Migrating to the next generation of Virtual Nodes on Azure Container Instances (ACI)
As a quick recap, Virtual Nodes lets Azure Kubernetes Service (AKS) burst pods onto Azure Container Instances (ACI) on‑demand, without managing or scaling extra VM nodes. In this article I will demonstrate how you can migrate an existing AKS cluster using the Virtual Nodes managed add-on (legacy), to the new generation of Virtual Nodes on ACI, which is deployed and managed via Helm. More information about Virtual Nodes on Azure Container Instances can be found here, and the GitHub repo is available here. Advanced documentation for Virtual Nodes on ACI is also available here, and includes topics such as node customisation, release notes and a troubleshooting guide. Please note that all code samples within this guide are examples only, and are provided without warranty/support. Background Virtual Nodes on ACI is rebuilt from the ground-up, and includes several fixes and enhancements, for instance: Added support/features VNet peering, outbound traffic to the internet with network security groups Init containers Host aliases Arguments for exec in ACI Persistent Volumes and Persistent Volume Claims Container hooks Confidential containers (see supported regions list here) ACI standby pools Planned future enhancements Support for ACR image pull via Service Principal (SPN) Kubernetes network policies Support for IPv6 Windows containers Port Forwarding Note: The new generation of the add-on is managed via Helm rather than as an AKS managed add-on. Limitations DaemonSets are not supported Virtual Nodes on ACI requires AKS clusters with Azure CNI networking (Kubenet is not supported) Incompatible with API server authorized IP ranges for AKS (because of the subnet delegation to ACI) Requirements Each Virtual Nodes on ACI deployment requires 3 vCPUs and 12 GiB memory on one of the AKS cluster’s VMs. Each Virtual Nodes on ACI deployment supports up to 200 pods. Deploying the Virtual Nodes managed add-on (legacy) For the sake of completeness, I will first guide you through the traditional steps of deploying the Virtual Nodes managed add-on for AKS. For this walkthrough, I'm using Bash via Windows Subsystem for Linux (WSL), along with the Azure CLI. These steps are adapted from the official documentation here Set up environment variables: location=northeurope rg=rg-virtualnode-demo vnetName=vnet-virtualnode-demo clusterName=aks-virtualnode-demo aksSubnetName=subnet-aks vnSubnetName=subnet-vn Create resource group for the cluster and VNet: az group create --name $rg --location $location Create Virtual Network (VNet) and AKS/ACI subnets: az network vnet create \ --resource-group $rg --name $vnetName \ --address-prefixes 10.0.0.0/8 \ --subnet-name $aksSubnetName \ --subnet-prefix 10.240.0.0/16 az network vnet subnet create \ --resource-group $rg \ --vnet-name $vnetName \ --name $vnSubnetName \ --address-prefixes 10.241.0.0/16 \ --delegations Microsoft.ContainerInstance/containerGroups Retrieve the resource IDs for the AKS and ACI subnets: az network vnet subnet show --resource-group $rg --vnet-name $vnetName --name $aksSubnetName --query id -o tsv subnetId=$(az network vnet subnet show --resource-group $rg --vnet-name $vnetName --name $aksSubnetName --query id -o tsv) vnSubnetId=$(az network vnet subnet show --resource-group $rg --vnet-name $vnetName --name $vnSubnetName --query id -o tsv) Create a small AKS cluster with 2 nodes: az aks create --resource-group $rg --name $clusterName \ --node-count 2 --node-osdisk-size 30 --node-vm-size Standard_B4ms \ --network-plugin azure --vnet-subnet-id $subnetId \ --generate-ssh-keys Enable the Virtual Nodes managed add-on (legacy): az aks enable-addons --resource-group $rg --name $clusterName --addons virtual-node --subnet-name $vnSubnetName Retrieve the Managed Identity (MSI) used by Virtual Nodes and assign it the Network Contributor role for the ACI subnet: vnIdentityId=$(az aks show \ --resource-group $rg \ --name $clusterName \ --query "addonProfiles.aciConnectorLinux.identity.resourceId" \ -o tsv) vnIdentityObjectId=$(az identity show --ids $vnIdentityId --query principalId -o tsv) az role assignment create \ --assignee-object-id "$vnIdentityObjectId" \ --assignee-principal-type ServicePrincipal \ --role "Network Contributor" \ --scope "$vnSubnetId" Download the cluster's kubeconfig file: az aks get-credentials --resource-group $rg --name $clusterName Confirm the Virtual Nodes node shows within the cluster and is in a Ready state (virtual-node-aci-linux): $ kubectl get node NAME STATUS ROLES AGE VERSION aks-nodepool1-35702456-vmss000000 Ready <none> 46m v1.33.6 aks-nodepool1-35702456-vmss000001 Ready <none> 46m v1.33.6 virtual-node-aci-linux Ready agent 3m28s v1.25.0-vk-azure-aci-1.6.2 Migrating to the next generation of Virtual Nodes on Azure Container Instances via Helm chart I will now explain how to migrate from the Virtual Nodes managed add-on (legacy) to the new generation of Virtual Nodes on ACI. For this walkthrough, I'm using Bash via Windows Subsystem for Linux (WSL), along with the Azure CLI. Direct migration is not supported, and therefore the steps below show an example of removing Virtual Nodes managed add-on and its resources and then installing the Virtual Nodes on ACI Helm chart. In this walkthrough I will explain how to delete and re-create the Virtual Nodes subnet, however if you need to preserve the VNet and/or use a custom subnet name, refer to the Helm customisation steps here. Initialise environment variables location=northeurope rg=rg-virtualnode-demo vnetName=vnet-virtualnode-demo clusterName=aks-virtualnode-demo aksSubnetName=subnet-aks vnSubnetName=subnet-vn Scale-down any running Virtual Nodes workloads (example below): kubectl delete deploy <deploymentName> -n <namespace> Disable the Virtual Nodes managed add-on (legacy): az aks disable-addons --resource-group $rg --name $clusterName --addons virtual-node Export a backup of the original subnet configuration: az network vnet subnet show --resource-group $rg --vnet-name $vnetName --name $vnSubnetName > subnetConfigOriginal.json Delete the original subnet (subnets cannot be renamed and therefore must be re-created): az network vnet subnet delete -g $rg -n $vnSubnetName --vnet-name $vnetName Create the new Virtual Nodes on ACI subnet (replicate the configuration of the original subnet but with the specific name value of cg): vnSubnetId=$(az network vnet subnet create \ --resource-group $rg \ --vnet-name $vnetName \ --name cg \ --address-prefixes 10.241.0.0/16 \ --delegations Microsoft.ContainerInstance/containerGroups --query id -o tsv) Assign the cluster's -kubelet identity Contributor access to the infrastructure resource group, and Network Contributor access to the ACI subnet: nodeRg=$(az aks show --resource-group $rg --name $clusterName --query nodeResourceGroup -o tsv) nodeRgId=$(az group show -n $nodeRg --query id -o tsv) agentPoolIdentityId=$(az aks show --resource-group $rg --name $clusterName --query "identityProfile.kubeletidentity.resourceId" -o tsv) agentPoolIdentityObjectId=$(az identity show --ids $agentPoolIdentityId --query principalId -o tsv) az role assignment create \ --assignee-object-id "$agentPoolIdentityObjectId" \ --assignee-principal-type ServicePrincipal \ --role "Contributor" \ --scope "$nodeRgId" az role assignment create \ --assignee-object-id "$agentPoolIdentityObjectId" \ --assignee-principal-type ServicePrincipal \ --role "Network Contributor" \ --scope "$vnSubnetId" Download the cluster's kubeconfig file: az aks get-credentials -n $clusterName -g $rg Clone the virtualnodesOnAzureContainerInstances GitHub repo: git clone https://github.com/microsoft/virtualnodesOnAzureContainerInstances.git Install the Virtual Nodes on ACI Helm chart: helm install <yourReleaseName> <GitRepoRoot>/Helm/virtualnode Confirm the Virtual Nodes node shows within the cluster and is in a Ready state (virtualnode-n): $ kubectl get node NAME STATUS ROLES AGE VERSION aks-nodepool1-35702456-vmss000000 Ready <none> 4h13m v1.33.6 aks-nodepool1-35702456-vmss000001 Ready <none> 4h13m v1.33.6 virtualnode-0 Ready <none> 162m v1.33.7 Delete the previous Virtual Nodes node from the cluster: kubectl delete node virtual-node-aci-linux Test and confirm pod scheduling on Virtual Node: apiVersion: v1 kind: Pod metadata: annotations: name: demo-pod spec: containers: - command: - /bin/bash - -c - 'counter=1; while true; do echo "Hello, World! Counter: $counter"; counter=$((counter+1)); sleep 1; done' image: mcr.microsoft.com/azure-cli name: hello-world-counter resources: limits: cpu: 2250m memory: 2256Mi requests: cpu: 100m memory: 128Mi nodeSelector: virtualization: virtualnode2 tolerations: - effect: NoSchedule key: virtual-kubelet.io/provider operator: Exists If the pod successfully starts on the Virtual Node, you should see similar to the below: $ kubectl get pod -o wide demo-pod NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES demo-pod 1/1 Running 0 95s 10.241.0.4 vnode2-virtualnode-0 <none> <none> Modify your