Azure VM Agent Status not ready
I have created a red hat openshift private cluster but the VMS are stuck in the state of "agent status not ready." I have followed these troubleshooting steps: Linux Virtual Machine Agent Status "Not Ready" - Microsoft Community Hub However, all of them seem to point to trying to check and see what is on the VM itself. I am unable to do this because I can't SSH into the machine. Has anyone else ran into this issue and been able to resolve it? I am deploying it via CLI as I was not able to do it via GUI for some reason. This is my script: #az login az account set --name "accountnamehidden" #az provider register -n Microsoft.RedHatOpenShift --wait #az provider register -n Microsoft.Compute --wait #az provider register -n Microsoft.Storage --wait #az provider register -n Microsoft.Authorization --wait $LOCATION= "eastus" # the location of your cluster $RESOURCEGROUP= "sample-rg" # the name of the resource group where you want to create your cluster $CLUSTER= "K8sDev1test" # the name of your cluster $arovnet= "sample-vnet" $mastersubnet = "k8sDev1-master-ue-snet" $workersubnet = "k8sDev1-worker-ue-snet" az aro create --resource-group "samplerg" --vnet-resource-group "sample-vnet-rg" --name $CLUSTER --vnet $arovnet --master-subnet "k8sDev1-master-ue-snet" --worker-subnet "k8sDev1-worker-ue-snet" --apiserver-visibility Private --ingress-visibility Private --fips true --outbound-type UserDefinedRouting --client-id hidden --client-secret hiddenAzure Red Hat OpenShift with hosted control planes now available in public preview
Red Hat OpenShift momentum on Azure keeps building, driven by teams standardizing on Kubernetes for application modernization and, increasingly, by AI-enabled applications that need a consistent platform for the services around them. Azure Red Hat OpenShift gives those teams a jointly engineered, first-party Azure service, and today's announcement adds a new way to run it. Today we are excited to announce the public preview of Azure Red Hat OpenShift with hosted control planes. Hosted control planes is a new deployment option that runs the OpenShift control plane as a fully managed service in a Microsoft-managed Azure subscription, operated by Microsoft and Red Hat site reliability engineers (SREs), separate from the worker nodes that run your applications. This deployment option helps organizations accelerate application modernization, increase developer velocity, streamline operations, and strengthen security while maintaining the familiar OpenShift experience jointly engineered and supported by Microsoft and Red Hat. Azure Red Hat OpenShift with standard architecture remains fully supported and actively developed. Azure Red Hat OpenShift provides a unified platform for managing different workloads such as AI-enabled applications alongside VMs and containers, offloading ongoing infrastructure management to a team of Red Hat SREs, giving you a robust foundation to build, deploy and manage applications at scale. One service, two places the control plane can live The two deployment models compare like this: Dimension Control plane in your subscription Hosted control plane Control-plane location Your Azure subscription Microsoft-managed Azure subscription Who operates it Red Hat SREs, in a single-tenant control plane. You pay for control-plane nodes on every cluster Red Hat SREs, in a multi-tenant hosted control plane Upgrade coupling Control plane and workers move as one Independent; Control plane can run on 2 minor versions ahead of worker node pools. Nodepools can also be on different versions with each other. Minimum cluster footprint 3 Control-plane nodes plus 3 worker nodes Two worker nodes Figure 1. Today the control plane runs in your subscription alongside your workers. With a hosted control plane, it moves into a Microsoft-managed Azure subscription operated by Red Hat SREs, and the two planes meet through a delegated VNet integration subnet. Key benefits of Azure Red Hat Openshift with hosted control planes Organizations can modernize at their own pace and run virtual machines, containers, and cloud-native applications on a single platform while extending existing applications for AI-ready workloads. Hosted control planes also help developers move faster. Clusters provision in minutes, and development and test environments can scale down during idle periods and scale back up when needed. This enables teams to accelerate experimentation, shorten testing cycles, and bring applications to production more quickly while optimizing infrastructure spend. To support growing multi-cluster environments, hosted control planes simplify platform operations and provide greater flexibility and control. Independent