How ARM Tracks Work That Takes Hours
By Arav Goyal, Joy Shah, Michael Cheng, Manik Sikka, Jenny Hunter, Johnson Shi Introduction When a user creates, updates, or deletes a resource in Azure, the request flows through Azure Resource Manager (ARM) before reaching the service that actually owns the resource. For operations that complete in milliseconds, the request and response fit cleanly into a single synchronous HTTP exchange. For operations that take seconds, minutes, or hours, this is not possible: HTTP connections cannot be held open that long, and the user's client needs a way to track the work asynchronously. ARM and Azure's resource providers (RPs) implement this through a standard long-running operation (LRO) protocol built on the Azure-AsyncOperation and Location HTTP headers, status URLs, and provisioning states. This post describes that protocol end-to-end and traces a request from the Portal or CLI all the way through to terminal completion. Key Takeaways All Azure control plane traffic (Portal, CLI, PowerShell, SDKs, REST API) is routed through ARM, which forwards requests to the appropriate resource provider. Operations that cannot complete inside a single HTTP request are returned as long-running operations, marked by an HTTP 201 or 202 response and a status URL the caller polls until completion. The two primary LRO patterns use the Azure-AsyncOperation header (returns operation status) and the Location header (returns the resource itself once complete). Both are guided by a Retry-After value when the resource provider supplies one. Clients should prefer the Azure-AsyncOperation URL when it is present, because the structured status response is more informative than the implicit "still 202" signal from polling Location alone. Many Azure resources also expose a provisioningState property that reaches a terminal value when the operation completes, providing a secondary signal in addition to the async operation status URL. Background: ARM as the Control Plane Azure Resource Manager is the deployment and management service for Azure. When a user issues a control plane request through any Azure interface (the Portal, the Azure CLI, PowerShell, an SDK, or a direct REST API call), the request reaches ARM at management.azure.com. ARM authenticates the request, authorizes it against the appropriate role assignments and policies, and then forwards it to the resource provider that owns the resource type in question. Resource providers are the Azure services that actually implement specific resource types. Microsoft.Compute provides virtual machines, Microsoft.Storage provides storage accounts, Microsoft.ContainerService provides managed Kubernetes clusters, and so on. Because every control plane request flows through this same path, the behavior described in this post applies regardless of which client the user is using. az group deployment create, a Bicep deployment from the Portal, and a direct PUT to management.azure.com all enter the system the same way. Synchronous and Asynchronous Operations Many control plane operations complete quickly enough to be handled synchronously. A GET request that reads the current state of a resource, for example, can usually return inline in tens of milliseconds. The user's client makes one request, receives one response with the requested data, and the interaction is finished. Other operations cannot complete this way. Provisioning a managed Kubernetes cluster, deploying a multi-resource template, or tearing down a private endpoint with downstream cleanup may take seconds, minutes, or hours of actual work on the resource provider's side. There are several reasons ARM cannot just hold a synchronous HTTP connection open for the duration of this work: Intermediate proxies and load balancers typically time out long-lived connections. Clients may go offline (a laptop closes, a network drops) while waiting. Holding a TCP connection open for an extended period consumes server resources for no useful purpose; the actual work happens elsewhere. To handle these cases, ARM and the resource providers implement a standard long-running operation protocol. The initial request returns immediately with a status code indicating the work has been accepted but is not yet done, along with one or more headers that tell the client where to check for status. The client then polls that status endpoint until the operation reaches a terminal state. The Long-Running Operation Protocol When a resource provider receives a request that will take longer than a synchronous response can accommodate, it returns an HTTP 201 Created or 202 Accepted response. The response includes one or both of two key headers that direct the caller to a status endpoint. The Azure-AsyncOperation header Azure-AsyncOperation contains a URL. When the client polls that URL, the response body is a structured representation of the operation's current state, including a status field. The status takes one of several values: An in-progress value such as InProgress or a resource-provider-specific equivalent. A terminal value: Succeeded, Failed, or Canceled. The client continues polling until the status field reaches a terminal value. Failed and canceled responses typically include an error field with structured detail about what went wrong. The Location header Location also contains a URL, but the semantics are different. While the operation is in progress, polling the Location URL returns another 202 Accepted response, often with a refreshed Retry-After value. Once the operation completes, polling the Location URL returns 200 OK with a terminal payload: the resource itself for a successful create or update (a PUT), or the action's result for a POST operation such as starting, stopping, or restarting a resource. Other terminal status codes are possible depending on the operation's outcome. Not every long-running operation returns an Azure-AsyncOperation header; some expose only Location. When both are present, clients should prefer the Azure-AsyncOperation URL, because the structured status response is more informative than the implicit "still 202" signal from Location-only polling. The Retry-After header Both patterns may be accompanied by a Retry-After header, an integer giving the resource provider's suggested interval (in seconds) before the next poll. Well-behaved clients honor this value. Ignoring it and polling faster than the resource provider has asked can trigger server-side throttling, at which point the client is no better off (and often worse off) than if it had waited. When Retry-After is absent, the client falls back to a default polling cadence determined by its own configuration. Provisioning State In addition to the operation-level status returned by the LRO protocol, many Azure resources expose a provisioningState property in their own resource manifest. When a client issues a GET on the resource itself (not the operation status URL), the response body contains the resource's current configuration along with a provisioningState field. The provisioning state moves through a predictable lifecycle: A transitional state during work: commonly Creating, Updating, or Deleting, sometimes with resource-provider-specific values. A terminal state once work completes: Succeeded, Failed, or Canceled. Where provisioningState is available, clients have two distinct ways to determine completion. They can poll the async operation URL, or they can poll the resource itself and watch for provisioningState to reach a terminal value. The async operation URL is the authoritative signal in either case; the resource manifest's provisioningState is a secondary observation point that can be useful when a client is already reading the resource for other reasons. The End-to-End Polling Chain Putting the pieces together, the lifecycle of a long-running operation from the client's perspective looks like this: User runs a command such as az aks create. The CLI sends a PUT request to management.azure.com/.../Microsoft.ContainerService/managedClusters/{name}. ARM authenticates and authorizes the request, then forwards it to the Microsoft.ContainerService resource provider. The resource provider accepts the work and returns 202 Accepted with Azure-AsyncOperation and/or Location headers, plus a Retry-After value. ARM forwards this response to the CLI. The CLI begins polling the status URL on the suggested interval. Each poll returns the current status, in progress or terminal. The resource provider continues its work in the background. Eventually the operation reaches a terminal state (Succeeded, Failed, or Canceled). The next poll after that returns the terminal status along with any final response body. The CLI reports completion to the user. Two ways to observe completion The sequence above shows the client polling the async operation status URL, which is the primary and authoritative completion signal. Where a resource also exposes provisioningState, the client has a second option. The two differ only in what the client polls and what comes back: Polling the async operation URL (Azure-AsyncOperation or Location) returns operation-level status directly. This is the path the LRO headers point to and the one to prefer. Polling the resource's provisioningState means issuing a GET on the resource itself and watching the provisioningState field reach a terminal value. This is useful when the client is already reading the resource for other reasons. Both observe the same underlying operation. They are not different operations or different code paths on the resource provider's side; they are two different endpoints a client can watch to learn the same thing. Closing The long-running operation protocol is one of those pieces of infrastructure that is invisible when it works. A user runs a command, waits, and eventually sees a result. Underneath, that simple experience rests on a well-defined contract: a 201 or 202 with a status URL, a set of headers that tell the client where and how often to check, a predictable set of terminal states, and an optional second signal through provisioningState. The contract is simple enough to describe in a single post and robust enough to handle everything from an eight-second deployment to a multi-hour cluster provision. The one part of the protocol that this post has treated as a given is the polling cadence: how often the client checks the status URL when no Retry-After value pins it. That cadence is more consequential than it looks. Every in-flight operation across the platform is being checked on repeatedly, and the interval between those checks determines how much work goes into useful status retrieval versus into repeatedly asking an operation that is not done yet whether it is done. Getting that cadence right, across a workload where some operations finish in seconds and others run for hours, is a genuinely interesting problem, and one worth a closer look another time.Designing for High Availability: The Operational Reference for Running a Geo-Replicated ACR
