Connect Azure Functions to more services with managed connectors
Azure Functions can already connect to many Azure services through triggers and bindings. With managed connectors, your functions can access about 1,700 connectors across services such as Microsoft 365, Microsoft Teams, Dataverse, SharePoint, OneDrive, and third-party systems. Connector triggers deliver events from these services to your function, while typed connector clients let your code take actions against them. You get this broader integration surface without writing the webhook registration code or managing the OAuth tokens required to connect to each service. Focus on your function's business logic and let Azure Connector Namespace handles the connection. Azure Functions integration with Connector Namespace is currently in public preview. It supports .NET isolated, Python, and Node.js. Review the managed connectors overview for current language, hosting plan, and regional availability. To demonstrate how connector triggers and actions work together, this article follows a .NET sample that automates RFP intake across SharePoint, Azure Content Understanding, and Teams. From an uploaded RFP to Teams notification Consider an organization that receives requests for proposals (RFPs) in a shared SharePoint document library. Someone must read each document, identify the requested capabilities, determine which subject-matter experts should respond, and notify the right team. The automated RFP intake sample turns that process into an event-driven workflow: A customer uploads an RFP to a SharePoint document library. A SharePoint connector trigger invokes an Azure Function when the file is created. The function uses a typed SharePoint connector client to retrieve the file contents. Azure Content Understanding extracts the document’s text and layout. The function applies deterministic rules to identify the customer, required capabilities, and recommended subject-matter experts. The function uses a typed Teams connector client to post the results as an Adaptive Card in a channel. Connector Namespace manages the SharePoint and Teams connections. The function controls file processing, document analysis, routing rules, error handling, and notification content How the sample works The .NET sample demonstrates both parts of the connector programming model: a connector trigger receives an event from SharePoint, and typed connector clients provided by the Connector SDKs to perform actions against SharePoint and Teams. The function starts when the SharePoint When a file is created trigger detects a new RFP. It declares the trigger using the ConnectorTrigger attribute and receives a typed payload containing the file’s properties: [Function("OnNewFile")] public async Task OnNewFile( [ConnectorTrigger] SharePointOnlineOnNewFileItemsTriggerPayload payload, CancellationToken cancellationToken) { // Process the newly uploaded file. } Because the trigger provides file properties rather than its contents, the function uses a typed SharePoint client to retrieve the document: byte[] response = await _sharePoint.GetFileContentAsync( Uri.EscapeDataString(siteAddress), fileIdentifier, cancellationToken: cancellationToken); byte[] document = SharePointFileContent.Decode(response); The SharePoint and Teams clients are registered through dependency injection. Each client uses the runtime URL of its Connector Namespace connection and authenticates with DefaultAzureCredential: services.AddSingleton( new SharePointOnlineClient( new Uri(sharePointRuntimeUrl), credential)); services.AddSingleton( new TeamsClient( new Uri(teamsRuntimeUrl), credential)); The function sends the document to Content Understanding’s prebuilt-layout analyzer, which extracts its text and structure. It then applies deterministic C# rules to identify the customer and required capabilities and map those capabilities to predefined subject-matter expert roles. Finally, the function creates an Adaptive Card containing the results and posts it to the configured Teams channel with the typed Teams client: await _teams.PostCardToConversationAsync( postAs, postIn, request, cancellationToken); Connector Namespace handles the SharePoint and Teams connections, while the function controls the document analysis, routing logic, error handling, and notification content. Try the sample The RFP intake sample includes the function code, Bicep infrastructure, Azure Developer CLI configuration, and supporting scripts. Its README explains how to test the workflow locally and deploy it to Azure. Common connector patterns Managed connectors are useful when a function must react to events or perform operations in external systems. Common patterns include: Event to action: React to an event in one service and take an action in another. Event to enrich to action: Retrieve additional information related to an event before acting. Event to document analysis to action: Extract text and structure from a document, apply application rules, and send the result through another connector. Event to AI to action: Analyze event data with an AI service and write the result back through a connector. Extend an existing function app: Add connector-based integrations alongside HTTP, timer, queue, Service Bus, Event Grid, or Durable Functions workloads. The RFP sample combines several of these patterns. A SharePoint event starts the workflow, a SharePoint action retrieves the document, Content