control plane and worker node lifecycles give organizations more control over application upgrade timing while Microsoft and Red Hat manage the underlying platform. Organizations can scale clusters across teams, environments, and regions, customize networking with bring-your-own container network interface (CNI), and maintain operational consistency without increasing management complexity. What the Azure platform adds What sets Azure Red Hat OpenShift apart is how deeply it integrates with the broader Azure platform, helping organizations simplify operations, accelerate adoption of OpenShift, and reduce the complexity of running cloud-native applications at scale. Customers gain access to Azure's global infrastructure, enterprise security capabilities, and rich portfolio of data and AI services, accelerating modernization and innovation without requiring changes to familiar OpenShift tools and workflows. For organizations already invested in Azure, Azure Red Hat OpenShift operates as a first-party Azure service that fits naturally into existing cloud operations. Clusters can be managed, governed, secured, and monitored using the same Azure tools, policies, and automation practices already used across the broader Azure estate. Customers also benefit from a unified commercial model, integrated billing, and Azure-native management experience, while Azure Red Hat OpenShift consumption contributes toward Microsoft Azure Consumption Commitment, helping maximize the value of existing cloud investments. Customers are already building on that foundation. Banco Bradesco, one of the largest financial institutions in Latin America, runs its enterprise AI platform on Azure Red Hat OpenShift, using integration with Azure identity, security, and policy capabilities to unify governance across more than 200 AI initiatives in a highly regulated environment. Topicus runs its Akkuro lending platform on Azure Red Hat OpenShift and deploys in Switzerland North to keep financial data in-country, using the same Azure-native controls to maintain a repeatable deployment model across regions. Both illustrate what the Azure platform adds: a consistent operating model, enterprise-grade security and compliance, and native access to Azure data and AI services on a service jointly operated by Microsoft and Red Hat. Figure 2. The Azure platform around the service: Microsoft Entra ID managed and workload identities, a customer-managed key in Azure Key Vault for etcd encryption, Azure Monitor diagnostic settings for control-plane logs, Customer Lockbox for Microsoft Azure for support-access approval, portal (upcoming), CLI, REST API and Bicep self-service, and native integration with Azure compute, database, analytics, and machine learning services. Secure by design Azure Red Hat OpenShift with hosted control planes is secure by design, helping you run sensitive and regulated workloads with confidence without adding operational complexity. By running the control plane in a Microsoft-managed subscription, the platform reduces infrastructure exposure while maintaining secure access to applications through managed ingress. Identity management is seamless. Cluster operators and applications use managed identities and workload identities that authenticate with short-lived, automatically rotated tokens, eliminating the need to manage service principals or long-lived credentials. Organizations can also integrate with their existing identity providers, preserving established authentication and access management practices. Azure-native protections help safeguard data and support compliance requirements. Data is encrypted by default with etcd encryption, with the option to use customer-managed keys through Azure Key Vault for additional control. Confidential and sandboxed containers provide additional protection for data in use, helping organizations address stringent security, data residency, and digital sovereignty requirements without deploying a separate cloud environment. The platform is backed by enterprise-grade compliance and support. Azure Red Hat OpenShift (with standard architecture) is pre-certified for HIPAA, FedRAMP, DoD IL4, PCI-DSS, ISO, and SOC standards, supported by joint Microsoft and Red Hat incident response. Customer Lockbox for Microsoft Azure adds another layer of control by requiring explicit customer approval before Microsoft support personnel can access cluster resources. Get started now Availability is expanding, with UK South, Canada Central, Australia East, Switzerland North, Brazil South, Central India, US East 2, and West Europe among the regions supported at public preview launch. The fastest way to get started is to create three resources: a cluster, a