By Johnson Shi, Zoey (Zhuyu) Li, Huangli Wu Introduction Three of the most common questions we hear from enterprise teams running geo-replicated Azure Container Registries (ACR) are: "How do I control which region serves my traffic?" — When my AKS clusters are spread across regions, can I pin each one to its co-located replica, or am I stuck with however the global endpoint routes? "What happens during a regional incident — is failover automatic or do I have to act?" — If the registry in one region degrades, does the global endpoint reroute on its own, or do I need to manually disable the affected replica? "What happens after the region recovers — does traffic return on its own?" — Is there a cooldown, a quarantine, or any manual step before failback? We answer those head-on, then go deeper on the operational details that come up when you actually run a geo-replicated registry: authentication across endpoint switches, throttling under load concentration, eventual-consistency failure modes, home region outage scope, webhooks, and private endpoint interaction. We draw on the official geo-replication docs, the global endpoint health-aware failover blog, the regional endpoints engineering design implementation, the regional endpoints public preview and private preview announcements, and the ACR reference for various registry endpoints, . This post also draws notes from the ACR product team on roadmap items that aren't yet documented elsewhere. Key Takeaways Health-aware failover is automatic. When the registry in a region degrades, the global endpoint reroutes away from it on the order of minutes, evaluated per-registry. No customer action required. Failback is automatic too. Once health-aware failover marks a region healthy again, the global endpoint resumes routing to it. There is no cooldown period. Health-aware failover applies only to global endpoint operations. It does not apply to regional endpoints (you're talking to one replica, period) or to dedicated data endpoints (the redirect is per-region). Health-aware failover is not triggered by throttling. It responds to regional ACR service health and Azure infrastructure health, not HTTP 429 responses. Use regional endpoints to manage per-replica throttling. Regional endpoints (Step 2a) give you explicit per-region URLs for workloads that need affinity, capacity planning, push/pull consistency, troubleshooting, or client-side failover. Use myregistry.<region>.geo.azurecr.io . Regional endpoints are available on Premium SKU registries. For workloads that don't need pinning, do nothing (Step 2b). The global endpoint plus health-aware failover handles routing automatically. Re-authenticate when switching endpoints. Each global or regional endpoint is its own authenticated surface; re-auth via az acr login , SDK auth, or the Kubernetes ACR credential provider on endpoint change. Don't run a long-lived DNS cache for the global endpoint. ACR purges DNS server-side on disable and during failover; a long-lived client cache works against that. For production workloads, enable dedicated data endpoints for security and DNS predictability on layer downloads. ACR is working on bounded staleness consistency for cross-replica eventual-consistency failure modes; see the FAQ. Background What is ACR geo-replication? Geo-replication is a Premium SKU feature that turns a single ACR registry into a multi-region, multi-write service. Every geo-replica in every region is writable — you can push, pull, and delete from any of them — and content syncs asynchronously between replicas under an eventual consistency model. Per-push replication time scales with the size and number of images being pushed. Similarly, when creating a new geo-replica, the time to populate the new geo-replica scales with the total size of the registry. A geo-replicated registry exposes a global endpoint at myregistry.azurecr.io . Behind that endpoint, ACR uses an internal traffic manager to direct each request to the replica with the best network performance profile for the caller — usually the closest replica, but not always. When clients are equidistant from multiple replicas, or when the closest replica is experiencing Azure infrastructure degradation, requests may be routed elsewhere. A geo-replicated registry also exposes a regional endpoint at myregistry.<region>.geo.azurecr.io , which allows clients to pin API requests to a specific geo-replica in lieu of global endpoints, which has Azure-managed routing among geo-replicas. Zone redundancy is always enabled for geo-replicas in regions where Azure has multiple availability zones — in those regions, ACR automatically spreads replica data across multiple availability zones within each region to protect against zonal outages. Endpoints and data endpoints: what goes where A common point of confusion: when you push or pull, not every request goes to the same place. The registry endpoints (global endpoint and regional endpoints), as well as the data endpoint, do different jobs. Your choice of data endpoint configuration has real consequences for security and resilience. Two kinds of traffic flow during a typical pull: Registry API traffic — authentication, manifest reads/writes, tag resolution, referrers, repository operations, blob location lookups, listing, metadata. This is everything except the actual layer (blob) bytes. All these API requests go to the global endpoint ( myregistry.azurecr.io ) or, if you've pinned your clients to call these APIs to a specific geo-replica, a geo-replica's regional endpoint ( myregistry.<region>.geo.azurecr.io ). Behind the scenes, the global endpoint internally proxies these requests to a specific geo-replica. Layer (blob) downloads — when the client asks for a blob, the registry doesn't serve the bytes itself. It returns an HTTP 307 redirect to a regional data endpoint (separate endpoint from the global endpoint or regional endpoints), and the client follows the redirect to download the layer from that region. Where that 307 sends you depends on whether you've enabled the registry's dedicated data endpoints feature: Configuration Layer downloads redirect to Default (no dedicated data endpoints) *.blob.core.windows.net (the underlying Azure storage account) Dedicated data endpoints enabled myregistry.<region>.data.azurecr.io for the region you were routed to Private endpoints enabled myregistry.<region>.data.azurecr.io for the region you were routed to Regional by design. Dedicated data endpoints always land you on a specific geo-replica's data endpoint — there is no "global data endpoint." With the global endpoint as your registry endpoint, the 307 redirect picks the data endpoint for whichever region the global endpoint chose to serve you. With a regional endpoint pinned to a specific region, the 307 always redirects you to that same region's data endpoint — never cross-region. Why dedicated data endpoints matter. Dedicated data endpoints are a Premium SKU feature that exists primarily to address security and firewall scoping. By default, layer downloads redirect to *.blob.core.windows.net — a wildcard storage FQDN. Firewall rules to allow that wildcard either let all Azure storage accounts through or none of them, which raises data exfiltration concerns and isn't tightly scoped to your registry. Dedicated data endpoints replace the wildcard with a fully qualified domain in your registry's own domain — myregistry.<region>.data.azurecr.io — so firewall rules can be scoped tightly to your specific registry, in your specific regions. That same design choice can also make layer downloads more predictable during routing changes. With dedicated data endpoints, the data endpoint FQDN is known ahead of time and lives in the registry's domain — one predictable hostname per region, configured once. Without them, the layer download has to resolve a wildcard storage FQDN that points to whichever storage account the registry happens to have provisioned, which is a separate DNS resolution path with its own routing behavior and its own caching profile. Dedicated data endpoints simplify the DNS picture by aligning the data path with the registry path and keeping the entire pull experience inside one set of predictable, scoped FQDNs. For any geo-replicated registry where security and high availability matter, enable dedicated data endpoints. Note: Health-aware failover applies only to operations against the global endpoint, not to regional endpoints or dedicated data endpoints. Take note that health-aware failover only kicks in and directs traffic away from a geo-replica when an Azure region is experiencing significant infrastructure degradation. At this stage, it does not kick in to redirect traffic to another geo-replica if a client's data plane API requests are throttled. See the relevant section below for the full scope when health-aware auto failover kicks in or not. The three traffic control tools ACR geo-replication gives you three complementary tools for controlling where traffic lands. Each one solves a different class of problem, and customers most often run into trouble when they reach for the wrong one. We name them up front and use these names throughout the post: Tool Who controls it What it does Use cases Health-aware failover Platform (automatic) Reroutes the global endpoint away from a region whose registry can't reliably serve requests Regional incidents, automatic recovery Replica enable/disable for global routing Customer (manual) Excludes a specific replica from global endpoint routing without deleting it; data continues syncing DR rehearsals, planned maintenance, quarantining a replica without losing it Regional endpoints Customer (per request) Dedicated per-region URLs ( myregistry.