deployments to run on Virtual Nodes on ACI For Virtual Nodes managed add-on (legacy), the following nodeSelector and tolerations are used to run pods on Virtual Nodes: nodeSelector: kubernetes.io/role: agent kubernetes.io/os: linux type: virtual-kubelet tolerations: - key: virtual-kubelet.io/provider operator: Exists - key: azure.com/aci effect: NoSchedule For Virtual Nodes on ACI, the nodeSelector/tolerations are slightly different: nodeSelector: virtualization: virtualnode2 tolerations: - effect: NoSchedule key: virtual-kubelet.io/provider operator: Exists Troubleshooting Check the virtual-node-admission-controller and virtualnode-n pods are running within the vn2 namespace: $ kubectl get pod -n vn2 NAME READY STATUS RESTARTS AGE virtual-node-admission-controller-54cb7568f5-b7hnr 1/1 Running 1 (5h21m ago) 5h21m virtualnode-0 6/6 Running 6 (4h48m ago) 4h51m If these pods are in a Pending state, your node pool(s) may not have enough resources available to schedule them (use kubectl describe pod to validate). If the virtualnode-n pod is crashing, check the logs of the proxycri container to see whether there are any Managed Identity permissions issues (the cluster's -agentpool MSI needs to have Contributor access on the infrastructure resource group): kubectl logs -n vn2 virtualnode-0 -c proxycri Further troubleshooting guidance is available within the official documentation. Support If you have issues deploying or using Virtual Nodes on ACI, add a GitHub issue hereRethinking Background Workloads with Azure Functions on Azure Container Apps
Objective Azure Container Apps provides a flexible platform for running background workloads, supporting multiple execution models to address different workload needs. Two commonly used models are: Azure Functions on Azure Container Apps - overview of Azure functions Azure Container Apps Jobs – overview of Container App Jobs Both are first‑class capabilities on the same platform and are designed for different types of background processing. This blog explores Use Cases where Azure Functions on Azure Container Apps are best suited Use Cases where Container App Jobs provide advantages Use Cases where Azure Functions on Azure Container Apps Are suited Azure Functions on Azure Container Apps are particularly well suited for event‑driven and workflow‑oriented background workloads, where work is initiated by external signals and coordination is a core concern. The following use cases illustrate scenarios where the Functions programming model aligns naturally with the workload, allowing teams to focus on business logic while the platform handles triggering, scaling, and coordination. Event‑Driven Data Ingestion Pipelines For ingestion pipelines where data arrives asynchronously and unpredictably. Example: A retail company processes inventory updates from hundreds of suppliers. Files land in Blob Storage overnight, varying widely in size and arrival time. In this scenario: Each file is processed independently as it arrives Execution is driven by actual data arrival, not schedules Parallelism and retries are handled by the platform .blob_trigger(arg_name="blob", path="inventory-uploads/{name}", connection="StorageConnection") async def process_inventory(blob: func.InputStream): data = blob.read() # Transform and load to database await transform_and_load(data, blob.name) Multi‑Step, Event‑Driven Processing Workflows Functions works well for workloads that involve multiple dependent steps, where each step can fail independently and must be retried or resumed safely. Example: An order processing workflow that includes validation, inventory checks, payment capture, and fulfilment notifications. Using Durable Functions: Workflow state persisted automatically Each step can be retried independently Execution resumes from the point of failure rather than restarting Durable Functions on Container Apps solves this declaratively: .orchestration_trigger(context_name="context") def order_workflow(context: df.DurableOrchestrationContext): order = context.get_input() # Each step is independently retryable with built-in checkpointing validated = yield context.call_activity("validate_order", order) inventory = yield context.call_activity("check_inventory", validated) payment = yield context.call_activity("capture_payment", inventory) yield context.call_activity("notify_fulfillment", payment) return {"status": "completed", "order_id": order["id"]} Scheduled, Recurring Background Tasks For time‑based background work that runs on a predictable cadence and is closely tied to application logic. Example: Daily financial summaries, weekly aggregations, or month‑end reconciliation reports. Timer‑triggered Functions allow: Schedules to be defined in code Logic to be versioned alongside application code Execution to run in the same Container Apps environment as other services .timer_trigger(schedule="0 0 6 * * *", arg_name="timer") async def daily_financial_summary(timer: func.TimerRequest): if timer.past_due: logging.warning("Timer is running late!") await generate_summary(date.today() - timedelta(days=1)) await send_to_stakeholders() Long‑Running, Parallelizable Workloads Scenarios which require long‑running workloads to be decomposed into smaller units of work and coordinated as a workflow. Example: A large data migration processing millions of records. With Durable Functions: Work is split into independent batches Batches execute in parallel across multiple instances Progress is checkpointed automatically Failures are isolated to individual batches .orchestration_trigger(context_name="context") def migration_orchestrator(context: df.DurableOrchestrationContext): batches = yield context.call_activity("get_migration_batches") # Process all batches in parallel across multiple instances tasks = [context.call_activity("migrate_batch", b) for b in batches] results = yield context.task_all(tasks) yield context.call_activity("generate_report", results) Use Cases where Container App Jobs are a Best Fit Azure Container Apps Jobs are well suited for workloads that require explicit execution control or full ownership of the runtime and lifecycle. Common examples include: Batch Processing Using Existing Container Images Teams often have existing containerized batch workloads such as data processors, ETL tools, or analytics jobs that are already packaged and validated. When refactoring these workloads into a Functions programming model is not desirable, Container Apps Jobs allow them to run unchanged while integrating into the Container Apps environment. Large-Scale Data Migrations and One-Time Operations Jobs are a natural fit for one‑time or infrequently run migrations, such as schema upgrades, backfills, or bulk data transformations. These workloads are typically: Explicitly triggered Closely monitored Designed to run to completion under controlled conditions The ability to manage execution, retries, and shutdown behavior directly is often important in these scenarios. Custom Runtime or Specialized Dependency Workloads Some workloads rely on: Specialized runtimes Native system libraries Third‑party tools or binaries When these requirements fall outside the supported Functions runtimes, Container Apps Jobs provide the flexibility to define the runtime environment exactly as needed. Externally Orchestrated or Manually Triggered Workloads In some architectures, execution is coordinated by an external system such as: A CI/CD pipeline An operations workflow A custom scheduler or control plane Container Apps Jobs integrate well into these models, where execution is initiated explicitly rather than driven by platform‑managed triggers. Long-Running, Single-Instance Processing For workloads that are intentionally designed to run as a single execution unit without fan‑out, trigger‑based scaling, or workflow orchestration Jobs provide a straightforward execution model. This includes tasks where parallelism, retries, and state handling are implemented directly within the application. Making the Choice Consideration Azure Functions on Azure Container Apps Azure Container Apps Jobs Trigger model Event‑driven (files, messages, timers, HTTP, events) Explicit execution (manual, scheduled, or externally triggered) Scaling behavior Automatic scaling based on trigger volume / queue depth Fixed or explicitly defined parallelism Programming model Functions programming model with triggers, bindings, Durable Functions General container execution model State management Built‑in state, retries, and checkpointing via Durable Functions Custom state management required Workflow orchestration Native support using Durable Functions Must be implemented manually Boilerplate required Minimal (no polling, retry, or coordination code) Higher (polling, retries, lifecycle handling) Runtime flexibility Limited to supported Functions runtimes Full control over runtime and dependencies Getting Started on Functions on Azure Container Apps If you’re already running on Container Apps, adding Functions is straightforward: Your Functions run alongside your existing apps, sharing the same networking, observability, and scaling infrastructure. Check out the documentation for details - Getting Started on Functions on Azure Container Apps # Create a Functions app in your existing Container Apps environment az functionapp create \ --name my-batch-processor \ --storage-account mystorageaccount \ --environment my-container-apps-env \ --workload-profile-name "Consumption" \ --runtime python \ --functions-version 4 Getting Started on Container App Jobs on Azure Container Apps If you already have an Azure Container Apps environment, you can create a job using the Azure CLI. Checkout the documentation for details - Jobs in Azure Container Apps az containerapp job create \ --name my-job \ --resource-group my-resource-group \ --environment my-container-apps-env \ --trigger-type Manual \ --image mcr.microsoft.com/k8se/quickstart-jobs:latest \ --cpu 0.25 \ --memory 0.5Gi Quick Links Azure Functions on Azure Container Apps overview Create your Azure Functions app through custom containers on Azure Container Apps Run event-driven and batch workloads with Azure Functions on Azure Container AppsFrom "Maybe Next Quarter" to "Running Before Lunch" on Container Apps - Modernizing Legacy .NET App
