Browse All Azure Content (515)

robertocarli introduces Microsoft Web IQ, an Azure-billed web search and grounding API designed for AI agents and RAG apps, and explains how it delivers fresh, citable web evidence with low latency using Bing’s web substrate, Harrier embeddings, DiskANN indexing, and structured passage-level results.

Wiliam_Rosa explains how Azure Databricks cross-engine ABAC enforces Unity Catalog row filters, column masks, and ABAC policies for external Apache Spark reads by routing queries through a Databricks-managed serverless enforcement layer, including required metastore settings, grants, Spark configuration, known limitations, and cost tracking.

sbaynes interviews Mercedes-AMG PETRONAS F1 lead race strategist Joey McMillan on how the team uses Microsoft Azure—especially Azure Batch—to run large-scale race simulations quickly enough to influence live, race-day strategy decisions under changing weather and track conditions.

Michael Scovetta shares joint Microsoft and NVIDIA guidance for managing risks when deploying open-weight AI models in enterprise environments, including a shared risk taxonomy, how to use evaluations as evidence, and where safeguards and responsibilities shift across model, runtime, and application layers.

abhilashasr threat-models Azure MCP Server for AI-assisted incident response, focusing on how Entra ID, Azure RBAC, Key Vault, Policy, and monitoring controls prevent an assistant from turning diagnostics into unintended production changes. It also includes a read-only demo connecting GitHub Copilot in VS Code to Azure Monitor and Log Analytics.

John Savill shares a short Azure Weekly Update for 16 October 2026, covering a handful of platform changes across resiliency, compute, networking, storage, and Azure’s AI hardware/model announcements.

RicardoDuncan rounds up the latest SQL developer tooling updates for VS Code, including new MSSQL extension features, GitHub Copilot schema-aware agent workflows, and Microsoft SQL “skills” for assessing and migrating SQL Server to Azure SQL, plus driver releases aligned with SQL Server 2025.

Rafia Aqil lays out three practical patterns for moving Azure Cosmos DB for NoSQL workloads across regions, with a clear decision framework and cutover guidance. The post includes Azure CLI commands for PITR restore and preview online container copy, plus validation steps and trade-offs around downtime, capacity, and multi-region design.

Nick Brady’s September 2026 roundup covers Microsoft Foundry updates across models, agents, observability, and tooling—highlighting GPT-6 family GA, Routines and Tool Search GA, Insights preview, Model Router preview controls, and Foundry Local 2.1 bring-your-own-model support with ONNX Runtime GenAI.

HilaryWang announces general availability of Automatic Zone Balance for Azure Virtual Machine Scale Sets, explaining how Azure continuously rebalances instances across availability zones with safeguards like create-before-delete and health checks, and how to enable it via portal, CLI, PowerShell, or REST API.

Microsoft Developer presents a Data Exposed session on Fabric Database Hub, focusing on bringing Azure SQL, SQL Server, Cosmos DB, PostgreSQL, and Fabric databases into a single place for discovery, estate-level visibility, governance, and AI-assisted insights to prioritize actions.

Mike Hulme explains how Azure is evolving its cloud-native platform to better support AI workloads and AI agents, focusing on coordinated compute/storage/networking, production-ready open-source foundations (notably Kubernetes), cost governance with FinOps, and “agentic” operations to improve reliability and efficiency at scale.

Lim Ai Leen reports on Bar-Ilan University’s pilot of an AI agent in Moodle that helps students search, summarize, and quiz themselves on course materials using natural language, built with Azure AI Services and Microsoft Copilot Studio and run in the university’s Azure environment.

Aaron Stark talks with Haishi Bai about Adaptive Applications, a capability-based architectural approach for building portable apps that can run across Azure, sovereign environments, edge, and on-premises. They focus on decoupling application needs (AI, data, identity) from the underlying services to meet compliance, residency, and continuity requirements.

Eduard van Valkenburg explains how Microsoft Agent Framework for Python models and uses vector stores for embeddings, vector search, and agent context. The post shows how to define typed records, index and query data, expose vector search as an agent tool, and keep conversation history searchable via context providers across multiple database integrations.

John Edward explains the planner-executor loop as a practical architecture for building agentic systems on Azure, focusing on how to separate planning from execution, validate model outputs, and run tool calls safely. The article maps the pattern to Azure services and highlights reliability, observability, and access-control decisions needed for production use.

dchelupati explains how to scale Azure SRE Agent from a single team to enterprise-wide use, focusing on access control, guardrails, auditing, multi-agent topologies, and “agent as code” practices for repeatable deployment and governance.

dchelupati shares a practical “start small” adoption guide for Azure SRE Agent, focused on picking a first operational use case, connecting only the required context (telemetry, repos, runbooks), and expanding into repeatable workflows with the right triggers and permissions.

Meagan McCrory explains that Extended Security Updates (ESU) for Windows Server 2012 and 2012 R2 ended on October 13, 2026, and outlines practical next steps to reduce security and compliance risk by upgrading, migrating to Azure, or modernizing workloads.

Sandeep Sen announces the stable Azure MCP Server 3.0 release and explains what’s new for bringing Azure operational tooling into MCP-compatible AI agent workflows, including Terraform and Advisor tooling, more predictable client integration, and security-focused defaults for resource creation.