<region>.geo.azurecr.io ) that bypass the internal traffic manager entirely Pinning AKS clusters to co-located replicas, push/pull consistency, capacity planning, troubleshooting, client-side failover Health-aware failover and replica enable/disable both act on the global endpoint. Regional endpoints are a separate URL surface that coexists with the global endpoint — enabling them does not disable the global endpoint myregistry.azurecr.io . You can use both simultaneously and choose per workload. The behavior in question When the registry in one region experiences a real degradation, there are three possible answers to "what happens?": (A) Nothing automatic. The customer must manually disable the affected region's endpoint to stop traffic from being routed there. (B) The system detects the regional front-door failure and reroutes within seconds. (C) A per-registry health evaluation detects the degradation and reroutes the global endpoint within minutes, with no customer action. After the region recovers, routing resumes automatically. The answer today is (C). Before health-aware failover, customers were stuck closer to (A) — the system could see whether the regional reverse proxy responded, but not whether the registry could actually serve real pull and push traffic end to end. Health-aware failover closes that gap. We walk through all three tools in the next section, in order: setting up geo-replication, using regional endpoints to pin specific workloads, keeping the global endpoint for everything else, the manual replica disable mechanism, re-enabling participation in global routing, and what to expect when health-aware failover triggers. Walkthrough The following steps assume an existing Premium SKU registry and the Azure CLI logged in. We use myregistry as the registry name, myrg as the resource group, and eastus as the home region. Substitute <your-registry> , <your-rg> , and <your-region> for your environment. Prerequisites A Premium SKU ACR registry (geo-replication requires Premium) Azure CLI ( az ) installed and logged in For regional endpoints (Step 2a): Azure CLI 2.86.0 or later. All regional endpoints commands ( --regional-endpoints , az acr show-endpoints , az acr login --endpoint ) are available natively in Azure CLI 2.86.0+. If you previously installed the acrregionalendpoint private preview CLI extension, uninstall it with az extension remove --name acrregionalendpoint to prevent conflicts with the built-in CLI commands. Step 1: Add a West US replica to a registry that lives in East US Geo-replication requires the Premium SKU. The create call below fails on Basic or Standard. # Confirm the registry is Premium az acr show --name myregistry --resource-group myrg \ --query sku.name --output tsv # Premium # Create a West US geo-replica az acr replication create --registry myregistry --location westus # Confirm both replicas are present az acr replication list --registry myregistry --output table NAME LOCATION PROVISIONING STATE STATUS REGION ENDPOINT ENABLED ------ ---------- -------------------- -------- ----------------------- eastus eastus Succeeded online True westus westus Succeeded online True Pushes and pulls continue working through the existing replica throughout initial sync. Because the registry is multi-region, multi-write, the existing replica keeps serving traffic while the new replica catches up in the background. Initial replica seeding time is a function of registry size — the total number and cumulative size of images already in the registry that need to be replicated to the new replica — not the size of any single image. Step 2a: Pin workloads to specific regions using regional endpoints Use regional endpoints when a workload needs explicit per-region control. The five common cases: Regional affinity — an AKS cluster in East US should pull from the East US replica, every time, without ever hopping to a more distant replica because of a network performance fluctuation. Predictable routing — workloads that need to know exactly which replica will serve them, for benchmarking, capacity planning, or in-region traffic SLAs. Push/pull consistency — pinning both ends of a publish-then-deploy flow to the same replica eliminates eventual-consistency races. Troubleshooting — reproducing an issue on a specific replica requires sending traffic to that specific replica. Client-side failover — customers with their own health checks and business rules want to implement failover on their own terms, on signals only they can see. Enable regional endpoints on the registry: az acr update -n myregistry -g myrg --regional-endpoints enabled When enabled, ACR automatically creates per-region login server URLs for every existing geo-replica. No per-region configuration is needed. Note: Regional endpoints can be enabled on any Premium SKU registry, even without geo-replication. A registry without geo-replication has a single geo-replica in the home region, which gets one regional endpoint URL. However, the feature is most useful when your registry has at least two geo-replicas, where you can pin different workloads to different replicas for routing control and capacity distribution. Push to a specific region using its regional endpoint: # Log in to the West US regional endpoint az acr login --name myregistry --endpoint westus # Tag and push using the regional endpoint URL docker tag myapp:v1 myregistry.westus.geo.azurecr.io/myapp:v1 docker push myregistry.westus.geo.azurecr.io/myapp:v1 Pin AKS deployments to their co-located replica by using regional endpoint URLs in the deployment manifest. The example below shows two clusters in different regions; each cluster references the regional endpoint for its own region's replica (assuming replicas exist in both eastus and westeurope ): # East US-based AKS cluster pulls from the East US replica apiVersion: apps/v1 kind: Deployment metadata: name: myapp-eastus spec: template: spec: containers: - name: myapp image: myregistry.eastus.geo.azurecr.io/myapp:v1 --- # West Europe-based AKS cluster pulls from the West Europe replica apiVersion: apps/v1 kind: Deployment metadata: name: myapp-westeurope spec: template: spec: containers: - name: myapp image: myregistry.westeurope.geo.azurecr.io/myapp:v1 This eliminates cross-region pulls when global routing would otherwise prefer a different replica for a given client, and it gives you a per-region traffic profile you can plan capacity against. Regional endpoint operational tips View all endpoints. Use az acr show-endpoints to see all endpoint URLs for your registry — global, regional (if enabled), and dedicated data endpoints (if enabled): az acr show-endpoints --name myregistry --resource-group myrg Import from a specific geo-replica. When importing images between registries, you can use a regional endpoint to import from a specific geo-replica of the source registry. This is useful when you want predictable network paths or need to import from a replica in a specific region: az acr import \ --name mydownstreamregistry \ --source myupstreamregistry.westeurope.geo.azurecr.io/myapp:v1 \ --image myapp:v1 Firewall rules for regional endpoints. If you use firewall rules, allow access to the following endpoints for each geo-replica that clients connect to: Endpoint Purpose myregistry.<region>.geo.azurecr.io Regional endpoint for registry operations myregistry.azurecr.io Global endpoint (if also used) myregistry.<region>.data.azurecr.io Layer downloads (if using private endpoints or dedicated data endpoints) *.blob.core.windows.net Layer downloads (if not using private endpoints or dedicated data endpoints) For the full list of endpoint types and FQDN patterns, see the ACR reference for various registry endpoints. DNS-based routing without changing manifests. If you don't want to maintain different deployment manifests per region, you can keep all manifests pointing to the global endpoint ( myregistry.azurecr.io ) and use software-defined networking or a regional traffic manager to resolve the global endpoint to the appropriate regional endpoint based on the originating region's traffic. This achieves the same co-location goals as regional endpoints — predictable routing and reduced latency — without embedding region-specific URLs in your deployment manifests. Step 2b: Keep using the global endpoint for everything else For workloads that don't need explicit pinning, do nothing. The global endpoint at myregistry.azurecr.io continues to work exactly as before, and the global endpoint plus health-aware failover gives you intelligent routing across replicas without configuration. ACR picks the best replica for each client based on network performance and reroutes during regional incidents. Regional endpoints coexist with the global endpoint — enabling them does not disable myregistry.azurecr.io . You can use both simultaneously and choose per workload, mixing pinned workloads (Step 2a) with workloads that ride the global endpoint (Step 2b) in the same registry. Step 3: Take a replica out of global endpoint routing Use this when you need to keep a replica alive but stop it from serving global-endpoint traffic — for DR rehearsals, planned maintenance, or troubleshooting an isolated replica. # Exclude the West US replica from global endpoint routing az acr replication update --registry myregistry --name westus \ --global-endpoint-routing false Confirm the change: az acr replication list --registry myregistry --output table NAME LOCATION PROVISIONING STATE STATUS REGION ENDPOINT ENABLED ------ ---------- -------------------- -------- ----------------------- eastus eastus Succeeded online True westus westus Succeeded online False Requests to myregistry.azurecr.io no longer route to West US. The replica still receives replicated content — and continues to replicate its own content out to other replicas — and storage quota and per-replica costs continue to accrue. If regional endpoints are enabled, the West US regional endpoint URL also continues to work; --global-endpoint-routing controls only the replica's participation in global endpoint routing. A note on naming. The CLI flag --global-endpoint-routing (on az acr replication update ) and the regional endpoints feature (enabled via az acr update --regional-endpoints enabled ) are two different things despite the similar names. --global-endpoint-routing controls whether a replica participates in global endpoint routing. The regional endpoints feature creates per-region URLs ( myregistry.<region>.geo.azurecr.io ) that bypass the global endpoint entirely. They are independent controls. In Azure CLI 2.86.0 and later, the old --region-endpoint-enabled flag has been renamed to --global-endpoint-routing . The old flag name is deprecated and will be removed in Azure CLI 2.87.0 (June 2026). If you have existing scripts or automation that use --region-endpoint-enabled , update them to use --global-endpoint-routing . CLI flags quick reference: Flag Scope Purpose --regional-endpoints Registry-level ( az acr create or az acr update ) Enables dedicated regional endpoint URLs ( myregistry.<region>.geo.azurecr.io ) for all geo-replicas. --global-endpoint-routing Per-geo-replica ( az acr replication create or az acr replication update ) Controls whether the global endpoint routes traffic to a specific geo-replica. Set to false to temporarily exclude a geo-replica from global routing. --data-endpoint-enabled Registry-level ( az acr create or az acr update ) Enables dedicated data endpoints ( myregistry.