Understanding extracts its contents, application code enriches the result, and a Teams action sends the notification. Closing thoughts Managed connectors extend the external systems that can trigger your functions and the services your function code can act on. This brings services such as SharePoint, Teams, Microsoft 365, and many third-party systems into the Azure Functions programming model without requiring you to build the underlying webhook and OAuth infrastructure. Choose Azure Functions with managed connectors when you want this broader integration surface in a code-first application and need custom branching, application libraries and SDKs, other Functions bindings, document or AI processing, or application-specific logic between the trigger and action. If the workload primarily orchestrates connector operations, involves little custom code, and would benefit from a visual designer, Azure Logic Apps is usually the simpler choice. Resources Documentations Overview of managed connectors in Azure Functions Azure Functions connector samples Azure Connector Namespace overview Content Understanding prebuilt-layout analyzer Connector SDK GitHub repos .NET SDK Python SDK Node.js SDK
Catalyst: Frontier stories of AI Infrastructure innovation
In the Catalyst series, we explore the transformative power of AI by showcasing visionary companies driving scientific and industry breakthroughs powered by Azure and NVIDIA. This video series spotlights how innovation ignites action and how today’s visionaries are shaping tomorrow with the power of AI and cloud innovation. Building resilience against extreme weather Tomorrow.io turns space-based data into AI-driven forecasting powered by Azure and NVIDIA at a global scale. Its high-performance AI models deliver real-time weather intelligence that helps governments and enterprises anticipate disruption, optimize operations, and act faster in the face of severe events. Expanding access in preventative healthcare Powered by Microsoft Azure and NVIDIA, Helfie uses multimodal AI to transform smartphone selfies into intelligent health screening at scale. By combining advanced models with real-time inference, it delivers rapid biometric insights that help identify risks earlier and expand access to remote and underserved communities. How AI is mapping the tree of life Powered by Azure and NVIDIA to build the world’s largest biological databases, Basecamp Research is decoding the complexity of biology to accelerate scientific breakthroughs. Building the next frontier of data Powered by Microsoft Azure and NVIDIA, Global Objects is digitizing the real world by creating high-fidelity digital twins of over 5 million physical items. These photorealistic 3D models are transforming immersive content creation across Hollywood, gaming, robotics, and cultural preservation. Changing how doctors diagnose diseases with AI Powered by Microsoft Azure and NVIDIA, Pangaea Data is closing care gaps by identifying untreated and under-treated patients across rare and hard-to-diagnose diseases faster, earlier, and more accurately. Learn more The Catalyst series explores how organizations are using AI infrastructure to tackle some of their most complex technical and business challenges, from accelerating scientific discovery to advancing autonomous systems and transforming core industries. Learn more about Catalyst. Learn more about Azure AI infrastructure.
Central pki
Has anyone implemented a centralised PKI where the Root CA is stored in Azure Key Vault and workloads can automatically obtain the Root CA when they are deployed, as well as receive updated versions when the Root CA needs to be rotated or renewed? I’m looking to implement a centralised PKI for an Azure hub-and-spoke architecture, where workloads across the spokes can automatically obtain and trust the Root CA. The workloads could include VMs, AKS, containers, App Services and Azure Functions. I’m aware that Azure Machine Configuration can be used to deploy certificates to VMs, but this is specific to VMs. What is the recommended approach for distributing the Root CA to the other Azure workload types using a consistent and automated mechanism? Ideally, I’d like the solution to be fully automated, so that new workloads receive the current Root CA during deployment and existing workloads automatically receive updated versions whenever the Root CA is rotated or renewed. I will be deploying the root ca in a key vault via terraform a project for the root ca only to update whenever is needed and then each resources to be able to get the latest root ca. Has anyone implemented something similar, or could you provide guidance on the recommended architecture and distribution mechanism?Security baseline for Microsoft Edge version 151