In early 2025, we wanted to modernize Jon Galloway's MVC Music Store — a classic ASP.NET MVC 5 app running on .NET Framework 4.8 with Entity Framework 6. The goal was straightforward: address vulnerabilities, enable managed identity, and deploy to Azure Container Apps and Azure SQL. No more plaintext connection strings. No more passwords in config files. We hit a wall immediately. Entity Framework on .NET Framework did not support Azure.Identity or DefaultAzureCredential. We just could not add a NuGet package and call it done — we’d need EF Core, which means modern .NET - and rewriting the data layer, the identity system, the startup pipeline, the views. The engineering team estimated one week of dedicated developer work. As a product manager without extensive .NET modernization experience, I wasn't able to complete it quickly on my own, so the project was placed in the backlog. This was before the GitHub Copilot "Agent" mode, the GitHub Copilot app modernization (a specialized agent with skills for modernization) existed but only offered assessment — it could tell you what needed to change, but couldn't make the end to end changes for you. Fast-forward one year. The full modernization agent is available. I sat down with the same app and the same goal. A few hours later, it was running on .NET 10 on Azure Container Apps with managed identity, Key Vault integration, and zero plaintext credentials. Thank you GitHub Copilot app modernization! And while we were on it – GitHub Copilot helped to modernize the experience as well, built more tests and generated more synthetic data for testing. Why Azure Container Apps? Azure Container Apps is an ideal deployment target for this modernized MVC Music Store application because it provides a serverless, fully managed container hosting environment. It abstracts away infrastructure management while natively supporting the key security and operational features this project required. It pairs naturally with infrastructure-as-code deployments, and its per-second billing on a consumption plan keeps costs minimal for a lightweight web app like this, eliminating the overhead of managing Kubernetes clusters while still giving you the container portability that modern .NET apps benefit from. That is why I asked Copilot to modernize to Azure Container Apps - here's how it went - Phase 1: Assessment GitHub Copilot App Modernization started by analyzing the codebase and producing a detailed assessment: Framework gap analysis — .NET Framework 4.0 → .NET 10, identifying every breaking change Dependency inventory — Entity Framework 6 (not EF Core), MVC 5 references, System.Web dependencies Security findings — plaintext SQL connection strings in Web.config, no managed identity support API surface changes — Global.asax → Program.cs minimal hosting, System.Web.Mvc → Microsoft.AspNetCore.Mvc The assessment is not a generic checklist. It reads your code — your controllers, your DbContext, your views — and maps a concrete modernization path. For this app, the key finding was clear: EF 6 on .NET Framework cannot support DefaultAzureCredential. The entire data layer needs to move to EF Core on modern .NET to unlock passwordless authentication. Phase 2: Code & Dependency Modernization This is where last year's experience ended and this year's began. The agent performed the actual modernization: Project structure: .csproj converted from legacy XML format to SDK-style targeting net10.0 Global.asax replaced with Program.cs using minimal hosting packages.config → NuGet PackageReference entries Data layer (the hard part): Entity Framework 6 → EF Core with Microsoft.EntityFrameworkCore.SqlServer DbContext rewritten with OnModelCreating fluent configuration System.Data.Entity → Microsoft.EntityFrameworkCore namespace throughout EF Core modernization generated from scratch Database seeding moved to a proper DbSeeder pattern with MigrateAsync() Identity: ASP.NET Membership → ASP.NET Core Identity with ApplicationUser, ApplicationDbContext Cookie authentication configured through ConfigureApplicationCookie Security (the whole trigger for this modernization): Azure.Identity + DefaultAzureCredential integrated in Program.cs Azure Key Vault configuration provider added via Azure.Extensions.AspNetCore.Configuration.Secrets Connection strings use Authentication=Active Directory Default — no passwords anywhere Application Insights wired through OpenTelemetry Views: Razor views updated from MVC 5 helpers to ASP.NET Core Tag Helpers and conventions _Layout.cshtml and all partials migrated The code changes touched every layer of the application. This is not a find-and-replace — it's a structural rewrite that maintains functional equivalence. Phase 3: Local Testing After modernization, the app builds, runs locally, and connects to a local SQL Server (or SQL in a container). EF Core modernizations apply cleanly, the seed data loads, and you can browse albums, add to cart, and check out. The identity system works. The Key Vault integration gracefully skips when KeyVaultName isn't configured — meaning local dev and Azure use the same Program.cs with zero code branches. Phase 4: AZD UP and Deployment to Azure The agent also generates the deployment infrastructure: azure.yaml — AZD service definition pointing to the Dockerfile, targeting Azure Container Apps Dockerfile — Multi-stage build using mcr.microsoft.com/dotnet/sdk:10.0 and aspnet:10.0 infra/main.bicep — Full IaaC including: Azure Container Apps with system + user-assigned managed identity Azure SQL Server with Azure AD-only authentication (no SQL auth) Azure Key Vault with RBAC, Secrets Officer role for the managed identity Container Registry with ACR Pull role assignment Application Insights + Log Analytics All connection strings injected as Container App secrets — using Active Directory Default, not passwords One command: AZD UP Provisions everything, builds the container, pushes to ACR, deploys to Container Apps. The app starts, runs MigrateAsync() on first boot, seeds the database, and serves traffic. Managed identity handles all auth to SQL and Key Vault. No credentials stored anywhere. What Changed in a Year Early 2025 Now Assessment Available Available Automated code modernization Semi-manual ✅ Full modernization agent Infrastructure generation Semi-manual ✅ Bicep + AZD generated Time to complete Weeks ✅ Hours The technology didn't just improve incrementally. The gap between "assessment" and "done" collapsed. A year ago, knowing what to do and being able to do it were very different things. Now they're the same step. Who This Is For If you have a .NET Framework app sitting on a backlog because "the modernization is too expensive" — revisit that assumption. The process changed. GitHub Copilot app modernization helps you rewrite your data layer, generates your infrastructure, and gets you to azd up. It can help you generate tests to increase your code coverage. If you have some feature requests – or – if you want to further optimize the code for scale – bring your requirements or logs or profile traces, you can take care of all of that during the modernization process. MVC Music Store went from .NET Framework 4.0 with Entity Framework 6 and plaintext SQL credentials to .NET 10 on Azure Container Apps with managed identity, Key Vault, and zero secrets in code. In an afternoon. That backlog item might be a lunch break now 😊. Really. Find your legacy apps and try it yourself. Next steps Modernize your .Net or Java apps with GitHub Copilot app modernization – https://aka.ms/ghcp-appmod Open your legacy application in Visual Studio or Visual Studio Code to start the process Deploy to Azure Container Apps https://aka.ms/aca/startAn AI led SDLC: Building an End-to-End Agentic Software Development Lifecycle with Azure and GitHub.