<region>.data.azurecr.io ) for layer blob downloads. Auto-enabled when at least one private endpoint is configured. This bidirectional sync during disable is intentional. When you re-enable the replica, every image pushed to the registry while the replica was disabled — from any region — is already present, so the replica can serve traffic immediately with no catch-up window. If we stopped syncing on disable, re-enabling would leave the replica with stale data and force a long catch-up before it could safely serve pulls. Step 4: Re-enable the replica to participate in global endpoint routing Re-enable the replica: az acr replication update --registry myregistry --name westus \ --global-endpoint-routing true NAME LOCATION PROVISIONING STATE STATUS REGION ENDPOINT ENABLED ------ ---------- -------------------- -------- ----------------------- eastus eastus Succeeded online True westus westus Succeeded online True There is no cooldown. The global endpoint resumes routing requests to the West US replica as soon as the change takes effect on ACR's side. Because data continued syncing while the replica was disabled (Step 3), the replica is immediately ready to serve pulls — no catch-up window. Note on DNS during disable/enable. When you take a replica out of global routing, ACR purges its own DNS records for that replica from the global endpoint on a fast path — there is no waiting on a published TTL on ACR's side. If clients run their own DNS cache for the global endpoint, however, those clients will keep resolving to the disabled replica until the client cache expires. We can't control client-side caches. The recommendation: do not run a long-lived DNS cache for the global endpoint. A short-lived DNS pin for the duration of a single push (covered in the DNS and Client-Side Considerations section) is fine and even helpful — but a long-lived DNS cache will make --global-endpoint-routing false look broken from the client's perspective. Step 5: What to expect when health-aware failover triggers Health-aware failover is automatic. ACR evaluates registry health on a per-registry basis, and when a registry in a region can't reliably serve requests, the global endpoint reroutes that registry's traffic to a healthy replica. There is no customer-invocable trigger — that's the point. End-to-end timing is on the order of minutes — fast enough to catch real regional degradation, slow enough to ride out transient errors that resolve on their own. DNS TTL may add additional propagation delay before all clients switch to the new region. Scope of health-aware failover. Health-aware failover applies only to operations against the global endpoint — the registry API calls (auth, get manifest, get tag, get referrers, get blob location). It evaluates health when those API calls come in; it does not trigger mid-operation. Two important consequences: Regional endpoints are not in scope. When you talk to a regional endpoint like myregistry.westus.geo.azurecr.io , you're talking to that one replica. There is no automatic reroute. If you've pinned a workload to a regional endpoint and that region degrades, you implement client-side failover by switching the workload to a different regional endpoint. Dedicated data endpoints are not in scope. Once a registry endpoint has redirected you to a dedicated data endpoint, you stay on that region's data endpoint for the duration of the layer download. There is no automatic reroute of an in-flight blob download. The region targeted by the redirect is decided up front by whichever registry endpoint served the blob-location call: the global endpoint chooses based on its per-registry health evaluation, and a regional endpoint always targets its own region. The signals you can use to confirm a failover is in progress: # Check replication status az acr replication list --registry myregistry --output table You can also check Resource Health for the registry in the Azure portal — navigate to your registry and select Resource health under the Help section to see platform-side degradation signals. You'll typically see: Increased pull latency as traffic shifts to a more distant replica Resource Health flagging known issues in the affected region Replication status indicating which replicas are online After the region recovers, the per-registry health evaluation marks it healthy again and the global endpoint resumes routing — automatic, no cooldown, no customer action. Note that health is evaluated per registry, not per region: if a degradation affects only a subset of registries in a region, only those registries are rerouted, and other registries in the same region continue to be served locally with no unnecessary latency penalty. Not triggered by throttling. Health-aware failover is DNS-based and responds to regional ACR service health and Azure infrastructure health. It does not reroute traffic based on HTTP 429 (throttling) responses. If a geo-replica is throttling your requests but the region's infrastructure is healthy, the global endpoint continues routing you to that geo-replica. To manage throttling, use regional endpoints to spread workloads across multiple geo-replicas for better capacity distribution. Note on long-running pushes during a failover. A multi-layer push that spans a failover boundary can land layers and the manifest on different replicas — exactly the failure mode that DNS bouncing produces during a single push. ACR is actively tightening health-aware failover behavior to minimize cross-replica scatter during these scenarios, and the recommendation today remains: pin pushes to a single replica via a regional endpoint when push/pull consistency matters. Common Questions Q1. Performance impact during initial replica creation on a live registry Because ACR is multi-region, multi-write, the existing replica continues serving pull and push traffic throughout the period when a new replica is being seeded. Replication is asynchronous and content propagates in the background; the time to populate a new geo-replica scales with the size of the registry — the cumulative number and total size of images already in the registry — not with any single image. The docs do not publish a quantified degradation percentage or a throttling window for this period, and they do not promise zero performance impact — the safe operating assumption for a live production registry is that existing replicas continue serving traffic normally, with the new replica catching up in the background. Q2. Restricted/updating state during initial sync There is no "restricted" state for the registry during normal replica creation. Writes, control-plane operations, and pushes/pulls against existing replicas continue normally. The only time configuration changes are unavailable is during a home region outage — see the relevant FAQ item later on for the full data-plane-versus-control-plane breakdown. Q3. Cooldown periods and non-straightforward failback scenarios There is no cooldown before failback, manual or automatic. Re-enabling a replica's participation in global endpoint routing takes effect immediately on ACR's side. Health-aware failover returns traffic to a region as soon as its per-registry health evaluation passes again. The failback case that is not seamless: if a recently pushed image has not yet replicated to the failover region, a pull from that region may not find the image until replication catches up. This is a function of eventual consistency, not failback timing — and it's part of a broader class of issues we cover in Q4. Q4. Common pull and push failure modes during the eventual-consistency window DNS bouncing during a single push is one well-known problem, but it isn't the only one. The eventual-consistency window between geo-replicas surfaces in several recurring failure modes worth knowing about: Push-then-immediate-pull-cross-region. Pushing myapp:v1 to one region and immediately pulling it from a different region can fail with manifest unknown until replication catches up. This shows up most painfully in CI/CD pipelines where one CI runner pushes an image and thousands of pods across other regions all try to pull from their local geo-replicas at the same time. Today, customers work around this with indeterminate sleeps before scheduling expensive compute, or with retry logic, or by waiting on a replication-complete signal — none of which is a clean planning story. Tag overwrite races. Pushing myapp:v1 , then re-pushing myapp:v1 shortly after with a fix (same tag, different digest), can leave different replicas resolving the same tag to different digests during the eventual-consistency window. Delete propagation. Deleting a tag or repository in one region takes some time to propagate to other replicas. Pulls from regions where the delete hasn't yet propagated can return the supposedly-deleted content. Mid-push failover scatter. A multi-layer push that spans a health-aware failover boundary or a DNS bouncing event can land layers on one replica and the manifest on another, surfacing as manifest validation errors or blob unknown on subsequent pulls. What ACR is doing about this. We're working on bounded staleness consistency for pushed images across all geo-replicas worldwide, which addresses these four failure modes directly. This will be covered in an upcoming blog post. If you're hitting eventual-consistency brittleness today and want to talk through your scenario, reach out to us on the Azure Container Registry GitHub repository — we want the customer signal to land in the design. Mitigations available today: Pin pushes to a single replica via a regional endpoint. Every sub-request in the push — login, blob uploads, manifest upload — goes to the same replica, eliminating the DNS bouncing and mid-push scatter classes entirely. Use a short-lived client-side DNS cache like dnsmasq scoped to the duration of a single push, only when you're not using regional endpoints. Do not run a long-lived DNS cache for the global endpoint — it interferes with --global-endpoint-routing false and with health-aware failover routing. Build retry logic into pulls that immediately follow a cross-region push. Either retry with backoff or check replication status with ACR webhooks before pulling. ACR can detect and notify you when an image or tag is available for pull in a geo-replica (say geo-replica B), after it has been pushed to another geo-replica (geo-replica A) and background replication has succeeded to geo-replica B. Design publish steps to be idempotent so retries triggered by mid-push failover are safe. Q5. Auth behavior across endpoint switches For safety, treat each global endpoint and each regional endpoint as its own authenticated surface. All registry APIs except the actual blob downloads (auth, manifests, tag resolution, referrers) flow through whichever endpoint you've chosen. If you switch from the global endpoint to a regional endpoint, or from one regional endpoint to another, re-authenticate. That means az acr login , fresh SDK auth, or — for AKS — letting the Kubernetes ACR credential provider handle re-auth, which it does automatically when the endpoint changes. Q6. Throttling under failover and pinning Throttling limits on registry API operations are per-replica, not per-registry. This has two operational implications: During health-aware failover, traffic that was spread across replicas can shift heavily onto whichever replicas remain in the global endpoint's routing pool. Capacity plan to spread traffic across two or three healthy replicas during a failover scenario rather than concentrating onto one — the global endpoint's routing already does this for you when multiple healthy replicas exist, but registries with only two regions configured can hit per-replica limits