We are pleased to announce the enterprise-ready release of the security baseline for Microsoft Edge version 151! We have reviewed the settings in Microsoft Edge version 151 and updated our guidance with six new recommendations. We have also identified one additional setting that organizations should consider evaluating in their environments. A new Microsoft Edge security baseline package was just released to the Download Center. You can download the new package from the Security Compliance Toolkit. Enable Process Isolation (added) We are enforcing ‘Enable Process Isolation’ to help protect Microsoft Edge from unauthorized access, modification, and tampering by other applications running on the device. This setting strengthens browser process integrity and helps safeguard sensitive data used by Microsoft Edge. Organizations that encounter compatibility issues with software that depends on browser process injection should treat such configurations as exceptions requiring explicit risk acceptance and compatibility validation. Enable renderer in app container (added) We are enforcing the default and enabling ‘Enable renderer in app container’ to strengthen Microsoft Edge’s browser isolation protections by ensuring renderer processes run within the additional restrictions provided by AppContainer. This helps reduce the impact of browser-based attacks and limits the ability of exploited renderer processes to interact with system resources. Organizations that require this setting to be disabled due to incompatible software should treat such configurations as exceptions that require explicit risk acceptance. Enable the network service sandbox (added) We are enforcing the default and enabling ‘Enable the network service sandbox’ to ensure Microsoft Edge network-facing processes operate within sandbox isolation boundaries that help reduce the impact of exploitation and limit access to system resources. Because disabling the network service sandbox weakens a core browser security protection, organizations should treat any requirement to disable this setting as an exception scenario requiring explicit risk acceptance and compatibility validation. Configure browser process code integrity guard (added) We are enabling ‘Configure browser process code integrity guard setting’ with a value of ‘Enable code integrity guard enforcement in the browser process’. The setting strengthens protections against unauthorized code injection into Microsoft Edge browser processes. Some enterprise applications, extensions, accessibility tools, or security products may still rely on legacy injection techniques and require compatibility validation. We encourage organizations to fully test this setting in their environments and work with vendors to identify or remediate incompatible software. Enable Application Bound Encryption (added) We are enabling ‘Enable Application Bound Encryption’ to strengthen protections for browser-stored credentials, authentication tokens, and other sensitive data by binding encryption more closely to the browser process. This helps reduce the risk of unauthorized access to protected browser data by malware or other untrusted software. Because disabling this setting weakens an important protection boundary, organizations should treat any requirement to disable the feature as an exception requiring explicit risk review. Enhance the security state in Microsoft Edge (added) We are enabling ‘Enhance the security state in Microsoft Edge’ and configuring the setting to Balanced to provide additional protection against modern web-based attacks while maintaining compatibility for most enterprise users. In Balanced mode, Microsoft Edge applies additional mitigations such as disabling just-in-time (JIT) JavaScript compilation and enabling added operating system protections on processes used to load sites that users do not frequently visit, helping reduce the risk of memory-related vulnerabilities. Because these protections can introduce compatibility issues for some applications or workflows, organizations should validate critical business sites and applications during their normal testing process prior to broad deployment and configure exceptions as needed. Additional details can be found here. Configure Automatic HTTPS (worth considering) We previously released a blog discussing a new feature called Automatic HTTPS. This setting can automatically switch your connections to websites from HTTP to HTTPS on sites that are highly likely to support the more secure protocol. This option helps ensure that users' network traffic is more secure and less susceptible to SSL stripping attacks. The best part, the end user doesn’t get prompted, it just works! This new feature has two configuration options: Navigations delivered over HTTP are switched to HTTPS’ (UpgradeCapableDomains) and ‘All navigation delivered over HTTP are switched to HTTPS’ (AlwaysUpgrade). There are trade-offs for each configuration: the UpgradeCapableDomains option only upgrades to HTTPS if Microsoft believes the site is likely to work over HTTPS, and this setting is unavailable if you’ve disabled the ComponentUpdatesEnabled policy. The more secure AlwaysUpgrade option unconditionally updates all HTTP requests to HTTPS, which will result in a user-visible error page if the target site does not support HTTPS. We encourage organizations to consider implementing and testing Automatic HTTPS within their environment. Collectively, these changes continue our focus on strengthening browser isolation, sandboxing, process integrity, and protection of sensitive browser data while balancing enterprise compatibility requirements. Microsoft Edge version 151 introduced 4 new computer and user settings. We have included a spreadsheet listing the new settings in the release to make it easier for you to find them. As a friendly reminder, all available settings for Microsoft Edge are documented here, and all available settings for Microsoft Edge Update are documented here. Please continue to give us feedback through the Security Baseline Community or in comments on this post.
