, SCIM, and audit logs, so the meaningful differences are provider coverage, where user lifecycle management begins, and contractual responsibility. The article also describes Better Auth's hosted infrastructure, dashboard, SIEM drain, self-service provisioning, and threat detection features, while noting its convergence with AuthKit on enterprise requirements. ### Source excerpt Both ship SSO, SCIM and audit logs now. The comparison that decides enterprise deals has moved to the long tail: provider coverage, where user lifecycle actually starts, and who is contractually on the hook. ## Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack DevFeed: [Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-12T18:24:22Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [AWS Outposts](), [Amazon Web Services](), [cloud-infrastructure](), [Network](), [Latency](), [data-processing](), [Security]() Tags: [5g](), [automation](), [aws](), [aws-outposts](), [cloud](), [compute](), [connectx](), [data-processing](), [enterprise](), [governance](), [latency](), [low-latency](), [networking](), [security]() ### AI overview AWS has generally released a second-generation single-rack AWS Outposts configuration: a self-contained 42U rack combining compute, storage, and networking with up to 2,688 vCPUs and 100 TB of Amazon EBS. The article describes its on-premises cloud compatibility, compact footprint, supported instance families, and accelerated networking options for trading floors and 5G cores. ### Source excerpt AWS has made second-generation single-rack AWS Outposts generally available, a self-contained 42U rack that puts compute, storage, and networking together with up to 2,688 vCPUs and 100 TB of Amazon EBS. It runs the same APIs, console, automation, governance policies, and security controls as the multi-rack second-generation Outposts and the parent AWS Region, so an The post Second-Gen Single-Rack AWS Outposts Puts 2,688 vCPUs and 100TB of EBS in One 42U Rack appeared first on StorageReview.com. ## Grafana 13.2 release: easier ways to query and explore your data DevFeed: [Grafana 13.2 release: easier ways to query and explore your data]() Original publisher: [Read original article]() Author: Grafana Labs Team Published: 2026-09-12T11:22:06.456390Z Content type: release Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana](), [dashboards]() Tags: [analytics](), [dashboards](), [enterprise](), [explore](), [grafana](), [grafana-cloud](), [logs](), [metrics](), [monitoring](), [observability](), [release](), [sql](), [traces]() ### AI overview Grafana 13.2 introduces generally available saved queries for Grafana Cloud and Grafana Enterprise, letting organizations store, discover, and reuse vetted queries across dashboards, Explore, and annotation queries. The release also highlights a new View panel sidebar for exploring busy panels. ### Source excerpt Grafana 13.2 is here, bringing more improvements to help you and your team explore your data and get to insights faster. In this post, we'll highlight the latest updates to saved queries, a feature that lets teams share, discover, and reuse queries to get to trusted answers faster and help new teammates get up to speed. We'll also explore how the new View panel sidebar makes exploring busy panels a breeze. If you want to read about all the latest updates in Grafana 13.2, please refer to the changelog or our What's New documentation. Saved queries: reuse trusted queries across dashboards and teams Good queries are hard-won. Writing one means knowing both the query language and your own data, like which of four similarly named metrics is the one you can trust. That knowledge usually sits with a few experienced people, or is gradually learned through exploration (increasingly AI-assisted), validation, and revision. Often teams end up rebuilding the same Grafana queries over and over, and the best ones live in pinned Slack messages or get copy-pasted from old dashboards. New team members feel it most, since their first weeks are often spent reverse-engineering existing dashboards just to work out how to ask a question of their own. The query history in Grafana Explore helps, keeping a couple of weeks of your own queries and letting you "star" the keepers. It's private to you, though. Until recently, there hasn't been a built-in way to take a query you trust and put it somewhere your whole organization can find it. How teams use saved queries We built saved queries, which is now generally available in Grafana Cloud and Grafana Enterprise, to address this challenge by providing a shared query library for your organization. When you write a query worth keeping, you can save it with a title, description, and tags. Saving works from dashboard panels, Explore, and annotation queries. This means teammates who don't know PromQL or SQL can still build dashboards from queries tha ## How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons DevFeed: [How to scale Alloy as a central telemetry gateway: capacity planning, load testing, and production lessons]() Original publisher: [Read original article]() Author: Fatjon Nebiu Published: 2026-09-12T11:22:06.456390Z Content type: tutorial Language: en Sources: [Grafana Labs blog on Grafana Labs]() Topics: [Grafana]() Tags: [analytics](), [auth](), [best-practices](), [cloud](), [cost](), [data](), [deployment](), [enterprise](), [grafana](), [grafana-alloy](), [grafana-cloud](), [how-to](), [kubernetes](), [logs](), [metrics](), [monitoring](), [observability](), [opentelemetry](), [platform](), [production](), [prometheus](), [scale](), [telemetry](), [testing](), [traces]() ### AI overview A practical guide to scaling Grafana Alloy as a centralized telemetry gateway. It covers capacity planning, load testing, and production considerations for collecting metrics, logs, and traces and forwarding them to Grafana Cloud. ### Source excerpt Running Alloy as a single-instance sidecar is simple. Running it as a centralized gateway that absorbs the full telemetry stream of an enterprise platform--tens of millions of active series, terabytes of logs per day, and tens of thousands of trace spans per second--is a different challenge altogether. To get it right, you need deliberate capacity planning, honest load testing, and a monitoring setup that doesn't rely on the very thing you're testing. As part of the Professional Services team here at Grafana Labs, we've seen this firsthand working with customers. In this post, we'll walk you through the best practices we follow to help them find success, and we'll do so using real, anonymized data from a recent engagement. We'll cover how we sized and load tested a production Alloy central collector deployment on Kubernetes, what the numbers looked like under real stress, and how the cluster behaves today handling the full production telemetry workload for a large enterprise platform. By the end, you should have a better sense for how you can create your own central gateway for collecting telemetry in Grafana Cloud. Why a central gateway? Before diving into numbers, it's worth explaining the pattern. In a central gateway setup, all telemetry from application teams--metrics, logs, and traces--flows to a shared Alloy fleet via OTLP or native Prometheus/Loki write protocols. Alloy buffers, processes, batches, and forwards everything to Grafana Cloud. This gives you several things that per-team sidecar deployments struggle to provide: A single control plane: Auth, rate limiting, and routing in one place so application teams don't need to manage Grafana Cloud credentials Centralized buffering: Ensure a transient Grafana Cloud slowdown doesn't immediately cause data loss at the source Cost visibility: Configure the gateway to only accept telemetry data containing the label or attribute that is mandatory for cost-attribution Protocol normalization: Send OTLP, Prometheus Remote ## CrowdStrike Announces Agentic Identity Provider DevFeed: [CrowdStrike Announces Agentic Identity Provider]() Original publisher: [Read original article]() Author: Ryan Terry Published: 2026-09-12T11:17:51.295154Z Content type: release Language: en Sources: [Blog]() Topics: [AI Agent](), [Next-Gen Identity Security](), [Authorization](), [Security](), [Authentication]() Tags: [2026](), [agentic](), [ai](), [ai-agent](), [api-keys](), [applications](), [authentication](), [authorization](), [cloud-infrastructure](), [code](), [enterprise](), [identity](), [identity-control](), [next-gen-identity-security](), [saas](), [security]() ### AI overview CrowdStrike announces Agentic Identity Provider, a capability in CrowdStrike Falcon Next-Gen Identity Security that gives AI agents trusted identities and continuously controls their access according to real-time security and business context. The announcement also covers expanded privileged access across SaaS applications, endpoints, code repositories, and cloud infrastructure. ### Source excerpt CrowdStrike gives every AI agent a trusted identity and controls their access based on real-time context, and expands modern privileged access. ## Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA DevFeed: [Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA]() Original publisher: [Read original article]() Author: Alex Martin; Dima Melnyk Published: 2026-09-12T11:04:33.891311Z Content type: release Language: en Sources: [Google Developers Blog]() Topics: [ai observability](), [SDKs](), [ci]() Tags: [agent](), [agents](), [ci](), [cli](), [development](), [enterprise](), [evaluation](), [experiments](), [gemini](), [llm](), [metrics](), [model](), [platform](), [production](), [telemetry](), [testing](), [traces]() ### AI overview Gemini Enterprise Agent Platform's evaluation service is generally available. It provides consistent evaluation of agents and models across local experiments and production traffic, with pre-built metrics, adaptive rubrics, custom metrics, simulators, and workflow integrations. ### Source excerpt Agent Platform's evaluation service is now generally available, providing developers with a unified engine to measure agent quality consistently across local development experiments and live production traffic. You can evaluate agents using over 20 pre-built metrics, DeepMind-backed adaptive rubrics, or custom code-based and LLM-as-a-judge metrics stored in a centralized, versioned registry. The service integrates directly into existing workflows via the Agent Platform SDK, agents-cli, and ADK, offering built-in user and environment simulators to automate complex multi-turn testing and streamline CI pipelines. [Next page]()