more easily during a failover. To mitigate, use regional endpoints to spread workloads across multiple geo-replicas and plan per-replica capacity. When pinning via regional endpoints (Step 2a), you concentrate traffic on whichever replica you've pinned to. If you've pinned all your AKS clusters to a single regional endpoint, you may hit that replica's per-region throttling limits at peak. Mitigations: pin different workloads to different regional endpoints across multiple regions for better topology mapping and capacity distribution, or use the global endpoint (Step 2b) for workloads where you don't need explicit pinning so ACR's routing can spread load. We're also working on improving the throttling metrics surfaced during health-aware failover events. Note: Health-aware failover does not reroute traffic based on HTTP 429 (throttling). If you're experiencing throttling but the region's infrastructure is healthy, the global endpoint continues routing you there. Use regional endpoints to explicitly spread load across replicas for capacity planning. Q7. Home region outage scope Geo-replication provides high availability for the data plane. During a home region outage, the control plane is unavailable, which means you can't create or delete replicas, modify network rules, or change replication settings until the home region recovers. ACR Tasks are also bound to the home region and don't run while it's unavailable. The data plane keeps working: Global endpoint continues routing pulls and pushes to healthy replicas. Regional endpoints continue working — you talk directly to specific replicas, and your client-side logic decides which region to use. Authentication, manifests, blob downloads, webhooks continue functioning through any healthy replica. The home region of a registry is fixed at creation and cannot be changed afterward. Microsoft's registry relocation guidance describes a redeployment procedure — creating a new registry in a different region — not an in-place change to an existing registry's home region. Note: If your registry uses a customer-managed key, review the key vault failover and redundancy guidance for maximum resilience. Key vault availability directly affects the registry's ability to encrypt and decrypt data. Q8. Webhooks during failover Webhooks fire from the replica that received the push. Because ACR also replicates content to other geo-replicas, webhooks fire from each geo-replica as the image syncs to it — so a single push results in webhook events from the receiving replica plus an event from each replica as replication completes. During a failover where pushes are routed to a different region, webhooks from those pushes fire from the new region; once the original region recovers and replication catches up, webhook events fire from there too. Webhook consumers should be designed to handle multiple events per pushed image and deduplicate as needed. Q9. Private endpoints with regional endpoints and dedicated data endpoints When a private endpoint is created against a registry, the private endpoint covers all of the registry's endpoint surfaces — the global endpoint, every regional endpoint (if regional endpoints are enabled), and every regional dedicated data endpoint. A single private endpoint in one VNet can reach the global endpoint (which routes you to a suitable replica), any regional endpoint in the same or a different region, and any region's dedicated data endpoint for blob downloads. The trade-off is private IP allocation: each endpoint surface consumes IPs in the VNet. With many replicas plus regional endpoints plus dedicated data endpoints all enabled, private endpoint creation can fail if the VNet runs out of available private IPs. IP address consumption per feature: Configuration IPs consumed per VNet Initial private endpoint (global endpoint + home region dedicated data endpoint) 2 Each geo-replication region added +1 (regional dedicated data endpoint) Regional endpoints enabled +1 per geo-replica Example: A registry with 3 geo-replicas and regional endpoints enabled consumes 7 private IPs per VNet: 1 (global) + 3 (data) + 3 (regional). Without regional endpoints, the same registry requires 4 private IPs: 1 (global) + 3 (data). Subnet sizing: Use at minimum a /27 (32 addresses) subnet for PE subnets on geo-replicated registries, and /24 where possible. To check how many private IPs are already consumed on a subnet: az network vnet subnet show \ --name <subnet-name> \ --vnet-name <vnet-name> \ --resource-group <resource-group> \ --query "{addressPrefix:addressPrefix, usedIPs:length(ipConfigurations || \`[]\`)}" \ --output table See the ACR private endpoints documentation for the full IP-allocation math and sizing guidance. Q10. Geo-replica creation stuck for private endpoint-enabled registries When creating a geo-replica for a registry that has private endpoints configured, the replica provisioning can get stuck in a Creating state if the identity performing the operation doesn't have sufficient permissions to create private endpoint networking resources. Solution: Manually delete the geo-replica that got stuck in the provisioning state. Ensure the identity has the permission Microsoft.Network/privateEndpoints/privateLinkServiceProxies/write before creating the geo-replica again. Also verify that every PE subnet connected to the registry has free IP capacity — if any PE subnet across any connected VNet does not have enough free IPs, the replication provisioning fails and rolls back. The replica appears briefly in a Creating state and then is removed. The resulting error does not identify which subnet or VNet is exhausted. Q11. Metrics, logs, and alerts for the three phases We map each phase to the signals available in the Monitoring Guidance section below. The headline: Resource Health (in the Azure portal) and az acr replication list give you the platform-side signals; Azure Monitor platform metrics are collected automatically, and resource logs require Diagnostic Settings to be enabled on the customer side. Behavior summary Scenario Automatic? Customer Action Required Notes Registry in a region degrades Yes None Health-aware failover; per-registry; minutes-scale; global endpoint operations only Region recovers after a degradation event Yes None No cooldown Pin AKS clusters to co-located replicas No Use regional endpoint URLs in deployment manifests (Step 2a) Coexists with global endpoint No pinning needed for most workloads Yes None — keep using myregistry.azurecr.io (Step 2b) Global endpoint plus health-aware failover Push/pull from the same replica (consistency) No Use a regional endpoint for both push and pull Eliminates DNS bouncing and mid-push scatter Capacity planning per region No Spread workloads across multiple regional endpoints Per-replica throttling; avoid concentrating on one replica DR rehearsal: take a replica out of global routing No az acr replication update --global-endpoint-routing false Data continues syncing both directions; costs continue accruing Re-enable replica participation in global routing No az acr replication update --global-endpoint-routing true No cooldown; replica is immediately ready Switch a workload between endpoints No Re-auth ( az acr login , SDK auth, or Kubernetes ACR credential provider) Each endpoint is its own authenticated surface Initial replica seeding on a live registry N/A None Existing replica continues serving traffic; seeding time scales with registry size Long-running push during a failover No Retry; design publishes to be idempotent Pin via regional endpoint to avoid mid-push scatter; ACR is tightening this behavior Pull of a recently pushed image from a different region No Wait for replication, retry with backoff, or check replication status Eventual consistency; bounded staleness consistency in development Home region outage Data plane: yes; control plane: no Use global or regional endpoints for data plane operations Control plane (replica config, network rules) requires home region DNS and Client-Side Considerations DNS bouncing during a single push is the most common geo-replication push problem in customer threads, and it warrants a section of its own. The failure mode. A docker push is a sequence of HTTP requests: blob uploads for each layer, then a manifest upload that references those layers by digest. If the Linux DNS resolver on the client doesn't cache myregistry.azurecr.io consistently for the duration of the push, individual sub-requests can resolve to different replicas. Because replication is eventually consistent, the manifest can land on a replica that doesn't yet have the layers it references, and the manifest validation fails. The two mitigations: Regional endpoints pin the push to a single replica end-to-end. Every sub-request — login, blob uploads, manifest upload — goes to the same replica. This is the cleanest fix and the one we recommend for any pipeline where push/pull consistency matters. A short-lived client-side DNS cache like dnsmasq scoped to the duration of a single push. For Linux VMs in Azure, follow the DNS name resolution options guidance. The pin should last the push and no longer. For other clients performing pushes, you can customize your stack's DNS resolver to have a similar short-lived DNS cache to pin the global endpoint's resolved DNS for only the duration of an image push operation. A note on long-lived DNS caching for the global endpoint. Don't run a long-lived DNS cache for myregistry.azurecr.io . ACR purges its own DNS records on the server side when a replica is taken out of global routing (Step 3) and during health-aware failover; a long-lived client-side cache will keep clients pointed at the old region after our purge, which makes both the manual disable mechanism and health-aware failover look broken from the client's perspective. Retry behavior: In-flight pushes during a failover may fail. Design publish steps to be idempotent so retries are safe. Pipelines that push in one region and immediately pull from a different region should retry with backoff or check replication status — eventual consistency means the pull may race ahead of replication. ACR is working on bounded staleness consistency that addresses this directly by enabling proxying (on ACR infrastructure) an image pull request from one geo-replica (if it does not have the image) to another geo-replica that has the image; see the relevant FAQ item. Note: Specific retry counts, back-off intervals, and push timeout values are application-layer decisions. The platform behavior is documented; the retry policy belongs to your client. Monitoring Guidance We map the three phases to the signals available from each source. Where a signal requires customer-side configuration, we flag it. Phase A: Initial replication (after creating a new replica) az acr replication list and az acr replication show — confirm the new replica reaches provisioningState: Succeeded and status: online , and view per-replica status. Azure Monitor platform metrics — push count, pull count, and other registry metrics are collected automatically and visible in the Azure portal under Metrics. No customer configuration is needed to view platform metrics. To export metrics or enable resource logs (detailed operation logs), configure Diagnostic Settings on the registry. Phase B: Failover (planned via replica disable, or automatic via health-aware failover) Per-replica regionEndpointEnabled state via az acr replication list — confirms whether a manual disable took effect, i.e. which replicas are currently eligible for global endpoint routing. Note: this flag reflects the manual configuration for configuring a geo-replica's global endpoint routing eligibility; it does not indicate whether health-aware failover has actively rerouted traffic away from a replica. Resource Health for the registry (in the Azure portal under Help > Resource health) — surfaces platform-side degradation signals during incidents. ACR does not yet expose a definitive "this region is currently serving your traffic" signal; Resource Health and client-side latency changes are the best available indicators. Pull latency from clients — increased latency from a more distant replica is the client-observable signal that traffic has rerouted. Azure Monitor platform metrics — visible per-region in the Azure portal Metrics blade. To export metrics or query them programmatically, enable Diagnostic Settings. Phase C: Failback (replica returns to global routing) az acr replication list — confirms regionEndpointEnabled: True (manual) or online status across all replicas (automatic). Pull latency normalizing as clients reach the recovered replica again. Resource Health clearing for the registry (visible in the Azure portal). Note: The health-aware failover blog calls out ongoing work to surface richer signals — including notifications for when routing changes and which region is currently serving your traffic. The signals listed above are what's available today. Pricing Considerations Storage billing vs. storage quota: Storage is billed per geo-replica — a 1 GiB image replicated to 5 geo-replicas is charged as 5 GiB of storage (1 GiB × 5 geo-replicas). However, storage quota (the tier's maximum storage limit) counts the image only once — the same 1 GiB image counts as 1 GiB toward your tier's maximum, not 5 GiB. Data transfer: Geo-replication can reduce costs by enabling in-region image pushes and pulls, which avoids cross-region data transfer charges during these push or pull operations. However, cross-region data transfer charges still apply when ACR replicates pushed content to other geo-replicas as part of eventual consistency. Disabled replicas still cost: When you take a replica out of global routing with --global-endpoint-routing false , storage and per-replica costs continue accruing because data continues syncing bidirectionally. For more information, see ACR pricing. Cleanup Run these commands to undo the walkthrough setup. Order matters: disable regional endpoints before deleting replicas, since regional endpoint URLs depend on which replicas exist. # Disable regional endpoints if you enabled them in Step 2a az acr update -n myregistry -g myrg --regional-endpoints disabled # Re-enable any replicas you disabled in Step 3 (no-op if already enabled) az acr replication update --registry myregistry --name westus \ --global-endpoint-routing true # Delete the West US replica created in Step 1 az acr replication delete --registry myregistry --name westus # Confirm only the home region replica remains az acr replication list --registry myregistry --output table Note: Replica deletion is a control-plane operation that requires the home region to be available. During a home region outage, replica configuration cannot be modified. Summary Table Question Answer When should I use regional endpoints vs the global endpoint? Use regional endpoints (Step 2a) for workloads that need affinity, predictable routing, push/pull consistency, troubleshooting, or client-side failover. Use the global endpoint (Step 2b) for everything else and let health-aware failover handle routing. What should I enable for secure, resilient layer downloads? Enable dedicated data endpoints. They scope firewall rules tightly to your registry and replace wildcard storage DNS with predictable per-region FQDNs. How do I avoid DNS-bouncing manifest validation failures on push? Pin pushes to a single replica via a regional endpoint. A short-lived client-side dnsmasq for the push duration is also fine if you're not using regional endpoints. Should I run a long-lived DNS cache for the global endpoint? No. ACR purges DNS server-side on disable and during failover; client-side caching works against that. Do I need to re-auth when switching endpoints? Yes. Each global or regional endpoint is its own authenticated surface. az acr login , SDK auth, or the Kubernetes ACR credential provider handles the re-auth. What happens during a home region outage? Data plane keeps working through any replica via the global endpoint or regional endpoints. Control plane operations (replica configuration, network rules) are unavailable until the home region recovers. The home region is fixed at registry creation. What's ACR doing about eventual-consistency pain? Bounded staleness consistency for cross-replica pushed images is in development and will be covered in an upcoming blog post. Reach out via GitHub if you want to share your scenario. For the full automation matrix — what's automatic, what requires customer action, and what to expect for each scenario — see the behavior summary above. If you have further questions about ACR geo-replication routing, pinning, capacity planning, eventual consistency, or failover behavior, reach out to us on the Azure Container Registry GitHub repository or file feedback through the Azure portal.Deploying to Azure Web App from Azure DevOps Using UAMI
TOC UAMI Configuration App Configuration Azure DevOps Configuration Logs UAMI Configuration Create a User Assigned Managed Identity with no additional configuration. This identity will be mentioned in later steps, especially at Object ID. App Configuration On an existing Azure Web App, enable Diagnostic Settings and configure it to retain certain types of logs, such as Access Audit Logs. These logs will be discussed in the final section of this article. Next, navigate to Access Control (IAM) and assign the previously created User Assigned Managed Identity the Website Contributor role. Azure DevOps Configuration Go to Azure DevOps → Project Settings → Service Connections, and create a new ARM (Azure Resource Manager) connection. While creating the connection: Select the corresponding User Assigned Managed Identity Grant it appropriate permissions at the Resource Group level During this process, you will be prompted to sign in again using your own account. This authentication will later be reflected in the deployment logs discussed below. Assuming the following deployment template is used in the pipeline, you will notice that additional steps appear in the deployment process compared to traditional service principal–based authentication. Logs A few minutes after deployment, related log records will appear. In the AppServiceAuditLogs table, you can observe that the deployment initiator is shown as the Object ID from UAMI, and the Source is listed as Azure (DevOps). This indicates that the User Assigned Managed Identity is authorized under my user context, while the deployment action itself is initiated by Azure DevOps.
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 pricing
Azure Logic App workflow (Standard) Resubmit and Retry
Hello Experts, A workflow is scheduled to run daily at a specific time and retrieves data from different systems using REST API Calls (8-9). The data is then sent to another system through API calls using multiple child flows. We receive more than 1500 input data, and for each data, an API call needs to be made. During the API invocation process, there is a possibility of failure due to server errors (5xx) and client errors (4xx). To handle this, we have implemented a "Retry" mechanism with a fixed interval. However, there is still a chance of flow failure due to various reasons. Although there is a "Resubmit" feature available at the action level, I cannot apply it in this case because we are using multiple child workflows and the response is sent back from one flow to another. Is it necessary to utilize the "Resubmit" functionality? The Retry Functionality has been developed to handle any Server API errors (5xx) that may occur with Connectors (both Custom and Standard), including client API errors 408 and 429. In this specific scenario, it is reasonable to attempt retrying or resubmitting the API Call from the Azure Logic Apps workflow. Nevertheless, there are other situations where implementing the retry and resubmit logic would result in the same error outcome. Is it acceptable to proceed with the Retry functionality in this particular scenario? It would be highly appreciated if you could provide guidance on the appropriate methodology. Thanks -SriReimagining AI Ops with Azure SRE Agent: New Automation, Integration, and Extensibility features
Azure SRE Agent offers intelligent and context aware automation for IT operations. Enhanced by customer feedback from our preview, the SRE Agent has evolved into an extensible platform to automate and manage tasks across Azure and other environments. Built on an Agentic DevOps approach - drawing from proven practices in internal Azure operations - the Azure SRE Agent has already saved over 20,000 engineering hours across Microsoft product teams operations, delivering strong ROI for teams seeking sustainable AIOps. An Operations Agent that adapts to your playbooks Azure SRE Agent is an AI powered operations automation platform that empowers SREs, DevOps, IT operations, and support teams to automate tasks such as incident response, customer support, and developer operations from a single, extensible agent. Its value proposition and capabilities have evolved beyond diagnosis and mitigation of Azure issues, to automating operational workflows and seamless integration with the standards and processes used in your organization. SRE Agent is designed to automate operational work and reduce toil, enabling developers and operators to focus on high-value tasks. By streamlining repetitive and complex processes, SRE Agent accelerates innovation and improves reliability across cloud and hybrid environments. In this article, we will look at what’s new and what has changed since the last update. What’s New: Automation, Integration, and Extensibility Azure SRE Agent just got a major upgrade. From no-code automation to seamless integrations and expanded data connectivity, here’s what’s new in this release: No-code Sub-Agent Builder: Rapidly create custom automations without writing code. Flexible, event-driven triggers: Instantly respond to incidents and operational