Jakub Oleksy reports on GitHub’s September 2026 reliability work and incidents, including database pressure reductions, overload controls, added headroom for GitHub Actions, expanded failure containment for pull requests, and progress moving production workloads and traffic to Azure.

paulyuk shows how the Azure SRE Agent canvas fits into GitHub Copilot (App and Copilot CLI) so on-call engineers can validate an agent’s incident diagnosis with their own telemetry, queries, and access controls, then share a reviewed update back to the team.

Microsoft Developer demonstrates an end-to-end workflow for building and shipping a hosted AI agent from the terminal using GitHub Copilot CLI and Microsoft Foundry, including planning, resource creation, model selection, evaluation-driven improvements, and a secure Azure Functions proxy to connect the agent to a website chat experience.

Joe Filcik and Colin Murphy explain how Azure Content Understanding uses grounding and field-level confidence to make document extraction results inspectable and actionable, then show how to calibrate confidence thresholds (per document type and per field) to route work in human-in-the-loop review workflows.

vinilv shows how to build a small, two-node kubeadm Kubernetes cluster on Azure using an N-series GPU VM, then validate GPU scheduling with the NVIDIA device plugin and a PyTorch CUDA test Job. The guide focuses on a transparent dev sandbox for debugging GPU workloads rather than a production-ready design.

Microsoft Threat Intelligence and Microsoft Digital Crimes Unit detail how a Chinese-language phishing-as-a-service ecosystem (Storm-2990 and Storm-3119) monetizes compromised Microsoft 365 and Azure for Education tenants, then provide practical mitigation guidance and Microsoft Defender detections to help organizations reduce phishing-driven account takeover risk.

sbaynes outlines how Microsoft is scaling Azure’s AI infrastructure end-to-end, covering custom silicon (Maia and Cobalt), networking for large AI clusters, and datacenter power and cooling redesigns. The post also highlights hardware-rooted security work like Azure Integrated HSM and the open-source Tessera Integrated HSM initiative.

sbaynes breaks down the Azure Maia 300 system architecture and the design choices Microsoft made to optimize cloud-scale LLM inference, focusing on dataflow, memory locality, low-precision formats, and rack-to-superpod networking to improve tokens-per-watt and tokens-per-dollar.

mk_sunitha introduces Fabric App functions (preview), a TypeScript-based serverless capability for Fabric Apps that lets builders run secure server-side logic, use application authentication to access downstream resources, and manage secrets safely. The post also shows how to scaffold, debug, and deploy functions, including using GitHub Copilot in VS Code.

Mark Russinovich and Saurabh Dighe announce Project Tessera, an open-source evolution of Azure Integrated HSM, and explain how its integrated HSM model and protocols aim to improve key protection, interoperability, and post-quantum readiness—plus how the same firmware stack can extend into confidential computing environments.

mariozeng explains how Microsoft is rethinking power delivery for Azure AI infrastructure, focusing on grid-to-chip efficiency for high-density GPU systems. The post covers LVDC and 800VDC distribution, disaggregated rack power (Mt. Diablo), solid-state transformers with energy storage, and server-to-silicon power delivery design.

Bryankel summarizes six open-source hardware security innovations announced at the Open Compute Project Global Summit, covering workload-attached HSM design (Tessera), integrated TPM in the BMC, whole-system attestation (Composite EAT), hierarchical bare-metal management, streaming-boot recovery, and Caliptra 2.2 silicon hardening with post-quantum crypto support.

Ali Zaman explains Microsoft Discovery and how it coordinates specialized AI agents to help scientists run an evidence-driven research loop, using battery design as the example and focusing on why search and summarization aren’t enough for real R&D workflows.

Jack Tracey explains that Azure Verified Modules (AVM) has moved from a community-led effort into Azure engineering, with dedicated engineers maintaining the specs, framework, and selected modules. The post outlines changes to AVM’s support statement and introduces a tiered module ownership model planned for early 2027.

Chu Lahlou explains how Azure Content Understanding prebuilt analyzers go beyond a schema by packaging domain knowledge with grounding and confidence signals, so document extraction results are safer to automate. The post also shares benchmark results for Advanced Contextualization in Content Understanding 2.0 Preview, covering quality, modeled cost, and latency.

Microsoft Developer highlights updates across .NET Aspire, Azure Virtual Machines, and SQL Server, focusing on what’s new in Aspire 13.6 for distributed app debugging, Azure VM lifecycle guidance changes, and running SQL Server on Azure Local when cloud-only isn’t an option.

sbaynes announces Microsoft’s plan to scale the Azure Maia accelerator system to multi-gigawatt deployments starting in 2027, outlining how Maia 200 is already running production inference and what architectural changes in Maia 300 target long-context, latency-sensitive, and agentic workloads.

Wuyi Weng shows how to run Qwen3.8-Flash-Next across two H100 GPUs on an Azure Spot VM, then instrument and troubleshoot it with OpenTelemetry, Application Insights/Azure Monitor, and Grafana. The post focuses on KV-cache behavior under agentic load, dashboard pitfalls, and a vLLM KV-cache FP8 setting that improves concurrency and time-to-first-token.

GisliGudmundsson shares a reference pattern for running Azure AI Foundry workloads inside a secure Azure landing zone, using private endpoints and private DNS zones, disabling local auth in favor of managed identity, and placing Azure API Management in front to enforce token limits and emit token usage metrics.

Tamir Dresher explains where Durable Task (and Durable Task Scheduler) actually helps in production: coordinating long-running workflows that need durable waits, replay, and recovery. Using five concrete scenarios—including Azure tenant provisioning and an AI-assisted incident workflow—he shows what durability solves and what your app still must own.