This is due to the inevitable move towards fully agentic, end-to-end SDLCs. We may not yet be at a point where software engineers are managing fleets of agents creating the billion-dollar AI abstraction layer, but (as I will evidence in this article) we are certainly on the precipice of such a world. Before we dive into the reality of agentic development today, let me examine two very different modules from university and their relevance in an AI-first development environment. Manual Requirements Translation. At university I dedicated two whole years to a unit called “Systems Design”. This was one of my favourite units, primarily focused on requirements translation. Often, I would receive a scenario between “The Proprietor” and “The Proprietor’s wife”, who seemed to be in a never-ending cycle of new product ideas. These tasks would be analysed, broken down, manually refined, and then mapped to some kind of early-stage application architecture (potentially some pseudo-code and a UML diagram or two). The big intellectual effort in this exercise was taking human intention and turning it into something tangible to build from (BA’s). Today, by the time I have opened Notepad and started to decipher requirements, an agent can already have created a comprehensive list, a service blueprint, and a code scaffold to start the process (*cough* spec-kit *cough*). Manual debugging. Need I say any more? Old-school debugging with print()’s and breakpoints is dead. I spent countless hours learning to debug in a classroom and then later with my own software, stepping through execution line by line, reading through logs, and understanding what to look for; where correlation did and didn’t mean causation. I think back to my year at IBM as a fresh-faced intern in a cloud engineering team, where around 50% of my time was debugging different issues until it was sufficiently “narrowed down”, and then reading countless Stack Overflow posts figuring out the actual change I would need to make to a PowerShell script or Jenkins pipeline. Already in Azure, with the emergence of SRE agents, that debug process looks entirely different. The debug process for software even more so… #terminallastcommand WHY IS THIS NOT RUNNING? #terminallastcommand Review these logs and surface errors relating to XYZ. As I said: breakpoints are dead, for now at least. Caveat – Is this a good thing? One more deviation from the main core of the article if you would be so kind (if you are not as kind skip to the implementation walkthrough below). Is this actually a good thing? Is a software engineering degree now worthless? What if I love printf()? I don’t know is my answer today, at the start of 2026. Two things worry me: one theoretical and one very real. To start with the theoretical: today AI takes a significant amount of the “donkey work” away from developers. How does this impact cognitive load at both ends of the spectrum? The list that “donkey work” encapsulates is certainly growing. As a result, on one end of the spectrum humans are left with the complicated parts yet to be within an agent’s remit. This could have quite an impact on our ability to perform tasks. If we are constantly dealing with the complex and advanced, when do we have time to re-root ourselves in the foundations? Will we see an increase in developer burnout? How do technical people perform without the mundane or routine tasks? I often hear people who have been in the industry for years discuss how simple infrastructure, computing, development, etc. were 20 years ago, almost with a longing to return to a world where today’s zero trust, globally replicated architectures are a twinkle in an architect’s eye. Is constantly working on only the most complex problems a good thing? At the other end of the spectrum, what if the performance of AI tooling and agents outperforms our wildest expectations? Suddenly, AI tools and agents are picking up more and more of today’s complicated and advanced tasks. Will developers, architects, and organisations lose some ability to innovate? Fundamentally, we are not talking about artificial general intelligence when we say AI; we are talking about incredibly complex predictive models that can augment the existing ideas they are built upon but are not, in themselves, innovators. Put simply, in the words of Scott Hanselman: “Spicy auto-complete”. Does increased reliance on these agents in more and more of our business processes remove the opportunity for innovative ideas? For example, if agents were football managers, would we ever have graduated from Neil Warnock and Mick McCarthy football to Pep? Would every agent just augment a ‘lump it long and hope’ approach? We hear about learning loops, but can these learning loops evolve into “innovation loops?” Past the theoretical and the game of 20 questions, the very real concern I have is off the back of some data shared recently on Stack Overflow traffic. We can see in the diagram below that Stack Overflow traffic has dipped significantly since the release of GitHub Copilot in October 2021, and as the product has matured that trend has only accelerated. Data from 12 months ago suggests that Stack Overflow has lost 77% of new questions compared to 2022… Stack Overflow democratises access to problem-solving (I have to be careful not to talk in past tense here), but I will admit I cannot remember the last time I was reviewing Stack Overflow or furiously searching through solutions that are vaguely similar to my own issue. This causes some concern over the data available in the future to train models. Today, models can be grounded in real, tested scenarios built by developers in anger. What happens with this question drop when API schemas change, when the technology built for today is old and deprecated, and the dataset is stale and never returning to its peak? How do we mitigate this impact? There is potential for some closed-loop type continuous improvement in the future, but do we think this is a scalable solution? I am unsure. So, back to the question: “Is this a good thing?”. It’s great today; the long-term impacts are yet to be seen. If we think that AGI may never be achieved, or is at least a very distant horizon, then understanding the foundations of your technical discipline is still incredibly important. Developers will not only be the managers of their fleet of agents, but also the janitors mopping up the mess when there is an accident (albeit likely mopping with AI-augmented tooling). An AI First SDLC Today – The Reality Enough reflection and nostalgia (I don’t think that’s why you clicked the article), let’s start building something. For the rest of this article I will be building an AI-led, agent-powered software development lifecycle. The example I will be building is an AI-generated weather dashboard. It’s a simple example, but if agents can generate, test, deploy, observe, and evolve this application, it proves that today, and into the future, the process can likely scale to more complex domains. Let’s start with the entry point. The problem statement that we will build from. “As a user I want to view real time weather data for my city so that I can plan my day.” We will use this as the single input for our AI led SDLC. This is what we will pass to promptkit and watch our app and subsequent features built in front of our eyes. The goal is that we will: - Spec-kit to get going and move from textual idea to requirements and scaffold. - Use a coding agent to implement our plan. - A Quality agent to assess the output and quality of the code. - GitHub Actions that not only host the agents (Abstracted) but also handle the build and deployment. - An SRE agent proactively monitoring and opening issues automatically. The end to end flow that we will review through this article is the following: Step 1: Spec-driven development - Spec First, Code Second A big piece of realising an AI-led SDLC today relies on spec-driven development (SDD). One of the best summaries for SDD that I have seen is: “Version control for your thinking”. Instead of huge specs that are stale and buried in a knowledge repository somewhere, SDD looks to make them a first-class citizen within the SDLC. Architectural decisions, business logic, and intent can be captured and versioned as a product evolves; an executable artefact that evolves with the project. In 2025, GitHub released the open-source Spec Kit: a tool that enables the goal of placing a specification at the centre of the engineering process. Specs drive the implementation, checklists, and task breakdowns, steering an agent towards the end goal. This article from GitHub does a great job explaining the basics, so if you’d like to learn more it’s a great place to start (https://github.blog/ai-and-ml/generative-ai/spec-driven-development-with-ai-get-started-with-a-new-open-source-toolkit/). In short, Spec Kit generates requirements, a plan, and tasks to guide a coding agent through an iterative, structured development process. Through the Spec Kit constitution, organisational standards and tech-stack preferences are adhered to throughout each change. I did notice one (likely intentional) gap in functionality that would cement Spec Kit’s role in an autonomous SDLC. That gap is that the implement stage is designed to run within an IDE or client coding agent. You can now, in the IDE, toggle between task implementation locally or with an agent in the cloud. That is great but again it still requires you to drive through the IDE. Thinking about this in the context of an AI-led SDLC (where we are pushing tasks from Spec Kit to a coding agent outside of my own desktop), it was clear that a bridge was needed. As a result, I used Spec Kit to create the Spec-to-issue tool. This allows us to take the tasks and plan generated by Spec Kit, parse the important parts, and automatically create a GitHub issue, with the option to auto-assign the coding agent. From the perspective of an autonomous AI-led SDLC, Speckit really is the entry point that triggers the flow. How Speckit is surfaced to users will vary depending on the organisation and the context of the users. For the rest of this demo I use Spec Kit to create a weather app calling out to the OpenWeather API, and then add additional features with new specs. With one simple prompt of “/promptkit.specify “Application feature/idea/change” I suddenly had a really clear breakdown of the tasks and plan required to get to my desired end state while respecting the context and preferences I had previously set in my Spec Kit constitution. I had mentioned a desire for test driven development, that I required certain coverage and that all solutions were to be Azure Native. The real benefit here compared to prompting directly into the coding agent is that the breakdown of one large task into individual measurable small components that are clear and methodical improves the coding agents ability to perform them by a considerable degree. We can see an example below of not just creating a whole application but another spec to iterate on an existing application and add a feature. We can see the result of the spec creation, the issue in our github repo and most importantly for the next step, our coding agent, GitHub CoPilot has been assigned automatically. Step 2: GitHub Coding Agent - Iterative, autonomous software creation Talking of coding agents, GitHub Copilot’s coding agent is an autonom ous agent in GitHub that can take a scoped development task and work on it in the background using the repository’s context. It can make code changes and produce concrete outputs like commits and pull requests for a developer to review. The developer stays in control by reviewing, requesting changes, or taking over at any point. This does the heavy lifting in our AI-led SDLC. We have already seen great success with customers who have adopted the coding agent when it comes to carrying out menial tasks to save developers time. These coding agents can work