changes. Expanded data connectivity: Unify diagnostics and troubleshooting across more data sources. Custom actions: Integrate with your existing tools and orchestrate end-to-end workflows via MCP. Prebuilt operational scenarios: Accelerate deployment and improve reliability out of the box. Unlike generic agent platforms, Azure SRE Agent comes with deep integrations, prebuilt tools, and frameworks specifically for IT, DevOps, and SRE workflows. This means you can automate complex operational tasks faster and more reliably, tailored to your organization’s needs. Sub-Agent Builder: Custom Automation, No Code Required Empower teams to automate repetitive operational tasks without coding expertise, dramatically reducing manual workload and development cycles. This feature helps address the need for targeted automation, letting teams solve specific operational pain points without relying on one-size-fits-all solutions. Modular Sub-Agents: Easily create custom sub-agents tailored to your team’s needs. Each sub-agent can have its own instructions, triggers, and toolsets, letting you automate everything from outage response to customer email triage. Prebuilt System Tools: Eliminate the inefficiency of creating basic automation from scratch, and choose from a rich library of hundreds of built-in tools for Azure operations, code analysis, deployment management, diagnostics, and more. Custom Logic: Align automation to your unique business processes by defining your automation logic and prompts, teaching the agent to act exactly as your workflow requires. Flexible Triggers: Automate on Your Terms Invoke the agent to respond automatically to mission-critical events, not wait for manual commands. This feature helps speed up incident response and eliminate missed opportunities for efficiency. Multi-Source Triggers: Go beyond chat-based interactions, and trigger the agent to automatically respond to Incident Management and Ticketing systems like PagerDuty and ServiceNow, Observability Alerting systems like Azure Monitor Alerts, or even on a cron-based schedule for proactive monitoring and best-practices checks. Additional trigger sources such as GitHub issues, Azure DevOps pipelines, email, etc. will be added over time. This means automation can start exactly when and where you need it. Event-Driven Operations: Integrate with your CI/CD, monitoring, or support systems to launch automations in response to real-world events - like deployments, incidents, or customer requests. Vital for reducing downtime, it ensures that business-critical actions happen automatically and promptly. Expanded Data Connectivity: Unified Observability and Troubleshooting Integrate data, enabling comprehensive diagnostics and troubleshooting and faster, more informed decision-making by eliminating silos and speeding up issue resolution. Multiple Data Sources: The agent can now read data from Azure Monitor, Log Analytics, and Application Insights based on its Azure role-based access control (RBAC). Additional observability data sources such as Dynatrace, New Relic, Datadog, and more can be added via the Remote Model Context Protocol (MCP) servers for these tools. This gives you a unified view for diagnostics and automation. Knowledge Integration: Rather than manually detailing every instruction in your prompt, you can upload your Troubleshooting Guide (TSG) or Runbook directly, allowing the agent to automatically create an execution plan from the file. You may also connect the agent to resources like SharePoint, Jira, or documentation repositories through Remote MCP servers, enabling it to retrieve needed files on its own. This approach utilizes your organization’s existing knowledge base, streamlining onboarding and enhancing consistency in managing incidents. Azure SRE Agent is also building multi-agent collaboration by integrating with PagerDuty and Neubird, enabling advanced, cross-platform incident management and reliability across diverse environments. Custom Actions: Automate Anything, Anywhere Extend automation beyond Azure and integrate with any tool or workflow, solving the problem of limited automation scope and enabling end-to-end process orchestration. Out-of-the-Box Actions: Instantly automate common tasks like running azcli, kubectl, creating GitHub issues, or updating Azure resources, reducing setup time and operational overhead. Communication Notifications: The SRE Agent now features built-in connectors for Outlook, enabling automated email notifications, and for Microsoft Teams, allowing it to post messages directly to Teams channels for streamlined communication. Bring Your Own Actions: Drop in your own Remote MCP servers to extend the agent’s capabilities to any custom tool or workflow. Future-proof your agentic DevOps by automating proprietary or emerging processes with confidence. Prebuilt Operations Scenarios Address common operational challenges out of the box, saving teams time and effort while improving reliability and customer satisfaction. Incident Response: Minimize business impact and reduce operational risk by automating detection, diagnosis, and mitigation of your workload stack. The agent has built-in runbooks for common issues related to many Azure resource types including Azure Kubernetes Service (AKS), Azure Container Apps (ACA), Azure App Service, Azure Logic Apps, Azure Database for PostgreSQL, Azure CosmosDB, Azure VMs, etc. Support for additional resource types is being added continually, please see product documentation for the latest information. Root Cause Analysis & IaC Drift Detection: Instantly pinpoint incident causes with AI-driven root cause analysis including automated source code scanning via GitHub and Azure DevOps integration. Proactively detect and resolve infrastructure drift by comparing live cloud environments against source-controlled IaC, ensuring configuration consistency and compliance. Handle Complex Investigations: Enable the deep investigation mode that uses a hypothesis-driven method to analyze possible root causes. It collects logs and metrics, tests hypotheses with iterative checks, and documents findings. The process delivers a clear summary and actionable steps to help teams accurately resolve critical issues. Incident Analysis: The integrated dashboard offers a comprehensive overview of all incidents managed by the SRE Agent. It presents essential metrics, including the number of incidents reviewed, assisted, and mitigated by the agent, as well as those awaiting human intervention. Users can leverage aggregated visualizations and AI-generated root cause analyses to gain insights into incident processing, identify trends, enhance response strategies, and detect areas for improvement in incident management. Inbuilt Agent Memory: The new SRE Agent Memory System transforms incident response by institutionalizing the expertise of top SREs - capturing, indexing, and reusing critical knowledge from past incidents, investigations, and user guidance. Benefit from faster, more accurate troubleshooting, as the agent learns from both successes and mistakes, surfacing relevant insights, runbooks, and mitigation strategies exactly when needed. This system leverages advanced retrieval techniques and a domain-aware schema to ensure every on-call engagement is smarter than the last, reducing mean time to resolution (MTTR) and minimizing repeated toil. Automatically gain a continuously improving agent that remembers what works, avoids past pitfalls, and delivers actionable guidance tailored to the environment. GitHub Copilot and Azure DevOps Integration: Automatically triage, respond to, and resolve issues raised in GitHub or Azure DevOps. Integration with modern development platforms such as GitHub Copilot coding agent increases efficiency and ensures that issues are resolved faster, reducing bottlenecks in the development lifecycle. Ready to get started? Azure SRE Agent home page Product overview Pricing Page Pricing Calculator Pricing Blog Demo recordings Deployment samples What’s Next? Give us feedback: Your feedback is critical - You can Thumbs Up / Thumbs Down each interaction or thread, or go to the “Give Feedback” button in the agent to give us in-product feedback - or you can create issues or just share your thoughts in our GitHub repo at https://github.com/microsoft/sre-agent. We’re just getting started. In the coming months, expect even more prebuilt integrations, expanded data sources, and new automation scenarios. We anticipate continuous growth and improvement throughout our agentic AI platforms and services to effectively address customer needs and preferences. Let us know what Ops toil you want to automate next!Who Created This Azure Resource? Here's How to Find Out
One of the most common questions Azure customers and administrators ask is: “How do I know who created this resource?” If you’ve ever been in charge of managing a large subscription with dozens (or even thousands) of resources, you know how important it is to answer this question quickly. Whether it’s for troubleshooting, governance, or compliance, tracking the origin of a resource can save time and reduce confusion. The good news: Azure makes this information available. You just need to know where to look. Step 1: Open the Resource Overview Navigate to the Overview page of the resource in question. This gives you the usual metadata like resource group, subscription, location, login server, and provisioning state. At first glance, however, you won’t see who created the resource. That information isn’t shown in the overview fields. Step 2: Switch to JSON View On the Overview page, look for the link labeled “JSON View” in the top right corner. Clicking this opens the full resource definition in JSON format. Step 3: Scroll to the systemData Section Within the JSON, scroll until you find the systemData object. This is where Azure tracks metadata about the resource lifecycle. Here’s what you’ll find: "systemData": { "createdBy": "someuser@domain.com", "createdByType": "User", "createdAt": "2025–05–20T19:50:33.1511397Z", "lastModifiedBy": "someuser@domain.com", "lastModifiedByType": "User", "lastModifiedAt": "2025–05–20T19:50:33.1511397Z" } What This Tells You createdBy → The user or service principal that created the resource. createdByType → Whether it was created by a human user, managed identity, or another Azure service. createdAt → The exact timestamp of creation (UTC). lastModifiedBy, lastModifiedByType, and lastModifiedAt → Useful if the resource was updated after creation. This metadata gives you clear visibility into who provisioned the resource and when. Why It Matters Governance — Understand ownership and responsibility. Troubleshooting — Track down configuration changes. Compliance & Auditing — Satisfy requirements for accountability in your cloud environment. By making the systemData object part of your standard investigation checklist, you’ll save yourself the guesswork the next time you’re wondering, “Who created this resource?”