node pool, and an external authentication provider, using either the Azure CLI extension or a Bicep template. From there, deploy a single application using workload identity to see the full flow end to end, signing in with your existing identity provider, and an application reaching Azure services with no stored credentials. See the Azure Red Hat OpenShift documentation for the current region list, supported versions, and the deployment reference, and tell us in the comments what you would like covered next. What it costs Component Price Purchasing Options OpenShift license (per worker core) $0.171 per hour per 4 worker vCPUs. The same flat rate applies to every VM series, size, and generation. Pay-as-you-go, or a 1- or 3-year reservation on the OpenShift license (available at GA) Azure infrastructure (worker VMs, storage, networking, plus control-plane nodes) Billed separately at standard Azure rates for Linux VMs. Standard clusters are also billed for the control-plane deployment; clusters with a hosted control plane are not. Pay-as-you-go, reservations, or Azure savings plan for compute Cluster management (only applicable to Azure Red Openshift with hosted control planes) A flat $0.25 per cluster per hour. Applies only to clusters with a hosted control plane. Pay-as-you-go A hosted control plane takes control-plane node cost off your bill and replaces it with a single, predictable cluster management fee. Pricing for hosted control planes is not yet reflected on the Azure Red Hat OpenShift pricing page; pricing calculator support is coming soon. Azure Red Hat OpenShift with hosted control planes is in public preview, and this pricing is preliminary and subject to change prior to general availability.
Konto got hacked and the hacker deleted it
A few weeks ago my email konto got hacked. The hacker also also deleted it. Now i cant log in in video games and in microsoft itsself but in like Disney or Gmail. I also went to the microsoft support but it dont work because they want to sent a code to a phone number or email(the email has @neplys in it) I dont know. Does somebody know my problem and could help me?Create a CLI and API like wslc but for Windows containers
The current issue is the dependency on Docker and the docker cli for Windows Containers. We would like a native solution from Microsoft for Windows Containers. This would be similar to wslc, but fully compatible for Windows containers. This could be done by leveraging existing Windows container APIs and the containerd container engine for Windows containers. I have also opened an enhancement feature on github for Windows Containers a month ago but there has been no feedback. https://github.com/microsoft/Windows-Containers/issues/643 Microsoft needs to step up and make Windows/ Windows Server containers on par with linux containersAzure Container Apps Sandboxes (Preview): Giving AI Agents a Safe Place to Work
Co-written by Nikoloz Buligini, Front End Developer at Templafy, and Jan Kalis, Azure Container Apps Sandboxes, Core AI, Microsoft Every team building with multi-tenant AI agent platforms hits the same wall. The agent is smart enough to read your code, reason about a bug, and propose a fix. But the moment it needs to take an action - clone a repo, install tooling, run a command, hit an internal endpoint - you have to answer some uncomfortable questions: where does it run, what permissions does it have and what can it access? Run it on your own infrastructure and inherit the blast radius. Give it broad network access and you have handed an autonomous process the keys to your environment. Lock it down too hard and the agent cannot do its job. This is exactly the problem Azure Container Apps Sandboxes was built to solve. And it is exactly the problem the team at Templafy solved in production. This post walks through what Sandboxes are, the features that make them a good fit for agentic workloads and how Templafy put ACA Sandboxes to work. What are Azure Container Apps Sandboxes? Azure Container Apps Sandboxes (Preview) are secure, isolated compute environments that start in seconds, scale to thousands, and do not charge you for compute while stopped. Each sandbox runs inside its own hardware-isolated microVM, fully separated from the host, the platform, and every other sandbox. Bring your own container image or use an included one, and Sandboxes handle provisioning, isolation, and lifecycle. This is the same compute fabric behind products like Cloud sandboxes in GitHub Copilot, Foundry Hosted Agents, and Azure Container Apps Express, and now you can build directly on it. For platform builders, that means enterprise-grade, multi-tenant isolation as a building block you would otherwise spend