in parallel to human developers and with each other. In our example we see that the coding agent creates a new branch for its changes, and creates a PR which it starts working on as it ticks off the various tasks generated in our spec. One huge positive of the coding agent that sets it apart from other similar solutions is the transparency in decision-making and actions taken. The monitoring and observability built directly into the feature means that the agent’s “thinking” is easily visible: the iterations and steps being taken can be viewed in full sequence in the Agents tab. Furthermore, the action that the agent is running is also transparently available to view in the Actions tab, meaning problems can be assessed very quickly. Once the coding agent is finished, it has run the required tests and, even in the case of a UI change, goes as far as calling the Playwright MCP server and screenshotting the change to showcase in the PR. We are then asked to review the change. In this demo, I also created a GitHub Action that is triggered when a PR review is requested: it creates the required resources in Azure and surfaces the (in this case) Azure Container Apps revision URL, making it even smoother for the human in the loop to evaluate the changes. Just like any normal PR, if changes are required comments can be left; when they are, the coding agent can pick them up and action what is needed. It’s also worth noting that for any manual intervention here, use of GitHub Codespaces would work very well to make minor changes or perform testing on an agent’s branch. We can even see the unit tests that have been specified in our spec how been executed by our coding agent. The pattern used here (Spec Kit -> coding agent) overcomes one of the biggest challenges we see with the coding agent. Unlike an IDE-based coding agent, the GitHub.com coding agent is left to its own iterations and implementation without input until the PR review. This can lead to subpar performance, especially compared to IDE agents which have constant input and interruption. The concise and considered breakdown generated from Spec Kit provides the structure and foundation for the agent to execute on; very little is left to interpretation for the coding agent. Step 3: GitHub Code Quality Review (Human in the loop with agent assistance.) GitHub Code Quality is a feature (currently in preview) that proactively identifies code quality risks and opportunities for enhancement both in PRs and through repository scans. These are surfaced within a PR and also in repo-level scoreboards. This means that PRs can now extend existing static code analysis: Copilot can action CodeQL, PMD, and ESLint scanning on top of the new, in-context code quality findings and autofixes. Furthermore, we receive a summary of the actual changes made. This can be used to assist the human in the loop in understanding what changes have been made and whether enhancements or improvements are required. Thinking about this in the context of review coverage, one of the challenges sometimes in already-lean development teams is the time to give proper credence to PRs. Now, with AI-assisted quality scanning, we can be more confident in our overall evaluation and test coverage. I would expect that use of these tools alongside existing human review processes would increase repository code quality and reduce uncaught errors. The data points support this too. The Qodo 2025 AI Code Quality report showed that usage of AI code reviews increased quality improvements to 81% (from 55%). A similar study from Atlassian RovoDev 2026 study showed that 38.7% of comments left by AI agents in code reviews lead to additional code fixes. LLM’s in their current form are never going to achieve 100% accuracy however these are still considerable, significant gains in one of the most important (and often neglected) parts of the SDLC. With a significant number of software supply chain attacks recently it is also not a stretch to imagine that that many projects could benefit from "independently" (use this term loosely) reviewed and summarised PR's and commits. This in the future could potentially by a specialist/sub agent during a PR or merge to focus on identifying malicious code that may be hidden within otherwise normal contributions, case in point being the "near-miss" XZ Utils attack. Step 4: GitHub Actions for build and deploy - No agents here, just deterministic automation. This step will be our briefest, as the idea of CI/CD and automation needs no introduction. It is worth noting that while I am sure there are additional opportunities for using agents within a build and deploy pipeline, I have not investigated them. I often speak with customers about deterministic and non-deterministic business process automation, and the importance of distinguishing between the two. Some processes were created to be deterministic because that is all that was available at the time; the number of conditions required to deal with N possible flows just did not scale. However, now those processes can be non-deterministic. Good examples include IVR decision trees in customer service or hard-coded sales routines to retain a customer regardless of context; these would benefit from less determinism in their execution. However, some processes remain best as deterministic flows: financial transactions, policy engines, document ingestion. While all these flows may be part of an AI solution in the future (possibly as a tool an agent calls, or as part of a larger agent-based orchestration), the processes themselves are deterministic for a reason. Just because we could have dynamic decision-making doesn’t mean we should. Infrastructure deployment and CI/CD pipelines are one good example of this, in my opinion. We could have an agent decide what service best fits our codebase and which region we should deploy to, but do we really want to, and do the benefits outweigh the potential negatives? In this process flow we use a deterministic GitHub action to deploy our weather application into our “development” environment and then promote through the environments until we reach production and we want to now ensure that the application is running smoothly. We also use an action as mentioned above to deploy and surface our agents changes. In Azure Container Apps we can do this in a secure sandbox environment called a “Dynamic Session” to ensure strong isolation of what is essentially “untrusted code”. Often enterprises can view the building and development of AI applications as something that requires a completely new process to take to production, while certain additional processes are new, evaluation, model deployment etc many of our traditional SDLC principles are just as relevant as ever before, CI/CD pipelines being a great example of that. Checked in code that is predictably deployed alongside required services to run tests or promote through environments. Whether you are deploying a java calculator app or a multi agent customer service bot, CI/CD even in this new world is a non-negotiable. We can see that our geolocation feature is running on our Azure Container Apps revision and we can begin to evaluate if we agree with CoPilot that all the feature requirements have been met. In this case they have. If they hadn't we'd just jump into the PR and add a new comment with "@copilot" requesting our changes. Step 5: SRE Agent - Proactive agentic day two operations. The SRE agent service on Azure is an operations-focused agent that continuously watches a running service using telemetry such as logs, metrics, and traces. When it detects incidents or reliability risks, it can investigate signals, correlate likely causes, and propose or initiate response actions such as opening issues, creating runbook-guided fixes, or escalating to an on-call engineer. It effectively automates parts of day two operations while keeping humans in control of approval and remediation. It can be run in two different permission models: one with a reader role that can temporarily take user permissions for approved actions when identified. The other model is a privileged level that allows it to autonomously take approved actions on resources and resource types within the resource groups it is monitoring. In our example, our SRE agent could take actions to ensure our container app runs as intended: restarting pods, changing traffic allocations, and alerting for secret expiry. The SRE agent can also perform detailed debugging to save human SREs time, summarising the issue, fixes tried so far, and narrowing down potential root causes to reduce time to resolution, even across the most complex issues. My initial concern with these types of autonomous fixes (be it VPA on Kubernetes or an SRE agent across your infrastructure) is always that they can very quickly mask problems, or become an anti-pattern where you have drift between your IaC and what is actually running in Azure. One of my favourite features of SRE agents is sub-agents. Sub-agents can be created to handle very specific tasks that the primary SRE agent can leverage. Examples include alerting, report generation, and potentially other third-party integrations or tooling that require a more concise context. In my example, I created a GitHub sub-agent to be called by the primary agent after every issue that is resolved. When called, the GitHub sub-agent creates an issue summarising the origin, context, and resolution. This really brings us full circle. We can then potentially assign this to our coding agent to implement the fix before we proceed with the rest of the cycle; for example, a change where a port is incorrect in some Bicep, or min scale has been adjusted because of latency observed by the SRE agent. These are quick fixes that can be easily implemented by a coding agent, subsequently creating an autonomous feedback loop with human review. Conclusion: The journey through this AI-led SDLC demonstrates that it is possible, with today’s tooling, to improve any existing SDLC with AI assistance, evolving from simply using a chat interface in an IDE. By combining Speckit, spec-driven development, autonomous coding agents, AI-augmented quality checks, deterministic CI/CD pipelines, and proactive SRE agents, we see an emerging ecosystem where human creativity and oversight guide an increasingly capable fleet of collaborative agents. As with all AI solutions we design today, I remind myself that “this is as bad as it gets”. If the last two years are anything to go by, the rate of change in this space means this article may look very different in 12 months. I imagine Spec-to-issue will no longer be required as a bridge, as native solutions evolve to make this process even smoother. There are also some areas of an AI-led SDLC that are not included in this post, things like reviewing the inner-loop process or the use of existing enterprise patterns and blueprints. I also did not review use of third-party plugins or tools available through GitHub. These would make for an interesting expansion of the demo. We also did not look at the creation of custom coding agents, which could be hosted in Microsoft Foundry; this is especially pertinent with the recent announcement of Anthropic models now being available to deploy in Foundry. Does today’s tooling mean that developers, QAs, and engineers are no longer required? Absolutely not (and if I am honest, I can’t see that changing any time soon). However, it is evidently clear that in the next 12 months, enterprises who reshape their SDLC (and any other business process) to become one augmented by agents will innovate faster, learn faster, and deliver faster, leaving organisations who resist this shift struggling to keep up.Beyond the Desktop: The Future of Development with Microsoft Dev Box and GitHub Codespaces