ExpressRoute Gateway Microsoft initiated migration
Objective The backend migration process is an automated upgrade performed by Microsoft to ensure your ExpressRoute gateways use the Standard IP SKU. This migration enhances gateway reliability and availability while maintaining service continuity. You receive notifications about scheduled maintenance windows and have options to control the migration timeline. For guidance on upgrading Basic SKU public IP addresses for other networking services, see Upgrading Basic to Standard SKU. Important: As of September 30, 2025, Basic SKU public IPs are retired. For more information, see the official announcement. You can initiate the ExpressRoute gateway migration yourself at a time that best suits your business needs, before the Microsoft team performs the migration on your behalf. This gives you control over the migration timing. Please use the ExpressRoute Gateway Migration Tool to migrate your gateway Public IP to Standard SKU. This tool provides a guided workflow in the Azure portal and PowerShell, enabling a smooth migration with minimal service disruption. Backend migration overview The backend migration is scheduled during your preferred maintenance window. During this time, the Microsoft team performs the migration with minimal disruption. You don’t need to take any actions. The process includes the following steps: Deploy new gateway: Azure provisions a second virtual network gateway in the same GatewaySubnet alongside your existing gateway. Microsoft automatically assigns a new Standard SKU public IP address to this gateway. Transfer configuration: The process copies all existing configurations (connections, settings, routes) from the old gateway. Both gateways run in parallel during the transition to minimize downtime. You may experience brief connectivity interruptions may occur. Clean up resources: After migration completes successfully and passes validation, Azure removes the old gateway and its associated connections. The new gateway includes a tag CreatedBy: GatewayMigrationByService to indicate it was created through the automated backend migration Important: To ensure a smooth backend migration, avoid making non-critical changes to your gateway resources or connected circuits during the migration process. If modifications are absolutely required, you can choose (after the Migrate stage complete) to either commit or abort the migration and make your changes. Backend process details This section provides an overview of the Azure portal experience during backend migration for an existing ExpressRoute gateway. It explains what to expect at each stage and what you see in the Azure portal as the migration progresses. To reduce risk and ensure service continuity, the process performs validation checks before and after every phase. The backend migration follows four key stages: Validate: Checks that your gateway and connected resources meet all migration requirements for the Basic to Standard public IP migration. Prepare: Deploys the new gateway with Standard IP SKU alongside your existing gateway. Migrate: Cuts over traffic from the old gateway to the new gateway with a Standard public IP. Commit or abort: Finalizes the public IP SKU migration by removing the old gateway or reverts to the old gateway if needed. These stages mirror the Gateway migration tool process, ensuring consistency across both migration approaches. The Azure resource group RGA serves as a logical container that displays all associated resources as the process updates, creates, or removes them. Before the migration begins, RGA contains the following resources: This image uses an example ExpressRoute gateway named ERGW-A with two connections (Conn-A and LAconn) in the resource group RGA. Portal walkthrough Before the backend migration starts, a banner appears in the Overview blade of the ExpressRoute gateway. It notifies you that the gateway uses the deprecated Basic IP SKU and will undergo backend migration between March 7, 2026, and April 30, 2026: Validate stage Once you start the migration, the banner in your gateway’s Overview page updates to indicate that migration is currently in progress. In this initial stage, all resources are checked to ensure they are in a Passed state. If any prerequisites aren't met, validation fails and the Azure team doesn't proceed with the migration to avoid traffic disruptions. No resources are created or modified in this stage. After the validation phase completes successfully, a notification appears indicating that validation passed and the migration can proceed to the Prepare stage. Prepare stage In this stage, the backend process provisions a new virtual network gateway in the same region and SKU type as the existing gateway. Azure automatically assigns a new public IP address and re-establishes all connections. This preparation step typically takes up to 45 minutes. To indicate that the new gateway is created by migration, the backend mechanism appends _migrate to the original gateway name. During this phase, the existing gateway is locked to prevent configuration changes, but you retain the option to abort the migration, which deletes the newly created gateway and its connections. After the Prepare stage starts, a notification appears showing that new resources are being deployed to the resource group: Deployment status In the resource group RGA, under Settings → Deployments, you can view the status of all newly deployed resources as part of the backend migration process. In the resource group RGA under the Activity Log blade, you can see events related to the Prepare stage. These events are initiated by GatewayRP, which indicates they are part of the backend process: Deployment verification After the Prepare stage completes, you can verify the deployment details in the resource group RGA under Settings > Deployments. This section lists all components created as part of the backend migration workflow. The new gateway ERGW-A_migrate is deployed successfully along with its corresponding connections: Conn-A_migrate and LAconn_migrate. Gateway tag The newly created gateway ERGW-A_migrate includes the tag CreatedBy: GatewayMigrationByService, which indicates it was provisioned by the backend migration process. Migrate stage After the Prepare stage finishes, the backend process starts the Migrate stage. During this stage, the process switches traffic from the existing gateway ERGW-A to the new gateway ERGW-A_migrate. Gateway ERGW-A_migrate: Old gateway (ERGW-A) handles traffic: After the backend team initiates the traffic migration, the process switches traffic from the old gateway to the new gateway. This step can take up to 15 minutes and might cause brief connectivity interruptions. New gateway (ERGW-A_migrate) handles traffic: Commit stage After migration, the Azure team monitors connectivity for 15 days to ensure everything is functioning as expected. The banner automatically updates to indicate completion of migration: During this validation period, you can’t modify resources associated with both the old and new gateways. To resume normal CRUD operations without waiting 15 days, you have two options: Commit: Finalize the migration and unlock resources. Abort: Revert to the old gateway, which deletes the new gateway and its connections. To initiate Commit before the 15-day window ends, type yes and select Commit in the portal. When the commit is initiated from the backend, you will see “Committing migration. The operation may take some time to complete.” The old gateway and its connections are deleted. The event shows as initiated by GatewayRP in the activity logs. After old connections are deleted, the old gateway gets deleted. Finally, the resource group RGA contains only resources only related to the migrated gateway ERGW-A_migrate: The ExpressRoute Gateway migration from Basic to Standard Public IP SKU is now complete. Frequently asked questions How long will Microsoft team wait before committing to the new gateway? The Microsoft team waits around 15 days after migration to allow you time to validate connectivity and ensure all requirements are met. You can commit at any time during this 15-day period. What is the traffic impact during migration? Is there packet loss or routing disruption? Traffic is rerouted seamlessly during migration. Under normal conditions, no packet loss or routing disruption is expected. Brief connectivity interruptions (typically less than 1 minute) might occur during the traffic cutover phase. Can we make any changes to ExpressRoute Gateway deployment during the migration? Avoid making non-critical changes to the deployment (gateway resources, connected circuits, etc.). If modifications are absolutely required, you have the option (after the Migrate stage) to either commit or abort the migration.Expanding access to specialized GPU infrastructure through Dapple and Azure
Enterprise AI has entered a new phase. For much of the past decade, the challenge was building enough compute for machine learning. Today, organizations need platforms for foundation model training, fine-tuning, large-scale inference, agentic systems, and enterprise AI. As these workloads grow in scale and sophistication, the infrastructure supporting them is undergoing a fundamental transformation. The defining characteristic of modern AI infrastructure is no longer compute alone. It is topology. Training state-of-the-art AI models requires coordinated communication across hundreds or thousands of accelerators. Performance increasingly depends on how GPUs are interconnected, how data moves across the network fabric, and how efficiently distributed systems synchronize at scale—making network architecture as critical as compute performance. This shift is driving purpose-built AI infrastructure built around tightly coupled GPU fabrics, accelerated networking back-end interconnects, specialized storage architectures, and operational models that treat physical topology as a first-class concern. The challenge is turning this infrastructure into AI production without creating a separate operational domain or adding complexity for platform teams, application developers, and infrastructure operators. A new approach to scaling purpose-built AI infrastructure Microsoft is expanding access to purpose-built GPU capacity through infrastructure partners, including Dapple. Customers can use this specialized infrastructure within the Azure environment they already rely on to operate and govern their workloads. Through our collaboration, Dapple’s dedicated, topology-aware GPU infrastructure (Dapple Private AI Cloud | Microsoft Marketplace) is integrated into an Azure-native operating model that preserves Azure governance, security, and operational consistency. This model for purpose-built AI infrastructure can benefit any organization running compute-intensive AI workloads, including large-scale pre-training, fine-tuning, and distributed inference. Why purpose-built AI infrastructure matters AI workloads place demands on infrastructure that differ fundamentally from traditional enterprise applications. Conventional cloud architectures support a broad range of workloads across diverse infrastructure pools. Large-scale AI training environments, by contrast, depend on highly deterministic infrastructure characteristics, including GPU proximity, network topology, bandwidth consistency, and low-latency communication across thousands of accelerators. As model sizes continue to grow, the efficiency of the underlying GPU fabric becomes a critical determinant of training performance, infrastructure utilization, and overall time-to-results. For this reason, many AI deployments are increasingly built around purpose-designed