years creating. For AI agents, a sandbox becomes a self-configurable tool: spin up a fresh environment in seconds, run untrusted code, compile a project, or explore a codebase, then throw it away. On one side you empower humans to build platforms. On the other you empower agents to extend their own capabilities. The features that make Sandboxes fit agentic work A fast microVM is table stakes. What makes Sandboxes practical for real agent workloads is the control around them. Snapshots capture a fully configured environment and resume from it, ideal for long-running tasks or cloning setups. Egress controls declare exactly what a sandbox may reach, so an agent can pull from source control and package registries but nothing you did not approve. Managed identities authenticate to Azure with no secrets in the image. Automatic suspend and resume map cleanly onto how conversational agents behave, warming back up with full context when a conversation continues. Ports give your orchestrator a channel to a long-running agent process inside the sandbox. Two newer capabilities go further: virtual network integration puts an agent workspace inside your own Azure VNet with access to private endpoints, and bring your own storage lets data and artifacts outlive a session under your compliance rules. Together these turn a fast disposable VM into something you can hand to an autonomous agent in production. Which brings us to Templafy. How Templafy uses Sandboxes, in their own words The following section is written by Nikoloz, Front End Developer at Templafy. At Templafy we built an AI agent that helps our teams by doing longer-running source-code exploration on their behalf. Someone asks a question in a Slack thread, and behind the scenes the agent needs a real, isolated workspace where it can clone repositories, run tooling, and dig through code without touching anything it should not. It started as an engineer-facing tool for deep technical questions, but we recently opened it up to our product team for questions about undocumented product behavior. There, the agent first checks our Help Center through Azure AI Search with no sandbox required and only spins up a sandbox to explore the code when the docs come up short. Since these users aren't engineers, we summarize what the exploration finds into something more approachable. Funnily enough, the product team has been using it more than engineering does, and the feedback since launch has been great. We needed strong isolation, fast startup, and tight control over what each workspace could reach. Azure Container Apps Sandboxes gave us exactly that. We were sold on the model early enough that we built our own TypeScript SDK for Sandboxes before there was an official one, so we could drive the whole lifecycle from our Node stack. Here is what happens when the AI decides to start a workflow for a Slack thread: Create a sandbox from the public node-24 image. Install Git and other development tools. Clone our repositories and configure OpenCode. Restrict egress to only the Azure DevOps, package registry, and service endpoints the agent actually needs. Expose a port used to communicate with the agent runtime. Create and reuse snapshots so we do not repeat the bootstrap process on every run. Associate successful sessions with their Slack threads for a short period, so users can make follow-up requests against the same warm workspace. Stop or suspend idle sandboxes and resume them when a conversation continues. Delete failed or expired sessions. To do all of this we lean on the SDK for the full surface area: sandbox lifecycle operations, command execution, files, snapshots, ports, egress policies, public disk-image inspection, and sandbox state. Two features carry most of the weight for us. The first is restricted egress. Our agent is autonomous and works with our source code, so we are not comfortable letting it talk to the open internet. Declaring a narrow allow-list of endpoints means the workspace can do its job and nothing more, and that control is what let us ship this with confidence. The second is snapshots. Cloning repositories and configuring the toolchain is not free and doing it on every Slack message would make the agent feel slow. With snapshots we pay that cost once and resume from a ready-to-work state, so follow-ups in a thread start fast. This is only the first workflow. We are already looking at background investigations using Application Insights and eventually letting the agent open pull requests for quick bug fixes. The same isolated-workspace pattern extends cleanly to all of it. Who this is for If you are building an AI agent that needs to run code, explore a repository, or reach into your systems, and