The modern developer platform has already moved past the desktop. We’re no longer defined by what’s installed on our laptops, instead we look at what tooling we can use to move from idea to production. An organisations developer platform strategy is no longer a nice to have, it sets the ceiling for what’s possible, an organisation can’t iterate it's way to developer nirvana if the foundation itself is brittle. A great developer platform shrinks TTFC (time to first commit), accelerates release velocity, and maybe most importantly, helps alleviate everyday frictions that lead to developer burnout. Very few platforms deliver everything an organization needs from a developer platform in one product. Modern development spans multiple dimensions, local tooling, cloud infrastructure, compliance, security, cross-platform builds, collaboration, and rapid onboarding. The options organizations face are then to either compromise on one or more of these areas or force developers into rigid environments that slow productivity and innovation. This is where Microsoft Dev Box and GitHub Codespaces come into play. On their own, each addresses critical parts of the modern developer platform: Microsoft Dev Box provides a full, managed cloud workstation. Dev Box gives developers a consistent, high-performance environment while letting central IT apply strict governance and control. Internally at Microsoft, we estimate that usage of Dev Box by our development teams delivers savings of 156 hours per year per developer purely on local environment setup and upkeep. We have also seen significant gains in other key SPACE metrics reducing context-switching friction and improving build/test cycles. Although the benefits of Dev Box are clear in the results demonstrated by our customers it is not without its challenges. The biggest challenge often faced by Dev Box customers is its lack of native Linux support. At the time of writing and for the foreseeable future Dev Box does not support native Linux developer workstations. While WSL2 provides partial parity, I know from my own engineering projects it still does not deliver the full experience. This is where GitHub Codespaces comes into this story. GitHub Codespaces delivers instant, Linux-native environments spun up directly from your repository. It’s lightweight, reproducible, and ephemeral ideal for rapid iteration, PR testing, and cross-platform development where you need Linux parity or containerized workflows. Unlike Dev Box, Codespaces can run fully in Linux, giving developers access to native tools, scripts, and runtimes without workarounds. It also removes much of the friction around onboarding: a new developer can open a repository and be coding in minutes, with the exact environment defined by the project’s devcontainer.json. That said, Codespaces isn’t a complete replacement for a full workstation. While it’s perfect for isolated project work or ephemeral testing, it doesn’t provide the persistent, policy-controlled environment that enterprise teams often require for heavier workloads or complex toolchains. Used together, they fill the gaps that neither can cover alone: Dev Box gives the enterprise-grade foundation, while Codespaces provides the agile, cross-platform sandbox. For organizations, this pairing sets a higher ceiling for developer productivity, delivering a truly hybrid, agile and well governed developer platform. Better Together: Dev Box and GitHub Codespaces in action Together, Microsoft Dev Box and GitHub Codespaces deliver a hybrid developer platform that combines consistency, speed, and flexibility. Teams can spin up full, policy-compliant Dev Box workstations preloaded with enterprise tooling, IDEs, and local testing infrastructure, while Codespaces provides ephemeral, Linux-native environments tailored to each project. One of my favourite use cases is having local testing setups like a Docker Swarm cluster, ready to go in either Dev Box or Codespaces. New developers can jump in and start running services or testing microservices immediately, without spending hours on environment setup. Anecdotally, my time to first commit and time to delivering “impact” has been significantly faster on projects where one or both technologies provide local development services out of the box. Switching between Dev Boxes and Codespaces is seamless every environment keeps its own libraries, extensions, and settings intact, so developers can jump between projects without reconfiguring or breaking dependencies. The result is a turnkey, ready-to-code experience that maximizes productivity, reduces friction, and lets teams focus entirely on building, testing, and shipping software. To showcase this value, I thought I would walk through an example scenario. In this scenario I want to simulate a typical modern developer workflow. Let's look at a day in the life of a developer on this hybrid platform building an IOT project using Python and React. Spin up a ready-to-go workstation (Dev Box) for Windows development and heavy builds. Launch a Linux-native Codespace for cross-platform services, ephemeral testing, and PR work. Run "local" testing like a Docker Swarm cluster, database, and message queue ready to go out-of-the-box. Switch seamlessly between environments without losing project-specific configurations, libraries, or extensions. 9:00 AM – Morning Kickoff on Dev Box I start my day on my Microsoft Dev Box, which gives me a fully-configured Windows environment with VS Code, design tools, and Azure integrations. I select my teams project, and the environment is pre-configured for me through the Dev Box catalogue. Fortunately for me, its already provisioned. I could always self service another one using the "New Dev Box" button if I wanted too. I'll connect through the browser but I could use the desktop app too if I wanted to. My Tasks are: Prototype a new dashboard widget for monitoring IoT device temperature. Use GUI-based tools to tweak the UI and preview changes live. Review my Visio Architecture. Join my morning stand up. Write documentation notes and plan API interactions for the backend. In a flash, I have access to my modern work tooling like Teams, I have this projects files already preloaded and all my peripherals are working without additional setup. Only down side was that I did seem to be the only person on my stand up this morning? Why Dev Box first: GUI-heavy tasks are fast and responsive. Dev Box’s environment allows me to use a full desktop. Great for early-stage design, planning, and visual work. Enterprise Apps are ready for me to use out of the box (P.S. It also supports my multi-monitor setup). I use my Dev Box to make a very complicated change to my IoT dashboard. Changing the title from "IoT Dashboard" to "Owain's IoT Dashboard". I preview this change in a browser live. (Time for a coffee after this hardwork). The rest of the dashboard isnt loading as my backend isnt running... yet. 10:30 AM – Switching to Linux Codespaces Once the UI is ready, I push the code to GitHub and spin up a Linux-native GitHub Codespace for backend development. Tasks: Implement FastAPI endpoints to support the new IoT feature. Run the service on my Codespace and debug any errors. Why Codespaces now: Linux-native tools ensure compatibility with the production server. Docker and containerized testing run natively, avoiding WSL translation overhead. The environment is fully reproducible across any device I log in from. 12:30 PM – Midday Testing & Sync I toggle between Dev Box and Codespaces to test and validate the integration. I do this in my Dev Box Edge browser viewing my codespace (I use my Codespace in a browser through this demo to highlight the difference in environments. In reality I would leverage the VSCode "Remote Explorer" extension and its GitHub Codespace integration to use my Codespace from within my own desktop VSCode but that is personal preference) and I use the same browser to view my frontend preview. I update the environment variable for my frontend that is running locally in my Dev Box and point it at the port running my API locally on my Codespace. In this case it was a web socket connection and HTTPS calls to port 8000. I can make this public by changing the port visibility in my Codespace. https://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/api/devices wss://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/ws This allows me to: Preview the frontend widget on Dev Box, connecting to the backend running in Codespaces. Make small frontend adjustments in Dev Box while monitoring backend logs in Codespaces. Commit changes to GitHub, keeping both environments in sync and leveraging my CI/CD for deployment to the next environment. We can see the Dev Box running local frontend and the Codespace running the API connected to each other, making requests and displaying the data in the frontend! Hybrid advantage: Dev Box handles GUI previews comfortably and allows me to live test frontend changes. Codespaces handles production-aligned backend testing and Linux-native tools. Dev Box allows me to view all of my files in one screen with potentially multiple Codespaces running in browser of VS Code Desktop. Due to all of those platform efficiencies I have completed my days goals within an hour or two and now I can spend the rest of my day learning about how to enable my developers to inner source using GitHub CoPilot and MCP (Shameless plug). The bottom line There are some additional considerations when architecting a developer platform for an enterprise such as private networking and security not covered in this post but these are implementation details to deliver the described developer experience. Architecting such a platform is a valuable investment to deliver the developer platform foundations we discussed at the top of the article. While in this demo I have quickly built I was working in a mono repository in real engineering teams it is likely (I hope) that an application is built of many different repositories. The great thing about Dev Box and Codespaces is that this wouldn’t slow down the rapid development I can achieve when using both. My Dev Box would be specific for the project or development team, pre loaded with all the tools I need and potentially some repos too! When I need too I can quickly switch over to Codespaces and work in a clean isolated environment and push my changes. In both cases any changes I want to deliver locally are pushed into GitHub (Or ADO), merged and my CI/CD ensures that my next step, potentially a staging environment or who knows perhaps *Whispering* straight into production is taken care of. Once I’m finished I delete my Codespace and potentially my Dev Box if I am done with the project, knowing I can self service either one of these anytime and be up and running again! Now is there overlap in terms of what can be developed in a Codespace vs what can be developed in Azure Dev Box? Of course, but as organisations prioritise developer experience to ensure release velocity while maintaining organisational standards and governance then providing developers a windows native and Linux native service both of which are primarily charged on the consumption of the compute* is a no brainer. There are also gaps that neither fill at the moment for example Microsoft Dev Box only provides windows compute while GitHub Codespaces only supports VS Code as your chosen IDE. It's not a question of which service do I choose for my developers, these two services are better together! *Changes have been announced to Dev Box pricing. A W365 license is already required today and dev boxes will continue to be managed through Azure. For more information please see: Microsoft Dev Box capabilities are coming to Windows 365 - Microsoft Dev Box | Microsoft LearnRun Playwright Tests on Cloud Browsers using Playwright Workspaces
This post walks through setting up and running Playwright UI and API tests on Azure Playwright Testing Service (Preview). It covers workspace setup, project configuration, remote browser execution, and viewing test reports and traces using Visual Studio or VS Code.
