# AI Operations Published articles for AI Operations. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent DevFeed: [Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent]() Original publisher: [Read original article]() Author: TNS Staff Published: 2026-09-15T16:21:45Z Content type: article Language: en Sources: [The New Stack]() Topics: [SRE](), [incident](), [Azure](), [observability](), [DevOps](), [Redis]() Tags: [agent](), [ai-agents](), [ai-operations](), [azure](), [code-review](), [deployment](), [devops](), [incident](), [microsoft-azure](), [monitoring](), [observability](), [post](), [redis](), [sponsor-microsoft-azure](), [sponsored](), [sponsored-post](), [sre](), [telemetry]() ### AI overview The article describes Azure SRE Agent as a system that analyzes telemetry, correlates deployment and monitoring data, investigates incidents, identifies root causes, recommends or prepares fixes, and supports mitigation and other operational tasks under human approval. It cites examples involving Microsoft service teams and InEight, including a recommendation to scale Redis. ### Source excerpt What if engineers could spend their time building and optimizing systems rather than maintaining them? It's 3 a.m., and the The post Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent appeared first on The New Stack. ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots DevFeed: [Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-10T17:01:36Z Content type: news Language: en Sources: [The New Stack]() Topics: [Multi-tenancy](), [AI Platform](), [GPU](), [benchmarking]() Tags: [ai](), [ai-operations](), [ai-platform](), [gpu](), [multi-tenancy](), [observability](), [platform-engineering]() ### AI overview Red Hat AI 3.5 adds multi-tenancy, isolation, priority-aware GPU scheduling, safety benchmarking, observability, and GPU resource management for enterprise AI workloads. ### Source excerpt Red Hat released Red Hat AI 3.5 this week, a move designed to let software engineering teams run AI with The post Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots appeared first on The New Stack. ## Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. DevFeed: [Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size.]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-10T09:00:43Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [ai-operations](), [cuopt](), [deployment](), [nemotron](), [nvidia](), [partnership](), [supply-chain](), [training]() ### AI overview Nvidia and Palantir are fine-tuning Nemotron for Nvidia supply-chain decisions as a sovereign-AI deployment. The companies say the 30B-parameter model outperforms a model 18 times larger and plan to extend lessons from the deployment to other sectors. ### Source excerpt Nvidia and Palantir announced Thursday that they're working together to bring "sovereign AI to critical supply chains," kicking off initially The post Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. appeared first on The New Stack. ## How much control should AI get? A CISO roundtable takes on SOC autonomy DevFeed: [How much control should AI get? A CISO roundtable takes on SOC autonomy]() Original publisher: [Read original article]() Author: Carly Page Published: 2026-09-09T15:38:58Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI Bots]() Tags: [agents](), [ai](), [ai-agents](), [ai-operations](), [autonomous](), [security](), [soc](), [sponsored-webinar](), [webinar]() ### AI overview The article examines how security operations centers may use AI agents to investigate alerts and the limits organizations should place on autonomous response actions. It emphasizes human oversight, reversibility, and the changing role of SOC analysts. ### Source excerpt Security operations centers have struggled with alerts for years, and AI agents offer a new way to tackle it: Enable The post How much control should AI get? A CISO roundtable takes on SOC autonomy appeared first on The New Stack. ## How NVIDIA scales expertise with ChatGPT Work DevFeed: [How NVIDIA scales expertise with ChatGPT Work]() Original publisher: [Read original article]() Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Nvidia](), [Artificial Intelligence]() Tags: [ai](), [ai-operations](), [chatgpt](), [events](), [global](), [nvidia](), [operations](), [partners](), [reduce](), [scale](), [tools](), [work](), [workflow](), [workflows]() ### AI overview NVIDIA uses ChatGPT Work to automate recurring operational workflows, analyze external AI developments, and help teams act on information more efficiently. A GTC planning workflow saves about 16 hours per week and can be adapted and shared across regions. ### Source excerpt NVIDIA teams use ChatGPT Work to reduce manual tasks, connect fast-moving signals, and scale successful workflows globally. ## CyberAgent moves faster with ChatGPT Enterprise and Codex DevFeed: [CyberAgent moves faster with ChatGPT Enterprise and Codex]() Original publisher: [Read original article]() Published: 2026-04-09T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Artificial Intelligence](), [codex](), [AI Strategy](), [Security](), [Code review]() Tags: [ai](), [ai-adoption](), [ai-operations](), [ai-tools](), [business](), [chatgpt](), [codex](), [enterprise](), [security](), [technology](), [work]() ### AI overview CyberAgent uses ChatGPT Enterprise and Codex as foundational tools for secure AI adoption across advertising, media, and gaming. The article describes organizational investment, governance, everyday uses such as research and drafting, and Codex-assisted design discussions, code review, and documentation. ### Source excerpt CyberAgent uses ChatGPT Enterprise and Codex to securely scale AI adoption, improve quality, and accelerate decisions across advertising, media, and gaming. ## What is AI Infrastructure? DevFeed: [What is AI Infrastructure?]() Original publisher: [Read original article]() Published: 2025-03-11T22:11:25Z Content type: article Language: en Sources: [Learn on Tailscale]() Topics: [AI Infrastructure](), [AI Platform](), [AI Development](), [Deployment]() Tags: [ai-infrastructure](), [ai-operations](), [deployment](), [infrastructure](), [software]() ### AI overview The article explains that AI infrastructure combines specialized hardware, software, networking, scalable storage, and data-management tools to support AI workloads. It also introduces strategies for improving processing and workflows while managing complexity and performance. ### Source excerpt AI infrastructure requires more than just high-speed networking solutions for rapid data transfer. Here we explore the advanced software tools, components and strategies advanced AI operations need to run efficiently.