you have been nervous about where that runs, ACA Sandboxes is for you. You do not have to choose between a capable agent and a safe one. Give it a hardware-isolated workspace, declare exactly what it can touch, snapshot the setup, and let it work. Templafy went from "how do we let an agent safely explore our source code" to a production workflow running out of Slack threads, on infrastructure they controlled end to end. The building blocks are the same ones you can pick up today. Next steps Create your first sandbox - https://sandboxes.azure.com/ Explore Azure Container Apps Sandboxes documentation - https://sandboxes.azure.com/docs/sandboxes/ Start with Azure Container Apps Sandboxes samples - https://github.com/azure-samples/azure-container-apps-sandboxes/Lock Down AKS End to End with Application Gateway for Containers and Managed Cilium L7
Hello Folks! If your AKS cluster looks like most production clusters I have walked through, one of two things is true. Either nobody writes any network policies and every pod can talk to every other pod, so one compromised container blows up the entire blast radius. Or somebody wrote a few coarse rules along the lines of “namespace A talks to namespace B over port 80”, which sounds secure right up until an attacker realizes that port 80 is exactly where they were planning to live anyway. Real attacks happen at Layer 7, dressed up like ordinary HTTP traffic, and L3 / L4 plumbing cannot tell the difference. That is the gap session MAIS09 from the Microsoft Azure Infrastructure Summit 2026 closes. Vyshnavi Namani and Darshil Shah from the Azure Networking product team walked through how two AKS-managed add-ons, Application Gateway for Containers (AGC) and Cilium L7 via Advanced Container Networking Services (ACNS), can lock down the entire path from the public internet to a single pod. No NGINX. No external WAF appliance. No third-party CNI to babysit. 📺 Watch the session: Why IT Pros Should Care Let me cut to the chase. If you operate AKS clusters today, this session matters because: You probably still have an ingress controller and an external WAF stitched together with annotations and prayers. AGC plus ACNS collapses that stack into first-party add-ons that AKS owns end to end. Both Application Gateway for Containers and Advanced Container Networking Services are generally available. This is not a preview demo, this is production. Security is finally readable. Every rule is a YAML object. Code review, audit, GitOps. No more “what does this NGINX config map even do anymore” archaeology. It actually works on a real attack pattern. The demo shows WAF killing a SQL-injection-style GET that Cilium would have happily forwarded, because the method (GET) was on the allow list. If you have ever had to explain to an auditor why a single compromised pod could pivot across your whole cluster, this is your exit ramp. The AKS Security Gap This Closes Most clusters are protected by a load balancer at the edge and basically nothing inside. The cluster door looks like a vault, but the hallways are wide open. Cilium calls this the lateral movement problem, and it is exactly how Kubernetes attacks unfold in the wild. Compromise a pod, then phone home, then pivot. What MAIS09 demonstrates is something different. AGC is the L7 front door (the metal detector at the lobby). ACNS Cilium L7 is the lock on every pod’s office door. Both speak HTTP. Both enforce identity. Both are managed by AKS itself. The legacy alternative, Application Gateway Ingress Controller (AGIC), bolted a full Application Gateway onto your cluster through a translator. Two services, two lifecycles, two finger-pointing teams when something broke. AGC is the successor, built from scratch for Kubernetes, speaking the Gateway API natively, enabled with a single AKS flag. AKS provisions the controller, wires the identity, delegates the subnet, and owns the upgrades. You own the policies. AGC + Managed Cilium, End to End Here is the mental model from the session. Picture four concentric layers of defense between the public internet and a pod. AGC front end. One Azure resource, one public DNS name, and (thanks to the Kubernetes Gateway API) multiple hostnames behind the same IP. The demo runs Contoso, Fabrikam, and Adventure Works on a single AGC public IP using three HTTPRoute objects. One infrastructure, three websites. Real cost savings, real ownership clarity (platform owns the Gateway, app teams own the HTTPRoutes). Azure WAF on AGC. This is the content inspector. It runs the OWASP Core Rule Set (DRS 2.1 in the demo) against every incoming request, looks for SQL injection, cross-site scripting, path traversal, and the rest of the OWASP Top 10, and returns a 403 