Proactive Cloud Ops with SRE Agent: Scheduled Checks for Cloud Optimization
The Cloud Optimization Challenge Your cloud environment is always changing: New features ship weekly Traffic patterns shift seasonally Costs creep up quietly Security best practices evolve Teams spin up resources and forget them It's Monday morning. You open the Azure portal. Everything looks... fine. But "fine" isn't great. That VM has been at 8% CPU for weeks. A Key Vault secret expires in 12 days. Nothing's broken. But security is drifting, costs are creeping, and capacity gaps are growing silently. The question isn't "is something broken?" it's "could this be better?" Four Pillars of Cloud Optimization Pillar What Teams Want The Challenge Security Stay compliant, reduce risk Config drift, legacy settings, expiring creds Cost Spend efficiently, justify budget Hard to spot waste across 100s of resources Performance Meet SLOs, handle growth Know when to scale before demand hits Availability Maximize uptime, build resilience Hidden dependencies, single points of failure Most teams check these sometimes. SRE Agent checks them continuously. Enter SRE Agent + Scheduled tasks SRE Agent can pull data from Azure Monitor, resource configurations, metrics, logs, traces, errors, cost data and analyze it on a schedule. If you use tools outside Azure (Datadog, PagerDuty, Splunk), you can connect those via MCP servers so the agent sees your full observability stack. My setup uses Azure-native sources. Here's how I wired it up. How I Set It Up: Step by Step Step 1: Create SRE Agent with Subscription Access I created an SRE Agent without attaching it to any specific resource group. Instead, I gave it Reader access at the subscription level. This lets the agent scan across all my resource groups for optimization opportunities. No resource group configuration needed. The agent builds a knowledge graph of everything VMs, storage accounts, Key Vaults, NSGs, web apps across the subscription. Step 2: Create and Upload My Organization Practices I created an org-practices.md file that defines what "good" looks like for my team: I uploaded this to SRE Agent's knowledge base. Now the agent knows our bar, not just Azure defaults. 👉 See my full org-practices.md Source repos for this demo: security-demoapp - App with intentional security misconfigurations costoptimizationapp - App with cost optimization opportunities Step 3: Connect to Teams Channel I connected SRE Agent to my team's Teams channel so findings land where we already work. Critical findings get immediate notifications. Warnings go into a daily digest. No more logging into separate dashboards. The insights come to us. Step 4: Connect Resource Groups to GitHub Repos Add the two resource groups to the SRE Agent and link the apps to their corresponding GitHub repos: Resource Group GitHub Repository rg-security-opt-demo security-demoapp rg-cost-opt-sreademo costoptimizationapp This enables the agent to create GitHub issues for findings linking violations directly to the repo responsible for that infrastructure. Step 5: Test with Prompts Before setting up automation, I tested the agent with manual prompts to make sure it was finding the right issues. The agent ran the checks, compared against my org-practices.md, and identified the issues. Security Check: Scan resource group "rg-security-opt-demo" for any violations of our security practices defined in org-practices.md in your knowledge base. list violations with severity and remediation steps. Make sure to check against all critical requirements and send message in teams channel with your findings and create an issue in the github repo https://github.com/dm-chelupati/security-demoapp.git Cost Check: Scan resource group "rg-cost-opt-sreademo" for any violations of our costpractices defined in org-practices.md in your knowledge base. list violations with severity and remediation steps. Make sure to check against all critical requirements and send message in teams channel with your findings and create an issue in the github repo https://github.com/dm-chelupati/costoptimizationapp.git Step 6: Check Output via GitHub Issues After running prompts, I checked GitHub. The agent had created issues. Each issue has the root cause, impact, and fix ready for the team to action or for Coding Agent to pick up and create a PR. 👉 See the actual issues created: Security findings issue Cost findings issue Step 7: Set Up Scheduled Triggers This is where it gets powerful. I configured recurring schedules: Weekly Security Check (Wednesdays 8 AM): Create a scheduled trigger that performs security practices checks against the org practices in knowledge base org-practices.md, creates github issue and send teams message on a weekly basis Wednesdays at 8 am UTC Weekly Cost Review (Mondays 8 AM): Create a scheduled trigger that performs cost practices checks against the org practices in knowledge base org-practices.md, creates github issue and send teams message on a weekly basis on Mondays at 8 am UTC Now optimization runs automatically. Every week, fresh findings land in GitHub Issues and Teams. Why Context Makes the SRE Agent Powerful Think about hiring a new SRE. They're excellent at their craft—they know Kubernetes, networking, Azure inside out. But on day one, they can't solve problems in your environment yet. Why? They don't have context: What are your SLOs? What's "acceptable" latency for your app? When do you rotate secrets? Monthly? Quarterly? Before each release? Which resources are production-critical vs. dev experiments? What's your tagging policy? Who owns what? How do you deploy? GitOps? Pipelines? Manual approvals? A great engineer becomes your great engineer once they learn how your team operates. SRE Agent works the same way. Out of the box, it knows Azure resource types, networking, best practices. But it doesn't know your bar. Is 20% CPU utilization acceptable or wasteful? Should secrets expire in 30 days or 90? Are public endpoints ever okay, or never? The more context you give the agent, your SLOs, your runbooks, your policies, the more it reasons like a team member who understands your environment, not just Azure in general. That's why Step 2 matters so much. When I uploaded our standards, the agent stopped checking generic Azure best practices and started checking our best practices. Bring your existing knowledge: You don't have to start from scratch. If your team's documentation already lives in Atlassian Confluence, SharePoint, or other tools, you can connect those via MCP servers. The agent pulls context from where your team already works, no need to duplicate content. Why This Matters Before this setup, optimization was a quarterly thing. Now it happens automatically: Before After Check security when audit requests it Daily automated posture check Find waste when finance complains Weekly savings report in Teams Discover capacity issues during incidents Scheduled headroom analysis Expire credentials and debug at 2 AM 30-day warning with exact secret names Optimization isn't a project anymore. It's a practice. Try It Yourself Create an SRE Agent with access to your subscription Upload your team's standards (security policies, cost thresholds, tagging rules) Set up a scheduled trigger, start with a daily security check Watch the first report land in Teams See what you've been missing while everything looked "fine." Learn More Azure SRE Agent documentation Azure SRE Agent blogs Azure SRE Agent community Azure SRE Agent home page Azure SRE Agent pricing Azure SRE Agent is currently in preview. Get StartedSecure Unique Default Hostnames Now GA for Functions and Logic Apps
We are pleased to announce that Secure Unique Default Hostnames are now generally available (GA) for Azure Functions and Logic Apps (Standard). This expands the security model previously available for Web Apps to the entire App Service ecosystem and provides customers with stronger, more secure, and standardized hostname behavior across all workloads. Why This Feature Matters Historically, App Service resources have used default hostname format such as: <SiteName>.azurewebsites.net While straightforward, this pattern introduced potential security risks, particularly in scenarios where DNS records were left behind after deleting a resource. In those situations, a different user could create a new resource with the same name and unintentionally receive traffic or bindings associated with the old DNS configuration, creating opportunities for issues such as subdomain takeover. Secure Unique Default Hostnames address this by assigning a unique, randomized, region‑scoped hostname to each resource, for example: <SiteName>-<Hash>.<Region>.azurewebsites.net This change ensures that: No other customer can recreate the same default hostname. Apps inherently avoid risks associated with dangling DNS entries. Customers gain a more secure, reliable baseline behavior across App Service. Adopting this model now helps organizations prepare for the long‑term direction of the platform while improving security posture today. What’s New: GA Support for Functions and Logic Apps With this release, both Azure Functions and Logic Apps (Standard) fully support the Secure Unique Default Hostname capability. This brings these services in line with Web Apps and ensures customers across all App Service workloads benefit from the same secure and consistent default hostname model. Azure CLI Support The Azure CLI for Web Apps and Function Apps now includes support for the “--domain-name-scope” parameter. This allows customers to explicitly specify the scope used when generating a unique default hostname during resource creation. Examples: az webapp create --domain-name-scope {NoReuse, ResourceGroupReuse, SubscriptionReuse, TenantReuse} az functionapp create --domain-name-scope {NoReuse, ResourceGroupReuse, SubscriptionReuse, TenantReuse} Including this parameter ensures that deployments consistently use the intended hostname scope and helps teams prepare their automation and provisioning workflows for the secure unique default hostname model. Why Customers Should Adopt This Now While existing resources will continue to function normally, customers are strongly encouraged to adopt Secure Unique Default Hostnames for all new deployments. Early adoption provides several important benefits: A significantly stronger security posture. Protection against dangling DNS and subdomain takeover scenarios. Consistent default hostname behavior as the platform evolves. Alignment with recommended deployment practices and modern DNS hygiene. This feature represents the current best practice for hostname management on App Service and adopting it now helps ensure that new deployments follow the most secure and consistent model available. Recommended Next Steps Enable Secure Unique Default Hostnames for all new Web Apps, Function Apps, and Logic Apps. Update automation and CLI scripts to include the --domain-name-scope parameter when creating new resources. Begin updating internal documentation and operational processes to reflect the new hostname pattern. Additional Resources For readers who want to explore the technical background and earlier announcements in more detail, the following articles offer deeper coverage of unique default hostnames: Public Preview: Creating Web App with a Unique Default Hostname This article introduces the foundational concepts behind unique default hostnames. It explains why the feature was created, how the hostname format works, and provides examples and guidance for enabling the feature when creating new resources. Secure Unique Default Hostnames: GA on App Service Web Apps and Public Preview on Functions This article provides the initial GA announcement for Web Apps and early preview details for Functions. It covers the security benefits, recommended usage patterns, and guidance on how to handle existing resources that were created without unique default hostnames. Conclusion Secure Unique Default Hostnames now provide a more secure and consistent default hostname model across Web Apps, Function Apps, and Logic Apps. This enhancement reduces DNS‑related risks and strengthens application security, and organizations are encouraged to adopt this feature as the standard for new deployments.From Vibe Coding to Working App: How SRE Agent Completes the Developer Loop