GPU clusters featuring: Fixed infrastructure topology Dedicated InfiniBand fabrics High-bandwidth, low-latency communication paths Topology-optimized storage architectures Rack-scale and cluster-scale operational boundaries These systems are designed as integrated AI platforms rather than collections of independent compute nodes. Purpose-built GPU infrastructure is organized around validated cluster blocks: groups of compute, network, and related resources managed as a single topology, readiness, and maintenance boundary within InfiniBand fabric domains. Kubernetes nodes remain workload-scheduling objects, while cluster blocks represent the infrastructure unit on which distributed-job performance and availability depend. Figure 1 highlights an important distinction between traditional cloud infrastructure and modern AI systems. In conventional environments, compute resources are often treated as interchangeable units. Distributed AI workloads operate differently. GPU placement, rack boundaries, InfiniBand connectivity, and cluster block relationships can directly influence training throughput, communication efficiency, and overall job completion time. Physical topology therefore becomes a first-class operational consideration. The scheduler must account not only for resource availability, but also for how GPUs, racks, fabric domains, and cluster blocks relate to one another. Extending Azure beyond traditional cloud boundaries Addressing this challenge requires more than connecting specialized AI infrastructure to Azure. Purpose-built infrastructure must operate as a natural extension of the Azure environment, with the governance, security, and operational experience customers already use. This is where Azure and enterprise AI clouds such as Dapple are taking a different approach. Rather than treating large-scale GPU clusters as isolated infrastructure islands, the architecture extends the Azure operational model into dedicated AI environments. Customers can continue using familiar Azure governance frameworks, security models, and operational tools while leveraging infrastructure specifically optimized for large-scale AI workloads. The result is an architecture that combines two complementary capabilities: Purpose-built AI infrastructure optimized for topology-aware GPU workloads An Azure-native operational experience that preserves governance, security, and lifecycle consistency This model enables organizations to focus on AI innovation without creating a separate operational domain for their most demanding AI infrastructure. The role of Azure Kubernetes Service as the operational control plane As AI infrastructure evolves, the role of Kubernetes is evolving alongside it. Kubernetes has become the dominant platform for orchestrating cloud-native applications, AI services, and distributed workloads. Increasingly, it is also becoming the operational abstraction layer that enables organizations to manage heterogeneous infrastructure through a consistent interface. Azure Kubernetes Service (AKS) plays a critical role in this evolution. In the Azure and Dapple architecture, AKS serves as the primary operational control plane for AI workloads running on purpose-built GPU infrastructure. The physical infrastructure remains optimized around the realities of large-scale AI systems, including GPU topology, InfiniBand fabrics, hardware maintenance domains, and infrastructure lifecycle management. At the same time, platform teams continue to interact through familiar Kubernetes constructs for workload deployment, scheduling policies, governance, and lifecycle management. Organizations gain access to infrastructure designed specifically for modern AI workloads while preserving the operational consistency of a Kubernetes-based platform. In practice, enterprises rarely operate a single orchestrator. Kubernetes provides the common substrate, but the layers above it differ by team: batch schedulers for large training runs, workflow engines for data and fine-tuning pipelines, and internal control planes built around a specific operating model. Many organizations run several of these concurrently. For this reason, orchestration is best treated as a customer-owned concern. The infrastructure layer remains responsible for presenting consistent topology, readiness, and lifecycle semantics to whichever orchestrators an organization already operates. Extending Kubernetes into topology-aware infrastructure While Kubernetes excels at abstracting infrastructure, large-scale AI systems frequently require orchestration platforms that understand physical topology, maintenance boundaries, network fabrics, and hardware relationships. Simply exposing GPUs to a scheduler is no longer sufficient. Infrastructure operations must account for cluster blocks, fabric domains, rack boundaries, node recovery workflows, and physical lifecycle management activities that do not naturally exist within traditional Kubernetes environments. To bridge this challenge, Azure and Dapple are collaborating on an architecture that extends Kubernetes operations into topology-aware AI infrastructure while maintaining a clear separation between workload intent and infrastructure intent. Within this model, AKS remains the authoritative control plane for Kubernetes operations. Platform teams continue to leverage: Kubernetes APIs GitOps workflows Policy management Application lifecycle operations Multi-cluster governance AI workload orchestration At the infrastructure layer, Dapple maintains awareness of: Cluster-block composition GPU fabric topology Network-domain relationships Hardware lifecycle operations Infrastructure maintenance events Recovery and repave workflows The Dapple Operator serves as the integration layer between these domains. Rather than exposing infrastructure complexity directly to platform teams, the operator