before the packet ever touches your pod. Microsoft maintains the rule set, you bind it to AGC via a SecurityPolicy. ACNS Cilium L7 ingress on every pod. This is where identity-based policy lives. Rules key off pod labels, not IPs, because IPs change every time the cluster autoscaler does its job. The demo uses an allow-agc-l7-get-only CiliumNetworkPolicy that lets the AGC backend reach the tenant pods, but only with GET or GET /products. Anything else, POST, PUT, DELETE, gets a Cilium-synthesized 403 before NGINX ever sees the request. ACNS east-west and egress policy. Two more policies do the heavy lifting inside. client-may-call-contoso-get-only lets the client pod reach Contoso with GET, and only Contoso. A default-deny baseline blocks everything else (pod-to-pod and pod-to-internet) with a single carve-out for kube-dns on port 53. The magic is that the same Cilium engine handles north-south, east-west, and egress with one consistent identity model. eBPF in the Linux kernel does the enforcement on the same node as the pod, so the decision happens before the packet leaves the host. No sidecars, no iptables sprawl, no daemonset you need to upgrade by hand. Real-world Scenarios The demo walks through six tests and the results map directly onto things you are probably trying to solve right now: Multi-site hosting on one IP. Three hostnames, one AGC, three 200 OKs from three different backend pods. If you are paying for three load balancers today, you can stop. WAF blocks a malicious GET that ACNS would have let through. This is the punch line of why you need both layers. The method (GET) is on the Cilium allow list, but the payload is a SQLi pattern. WAF returns 403 at the edge. Defense in depth, working as advertised. Method enforcement at the pod door. GET returns 200, POST/PUT/DELETE return 403, GET /admin returns 403, GET /products returns 200. Cilium is doing actual HTTP inspection, not just dropping packets. East-west enforcement with readable verdicts. Client to Contoso GET is 200. Same client, same destination, POST is 403 (L7 deny, TCP completed). Client to Fabrikam is 000 (L4 drop, no TCP handshake). Reading the difference between 403 and 000 is now a debuggable signal, not a mystery. Default-deny egress kills phone-home. A pod tries to reach bing.com. DNS resolves (the carve-out works), TCP SYN goes nowhere, wget gives up with exit code 1. If that pod was compromised and trying to exfiltrate data, this is where the attack chain dies. Selective allow still works. Same pod, same tools, but a DNS lookup against kube-dns inside the cluster returns instantly. We did not unplug the network. We locked it down with a purpose. Honest tradeoffs to call out. The session does not pretend everything is free. AGC introduces a billed subnet association and a managed identity you do not manage in BYO mode. Cilium L7 needs the Cilium data plane (ACNS Container Network Security features are Cilium-only). The Envoy proxy that handles L7 inspection has a cost only when you actually enforce L7, which is a fair deal in my book. Getting Started If you want to try this on a cluster of your own, three flags do most of the work on az aks create: --network-dataplane cilium (turns on the eBPF data plane) --enable-acns (enables Advanced Container Networking Services, including Hubble observability and Cilium L7 policy) --enable-app-routing or the ALB add-on flag (provisions the AGC controller as an AKS-managed add-on) From there you write four YAML objects: a default-deny CiliumNetworkPolicy, an allow-DNS carve-out, an AGC ingress allow with method and path constraints, and your east-west allow rules. The session repo includes the full set so you can clone and follow along. One bonus worth knowing about. ACNS ships Hubble out of the box, with pre-built Azure Managed Grafana dashboards. Flow logs, service maps, policy hit counts. Even on pods that are not yet under L7 enforcement, you get observability for free. When something breaks at 2 a.m., you have an audit trail instead of a tcpdump. Resources Azure Application Gateway for Containers documentation Set up Layer 7 policies with Advanced Container Networking Services AKS security concepts Cluster security best practices for AKS Container Network Observability for AKS (Hubble, Prometheus, Grafana) Advanced Container Networking Services hands-on lab Use cases of Advanced Network Observability for AKS (Azure Networking Blog) Watch the Rest of the Summit If MAIS09 hit the spot, there are dozens more sessions in the same playlist covering AKS networking at scale, Azure Local, AVM, the new Deployment Agent, and a lot more. Grab a coffee and binge. Microsoft Azure Infrastructure Summit 2026 playlist Cheers! Pierre Roman