The Most Common Challenge in Modern Cloud Apps There's a category of bugs that drive engineers crazy: multi-layer infrastructure issues. Your app deploys successfully. Every Azure resource shows "Succeeded." But the app fails at runtime with a vague error like Login failed for user ''. Where do you even start? You're checking the Web App, the SQL Server, the VNet, the private endpoint, the DNS zone, the identity configuration... and each one looks fine in isolation. The problem is how they connect and that's invisible in the portal. Networking issues are especially brutal. The error says "Login failed" but the actual causes could be DNS, firewall, identity, or all three. The symptom and the root causes are in completely different resources. Without deep Azure networking knowledge, you're just clicking around hoping something jumps out. Now imagine you vibe coded the infrastructure. You used AI to generate the Bicep, deployed it, and moved on. When it breaks, you're debugging code you didn't write, configuring resources you don't fully understand. This is where I wanted AI to help not just to build, but to debug. Enter SRE Agent + Coding Agent Here's what I used: Layer Tool Purpose Build VS Code Copilot Agent Mode + Claude Opus Generate code, Bicep, deploy Debug Azure SRE Agent Diagnose infrastructure issues and create developer issue with suggested fixes in source code (app code and IaC) Fix GitHub Coding Agent Create PRs with code and IaC fix from Github issue created by SRE Agent Copilot builds. SRE Agent debugs. Coding Agent fixes. What I Built I used VS Code Copilot in Agent Mode with Claude Opus to create a .NET 8 Web App connected to Azure SQL via private endpoint: Private networking (no public exposure) Entra-only authentication Managed identity (no secrets) Deployed with azd up. All green. Then I tested the health endpoint: $ curl https://app-tsdvdfdwo77hc.azurewebsites.net/health/sql {"status":"unhealthy","error":"Login failed for user ''.","errorType":"SqlException"} Deployment succeeded. App failed. One error. How I Fixed It: Step by Step Step 1: Create SRE Agent with Azure Access I created an SRE Agent with read access to my Azure subscription. You can scope it to specific resource groups. The agent builds a knowledge graph of your resources and their dependencies visible in the Resource Mapping view below. Step 2: Connect GitHub to SRE Agent using GitHub MCP server I connected the GitHub MCP server so the agent could read my repository and create issues. Step 3: Create Sub Agent to analyze source code I created a sub-agent for analyzing source code using GitHub mcp tools. this lets SRE Agent understand not just Azure resources, but also the Bicep and source code files that created them. "you are expert in analyzing source code (bicep and app code) from github repos" Step 4: Invoke Sub-Agent to Analyze the Error In the SRE Agent chat, I invoked the sub-agent to diagnose the error I received from my app end point. It correlated the runtime error with the infrastructure configuration Step 5: Watch the SRE Agent Think and Reason SRE Agent analyzed the error by tracing code in Program.cs, Bicep configurations, and Azure resource relationships Web App, SQL Server, VNet, private endpoint, DNS zone, and managed identity. Its reasoning process worked through each layer, eliminating possibilities one by one until it identified the root causes. Step 6: Agent Creates GitHub Issue Based on its analysis, SRE Agent summarized the root causes and suggested fixes in a GitHub issue: Root Causes: Private DNS Zone missing VNet link Managed identity not created as SQL user Suggested Fixes: Add virtualNetworkLinks resource to Bicep Add SQL setup script to create user with db_datareader and db_datawriter roles Step 7: Merge the PR from Coding Agent Assign the Github issue to Coding Agent which then creates a PR with the fixes. I just reviewed the fix. It made sense and I merged it. Redeployed with azd up, ran the SQL script: curl -s https://app-tsdvdfdwo77hc.azurewebsites.net/health/sql | jq . { "status": "healthy", "database": "tododb", "server": "tcp:sql-tsdvdfdwo77hc.database.windows.net,1433", "message": "Successfully connected to SQL Server" } 🎉 From error to fix in minutes without manually debugging a single Azure resource. Why This Matters If you're a developer building and deploying apps to Azure, SRE Agent changes how you work: You don't need to be a networking expert. SRE Agent understands the relationships between Azure resources private endpoints, DNS zones, VNet links, managed identities. It connects dots you didn't know existed. You don't need to guess. Instead of clicking through the portal hoping something looks wrong, the agent systematically eliminates possibilities like a senior engineer would. You don't break your workflow. SRE Agent suggests fixes in your Bicep and source code not portal changes. Everything stays version controlled. Deployed through pipelines. No hot fixes at 2 AM. You close the loop. AI helps you build fast. Now AI helps you debug fast too. Try It Yourself Do you vibe code your app, your infrastructure, or both? How do you debug when things break? Here's a challenge: Vibe code a todo app with a Web App, VNet, private endpoint, and SQL database. "Forget" to link the DNS zone to the VNet. Deploy it. Watch it fail. Then point SRE Agent at it and see how it identifies the root cause, creates a GitHub issue with the fix, and hands it off to Coding Agent for a PR. Share your experience. I'd love to hear how it goes. Learn More Azure SRE Agent documentation Azure SRE Agent blogs Azure SRE Agent community Azure SRE Agent home page Azure SRE Agent pricingStop Running Runbooks at 3 am: Let Azure SRE Agent Do Your On-Call Grunt Work
Your pager goes off. It's 2:47am. Production is throwing 500 errors. You know the drill - SSH into this, query that, check these metrics, correlate those logs. Twenty minutes later, you're still piecing together what went wrong. Sound familiar? The On-Call Reality Nobody Talks About Every SRE, DevOps engineer, and developer who's carried a pager knows this pain. When incidents hit, you're not solving problems - you're executing runbooks. Copy-paste this query. Check that dashboard. Run these az commands. Connect the dots between five different tools. It's tedious. It's error-prone at 3am. And honestly? It's work that doesn't require human creativity but requires human time. What if an AI agent could do this for you? Enter Azure SRE Agent + Runbook Automation Here's what I built: I gave SRE Agent a simple markdown runbook containing the same diagnostic steps I'd run manually during an incident. The agent executes those steps, collects evidence, and sends me an email with everything I need to take action. No more bouncing between terminals. No more forgetting a step because it's 3am and your brain is foggy. What My Runbook Contains Just the basics any on-call would run: az monitor metrics – CPU, memory, request rates Log Analytics queries – Error patterns, exception details, dependency failures App Insights data – Failed requests, stack traces, correlation IDs az containerapp logs – Revision logs, app configuration That's it. Plain markdown with KQL queries and CLI commands. Nothing fancy. What the Agent Does Reads the runbook from its knowledge base Executes each diagnostic step Collects results and evidence Sends me an email with analysis and findings I wake up to an email that says: "CPU spiked to 92% at 2:45am, triggering connection pool exhaustion. Top exception: SqlException (1,832 occurrences). Errors correlate with traffic spike. Recommend scaling to 5 replicas." All the evidence. All the queries used. All the timestamps. Ready for me to act. How to Set This Up (6 Steps) Here's how you can build this yourself: Step 1: Create SRE Agent Create a new SRE Agent in the Azure portal. No Azure resource groups to configure. If your apps run on Azure, the agent pulls context from the incident itself. If your apps run elsewhere, you don't need Azure resource configuration at all. Step 2: Grant Reader Permission (Optional) If your runbooks execute against Azure resources, assign Reader role to the SRE Agent's managed identity on your subscription. This allows the agent to run az commands and query metrics. Skip this if your runbooks target non-Azure apps. Step 3: Add Your Runbook to SRE Agent's Knowledge base You already have runbooks, they're in your wiki, Confluence, or team docs. Just add them as .md files to the agent's knowledge base. To learn about other ways to link your runbooks to the agent, read this Step 4: Connect Outlook Connect the agent to your Outlook so it can send you the analysis email with findings. Step 5: Create a Subagent Create a subagent with simple instructions like: "You are an expert in triaging and diagnosing incidents. When triggered, search the knowledge base for the relevant runbook, execute the diagnostic steps, collect evidence, and send an email summary with your findings." Assign the tools the agent needs: RunAzCliReadCommands – for az monitor, az containerapp commands QueryLogAnalyticsByWorkspaceId – for KQL queries against Log Analytics QueryAppInsightsByResourceId – for App Insights data SearchMemory – to find the right runbook SendOutlookEmail – to deliver the analysis Step 6: Set Up Incident Trigger Connect your incident management tool - PagerDuty, ServiceNow, or Azure Monitor alerts and setup the incident trigger to the subagent. When an incident fires, the agent kicks off automatically. That's it. Your agentic workflow now looks like this: This Works for Any App, Not Just Azure Here's the thing: SRE Agent is platform agnostic. It's executing your runbooks, whatever they contain. On-prem databases? Add your diagnostic SQL. Custom monitoring stack? Add those API calls. The agent doesn't care where your app runs. It cares about following your runbook and getting you answers. Why This Matters Lower MTTR. By the time you're awake and coherent, the analysis is done. Consistent execution. No missed steps. No "I forgot to check the dependencies" at 4am. Evidence for postmortems. Every query, every result, timestamped and documented. Focus on what matters. Your brain should be deciding what to do not gathering data. The Bottom Line On-call runbook execution is the most common, most tedious, and most automatable part of incident response. It's grunt work that pulls engineers away from the creative problem-solving they were hired for. SRE Agent offloads that work from your plate. You write the runbook once, and the agent executes it every time, faster and more consistently than any human at 3am. Stop running runbooks. Start reviewing results. Try it yourself: Create a markdown runbook with your diagnostic queries and commands, add it to your SRE Agent's knowledge base, and let the agent handle your next incident. Your 3am self will thank you.