translates Azure-native operational intent into topology-aware infrastructure actions while synchronizing infrastructure state back into the Kubernetes environment. When hardware maintenance, node replacement, cluster recovery, or lifecycle events occur, the operator coordinates state transitions across both layers, helping ensure that infrastructure operations and workload operations remain aligned. FlexNode connects external workers to AKS, while the Dapple Operator synchronizes cluster block identity, topology, fabric health, maintenance intent, and rejoin evidence with the underlying Dapple infrastructure. This architecture reflects an important design principle: AKS remains responsible for workload intent, while Dapple remains responsible for infrastructure intent. The result is a unified operational model that preserves the architectural characteristics of purpose-built AI infrastructure while enabling Azure-native operations. Learn more Dapple Private AI Cloud | Microsoft Marketplace Dapple | The Enterprise OS Cloud
Windows updates for server 2019
Hello, We have a mixture of two 2012 and three 2019 domain controllers in our environment. We have a GPO for domain controllers that is set to download updates and schedule them for installation (and any reboots) for 4am on Saturday mornings. The 2012 DCs follow this GPO, but the 2019 DCs don't. They will install updates and reboot at random times. I did a gpresult to make sure that the GPO was applied and the settings were correct. In looking at the registry of one of the 2019 DCs, all looks to be correct? Registry screen shot below: Can anyone tell me how to troubleshoot further? Thanks. Bryan HuntIntroducing a Guided Copilot Experience for Building Azure Apps in VS Code
Today we're previewing a new way to build cloud apps with GitHub Copilot in VS Code: a guided Copilot experience that takes you from idea to a deployed Azure app through a structured, predictable workflow instead of a free-form chat session that may or may not land where you need it to. The problem: Copilot is powerful, but unpredictable Ask Copilot to "build me a Node.js API on Azure Functions with a Postgres database" today, and you might get a working project, or you might not. Sometimes Copilot scaffolds the app but skips infrastructure. Sometimes it generates a deployment script that fails halfway through. Sometimes it forgets what you were building three prompts later. The underlying model is capable. The problem is the shape of the experience: an open-ended chat with no checkpoints, no guardrails, and no consistent path from "I have an idea" to "it's running in Azure." How it works The guided Copilot experience adds to that open-ended flow with three clear, explicit stages: 1. Project scaffolding: Describe your app in plain language. Copilot proposes an architecture and asks for anything that is missing (e.g., language, app type, Azure services) through simple forms and pickers, not more back-and-forth chat. You review and approve the plan before any code is written. 2. Local development: Your project is scaffolded then Copilot checks for the runtimes, emulators, and tools you'll need and helps you install what's missing, so the project runs locally from the very first launch. Debug configurations are wired up automatically, so you can test and iterate on your app before it ever touches Azure. 3. Deployment: Copilot shows the tools that will be used for deployment along with a summary of all resources that will be created and a cost estimate so you can be confident in what you're getting. Infrastructure files are created for your app to ensure a consistent and reproducible deployment environment for testing and staging before going to production. Deployment runs through the same reliable tooling used across Azure (az and azd), so if something goes wrong, you get a clear explanation and a concrete next step instead of a wall of CLI output. Once your app is live, the guided experience doesn't disappear, Copilot retains context about your project's architecture, so coming back later to add a service or make a change picks up right where you left off. 🎉 What this means for how you work Determinism over guesswork. The same input reliably produces the same kind of outcome, so no more wondering which "mode" Copilot is going to be in. First try success. Projects are built to run and deploy on the first attempt, not after several rounds of manual repair. Structure over chat walls. Decisions that matter such as architecture choices, missing configuration, and deployment targets are surfaced through real UI, so you're not parsing paragraphs of text to figure out what Copilot needs from you. Stay in VS Code. The entire journey, from planning through deployment, happens without leaving your editor. What's in the initial preview The first version focuses on JavaScript and TypeScript projects, including web apps, Azure Functions, Container Apps, and Static Web Apps, with support for PostgreSQL, Azure Storage, Azure Key Vault, and Azure OpenAI. .NET and Python support are on the roadmap for a future release. Nothing about your existing Copilot workflows changes, the guided experience is an additional, opinionated path alongside the free form chat you already know, for when you want a reliable, structured way to go from zero to deployed. What's next This is an early look at a broader shift in how we think about AI-assisted development on Azure: less "ask and hope," more structured collaboration with clear checkpoints and real UI where it counts. We're continuing to expand language support, refine the deployment experience, and incorporate feedback from developers using it in the wild. Install the Azure Tools extension pack and open an empty folder to try the guided Copilot experience and let us know what you think. File issues or share feedback on the GitHub repo.
Action required: PromQL regex matching in Azure Monitor Workspace is becoming spec-compliant
TL;DR: Bare regex matchers like pod=~"foo" will soon return only exact matches, not substring matches. If you rely on =~ for "starts with" or "contains" semantics, update your queries to add .* before the change lands. What is changing AMW's Prometheus Query Service is moving to fully-anchored regex matching, aligning with the upstream Prometheus specification. After the change, every =~ and !~ matcher is wrapped with ^…$ before evaluation. This matches the behavior of self-hosted Prometheus, Grafana Cloud Metrics, and Amazon Managed Prometheus. "Regex matches are fully anchored. A match of env=~"foo" is treated as env=~"^foo$"." Reference: Prometheus querying basics — Instant vector selectors Behavior delta Given these timeseries: kube_pod_container_status_ready{pod="grafana-otel-collector-97b6c55f4-abc12"} kube_pod_container_status_ready{pod="grafana-otel-collector-97b6c55f4-def34"} kube_pod_container_status_ready{pod="grafana-otel-collector-97b6c55f4"} Query: kube_pod_container_status_ready{pod=~"grafana-otel-collector-97b6c55f4"} Environment Effective regex Series returned AMW PQS — current grafana-otel-collector-97b6c55f4 (unanchored) 3 (all matching pods) AMW PQS — after change ^grafana-otel-collector-97b6c55f4$ 1 (exact name only) Self-hosted Prometheus, Grafana Cloud, AMP ^grafana-otel-collector-97b6c55f4$ 1 (exact name only) If your query depends on the first row, it will return fewer — often zero — series after the change. What to do Audit any query, alert rule, recording rule, or dashboard variable that uses =~ or !~. Map each to the table below. Intent Update to Exact match No change necessary; both label="value" and label=~"value" should return exact matches after updating query engine version for fully anchored regex support. Starts with label=~"value.*" Ends with label=~".*value" Contains label=~".*value.*" Set of prefixes label=~"(a\|b\|c).*" Recommendation: Where the intent is exact match, switch from =~ to = It is clearer to readers and skips per-series regex evaluation. When ready, update AMW configuration to use the latest Query engine version. This can be done via the Azure Portal UI, Azure CLI, or PowerShell. Configuring AMW to use the latest Prometheus query engine version Azure Portal In the Azure portal, open your Azure Monitor workspace. Under Settings, select Properties. Select the Latest segment of the Query engine version for the AMW. Azure CLI Run the following command to update to the latest (2.0) metrics query engine version for the AMW: Az monitor account metrics-container update --subscription <subscriptionId> -g <resourcegroupName> -w <azmonWorkspaceName> -n default --version 2.0 Azure PowerShell Run the following command to use the latest Query engine version for the AMW: $resourceId = “/subscriptions/<subscriptionId>/resourceGroups/<resourcegroupName>/providers/Microsoft.Monitor/accounts/<azureMonitorWorkspaceName>/metricsContainers/default” Az resource update --ids $resourceId --api-version 2025-10-03 --set properties.version=2.0 --force-string To confirm: Az resource show --ids $resourceId --api-version 2025-10-03 --query properties.version --output tsv Expected output: 2.0 Common pitfalls Alternation anchors the whole expression. pod=~"foo|bar" becomes ^(?:foo|bar)$, not ^foo|bar$. Add .* and parentheses for prefix sets. . matches any character, including -. pod=~"my-pod.*" will match my-pod-xyz, my-podxyz, and my-pod9xyz. Use character classes like [-] for tighter matches. Empty-label matching is unchanged. label=~"" still matches series without that label. Timeline Public Preview rollout: September 2026 In Public Preview, newly created AMWs will default to the latest metrics query version (“2.0”) which features fully anchored regex matching. AMWs created prior to September 2026 will continue using version (“1.0”) unless users take action to update. This provides users with the opportunity to verify their query intent and update accordingly prior to cutover to the latest AMW metrics query engine version. General Availability: March 2027 In General Availability, all AMWs regardless of creation date will be updated to version “2.0” for cross-workspace compatibility. Action window: Audit and update queries before the General Availability date.Bulk task changes
Does anyone know how am I able to add dates to tasks in bulk if it's the same due date? How do I assign tasks in bulk to a specific person? These used to easily be done by selecting the sections and right clicking to change but many settings updated today and I am no longer able to do this.