FSI Knowledge Mining and Intelligent Document Process Reference Architecture
FSI customers such as insurance companies and banks rely on their vast amounts of data to provide sometimes hundreds of individual products to their customers. From assessing product suitability, underwriting, fraud investigations, and claims handling, many employees and applications depend on accessing this data to do their jobs efficiently. Since the capabilities of GenAI have been realised, we have been helping our customers in this market transform their business with unified systems that simplify access to this data and speed up the processing times of these core tasks, while remaining compliant with the numerous regulations that govern the FSI space. Combining the use of Knowledge Mining with Intelligent Document processing provides a powerful solution to reduce the manual effort and inefficacies of ensuring data integrity and retrieval across the many use cases that most of our customers face daily. What is Knowledge Mining and Intelligent Document Processing? Knowledge Mining is a process that transforms large, unstructured data sets into searchable knowledge stores. Traditional search methods often rely on keyword matching, which can miss the context of the information. In contrast, knowledge mining uses advanced techniques like natural language processing (NLP) to understand the context and meaning behind the data, providing a robust searching mechanism that can look across all these data sources, understand the relationships between the data therefore providing more accurate and relevant results. Intelligent Document Processing (IDP) is a workflow automation technology designed to scan, read, extract, categorise, and organise meaningful information from large streams of data. Its primary function is to extract valuable information from extensive data sets without human input, thereby increasing processing speed and accuracy while reducing costs. By leveraging a combination of Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), and Natural Language Processing (NLP), IDP handles both structured and unstructured documents. By ensuring that the processed data meets the "gold standard" - structured, complete, and compliant - IDP helps organizations maintain high-quality, reliable, and actionable data. The Power of Knowledge Mining and Intelligent Document Processing as a Unified Solution Knowledge Mining excels at quickly responding to natural language queries, providing valuable insights and making previously unsearchable data accessible. At the same time, IDP ensures that the processed data meets the "gold standard"—structured, complete, and compliant—making it both reliable and actionable. Together, these technologies empower organisations to harness the full potential of their data, driving better decision-making and improved efficiency. __________________________________________________________________ Meet Alex: A Day in the Life of a Fraud Case Worker Responsibilities: Investigate potential fraud cases by manually searching across multiple systems. Read and analyse large volumes of information to filter out relevant data. Ensure compliance with regulatory requirements and maintain data accuracy. Prepare detailed reports on findings and recommendations. Lost in Data: The Struggles of Manual Fraud Investigation Alex receives a new fraud case and starts by manually searching through multiple systems to gather information. This process takes several hours, and Alex has to read through numerous documents and emails to filter out relevant data. The inconsistent data formats and locations make it challenging to ensure accuracy. By the end of the day, Alex is exhausted and has only made limited progress on the case. Effortless Efficiency: Fraud Investigation Transformed with Knowledge Mining and IDP Alex receives a new fraud case and needs to gather all relevant information quickly. Instead of manually searching through multiple systems, Alex inputs the following natural language query into the unified system: "Show me all documents, emails, and notes related to the recent transactions of client X that might indicate fraudulent activity." The system quickly retrieves and presents a comprehensive summary of all relevant documents, emails, and notes, ensuring that the data is structured, complete, and compliant. This allows Alex to focus on analysing the data and making informed decisions, significantly improving the efficiency and accuracy of the investigation. How has Knowledge Mining and IDP transformed Alex's role? Before implementing Knowledge Mining and Intelligent Document Processing, Alex faced a manual process of searching across multiple systems to gather information. This was time-consuming and labour-intensive, often leading to delays in investigations. The overwhelming volume of data from various sources made it difficult to filter out relevant information, and the inconsistent data formats and locations increased the risk of errors. This high workload not only reduced Alex's efficiency but also led to burnout and decreased job satisfaction. However, with the introduction of a unified system powered by Knowledge Mining and IDP, these challenges were significantly mitigated. Automated searches using natural language queries allowed Alex to quickly find relevant information, while IDP ensured that the data processed was structured, complete, and compliant. This unified system provided a comprehensive view of the data, enabling Alex to make more informed decisions and focus on higher-value tasks, ultimately improving productivity and job satisfaction. ____________________________________________________________________ Example Architecture Knowledge Mining Users can interact with the system through a portal on the customer’s front-end of choice. This will serve as the entry point for submitting queries and accessing the knowledge mining service. Front-end options could include web apps, container services or serverless integrations. Azure AI Search provides powerful RAG capabilities. Meanwhile, Azure Open AI provides access to large language models to summarise responses. These services combined will take the user’s query to search the knowledge base and return relevant information which can be augmented as required. Prompt engineering can provide customisation to how the data is returned. You define what the data sources your Azure AI Search will consume. This can be Azure storage services or other data repositories. Data that meets a pre-defined gold standard is queried by Azure AI Search and relevant data is returned to the user. Gold standard data could be based on compliance or business needs. Power BI can be used to create analytical reports based on the data retrieved and processed. This step involves visualising the data in an interactive and user-friendly manner, allowing users to gain insights and make data-driven decisions. Intelligent Document Processing (Optional) Azure Data Factory is a data integration service that allows you to create workflows for data movement and transforming data at scale. This business data can be easily ingested to your Azure data storage solutions using pre-built connectors. This event driven approach ensures that as new data is generated, it can automatically be processed and ready for use in your knowledge mining solution. Data can be transformed using Functions apps and Azure OpenAI. Through prompt engineering, the large language model (LLM) can highlight specific issues in the documents, such as grammatical errors, irrelevant content, or incomplete information. The LLM can then be used to rewrite text to improve clarity and accuracy, add missing information, or reformat content to adhere to guidelines. Transformed data is stored as gold standard data. ____________________________________________________________________ Additional Cloud Considerations Networking VNETs (Virtual Networks) are a fundamental component of cloud infrastructure that enable secure and isolated networking configurations within a cloud environment. They allow different resources, such as virtual machines, databases, and services, to communicate with each other securely. Virtual networks ensure that services such as Azure AI Search, Azure OpenAI, and Power BI, can securely communicate with each other. This is crucial for maintaining the integrity and confidentiality of sensitive financial data. Express Route or VPN are expected to be used when connecting on-premises infrastructure to Azure for several reasons. Your company Azure ExpressRoute provides a private, reliable, and high-speed connection between your data center and Microsoft Azure. It allows you to extend your infrastructure to Azure by providing private access to resources deployed in Azure Virtual Networks and public services like App service, private end points to various other services. This private peering ensures that your traffic never enters the public Internet, enhancing security and performance. ExpressRoute uses Border Gateway Protocol (BGP) for dynamic routing between your on-premises networks and Azure, ensuring efficient and secure data exchange. It also offers built-in redundancy and high availability, making it a robust solution for critical workloads. Azure Front Door is a cloud-based Content Delivery Network (CDN) and application delivery service provided by Microsoft. It offers several key features, including global load balancing, dynamic site acceleration, SSL offloading, and a web application firewall, making it an ideal solution for optimizing and protecting web applications. We are expecting to use Front door in scenarios when the architecture will be expected to be used by users outside the organisation. Azure API Management in this scenario is expected to be used when we look to rollout the solution to larger groups. We look to then integrate much more security, rate limiting, load balancing, etc. Monitoring and Governance Azure Monitor: This service collects and analyses telemetry data from various resources, providing insights into the performance and health of the system. It enables proactive identification and resolution of issues, ensuring the system runs smoothly. Azure Cost Management and Billing: Provides tools for monitoring and controlling costs associated with the solution. It offers insights into spending patterns and resource usage, enabling efficient financial governance. Application Insights: Provides application performance monitoring (APM) designed to help you understand how your applications are performing and to identify issues that may affect their performance and reliability These components together ensure that the Knowledge Mining and Intelligent Document Processing solution is monitored for performance, secured against threats, compliant with regulations, and managed efficiently from a cost perspective. ____________________________________________________________________ Next steps: Identify the data and its sources that will feed into your own Knowledge Mine. Consider if you also need to implement Intelligent Document Processing to ensure data quality. Define your 'gold standards'. These guidelines will determine how your data might be transformed. Consider how to provide access to the data through an application portal, choose the right front-end technology for your use case. Once you have configured Azure AI search to point to the chosen data, consider how you might augment responses using Azure AI LLM models. Useful resources AI Landing Zone reference architecture Azure and Open AI with API Manager Secure connectivity from on premesis to Azure hosted solutionsRunning PowerShell Scripts on Azure VMs with Domain User Authentication using Azure Functions
Azure Functions provides a powerful platform for automating various tasks within your Azure environment. In this specific scenario, we’ll use Azure Functions to run commands remotely on a VM while authenticating as a domain user, not as a system-assigned or user-assigned managed identity. Prerequisites Before we dive into the implementation, ensure you have the following prerequisites: Azure Subscription: You'll need an Azure subscription. Azure VM: A Windows VM is required where the PowerShell script will be executed. Azure Function: You will need an Azure Function with the appropriate configurations to run the PowerShell script. Domain User Account: A domain user account for authentication. Azure PowerShell Modules: The Azure PowerShell modules (Az) installed to work with Azure resources. Workflow Diagram: Based on demo script example Step 1: Setting Up Azure Function with Managed Identity We'll start by setting up an Azure Function that will handle the execution of commands on a remote Azure VM. 1.1. Create a New Azure Function Go to the Azure Portal and navigate to Azure Functions. Create a new Function App. This will be your container for the function that will execute the commands. Set up the Function App with appropriate configurations such as region, runtime stack (select PowerShell), and storage account. 1.2. Configure Managed Identity To authenticate the Azure Function to Azure resources like the VM, we’ll use Managed Identity: Go to the Identity section of your Function App and enable System Assigned Managed Identity. Ensure the Function App has the necessary permissions to interact with the VM and other resources. Go to the Azure VM and assign the Managed Identity with the required RBAC roles (e.g., Virtual Machine Contributor). Step 2: Script for Running PowerShell Commands on Azure VM Now let’s focus on writing the script that will execute the PowerShell commands remotely on the Azure VM using domain authentication. 2.1. Set Up PowerShell Script for Remote Execution The PowerShell script to be executed should be capable of running commands like user creation, creating test files and folders, etc. We can use complex script to execute on Azure Windows VM automation tasks except few operations which requires special configuration (drive map, Azure File Share, SMB share). Below is the script for the task: #Script to create a folder on Windows VM using Azure Functions with Domain User Authentication. Script name testing.ps1 # Log file path $logFilePath = "C:\Users\Public\script.log" # Function to log messages to the log file function Write-Log { param([string]$message) $timestamp = (Get-Date).ToString("yyyy-MM-dd HH:mm:ss") $logMessage = "$timestamp - $message" Add-Content -Path $logFilePath -Value "$logMessage`r`n" } Write-Log "Script execution started." # Authenticate with domain user credentials (you can remove this part if not needed for local script) $domainUser = "dctest01\test" #replace with your domain user. Use Azure Key Vault for production. $domainPassword = "xxxxxxx" #replace with your domain user password. Use Azure Key Vault for production. $securePassword = ConvertTo-SecureString $domainPassword -AsPlainText -Force $credentials = New-Object System.Management.Automation.PSCredential($domainUser, $securePassword) Write-Log "Executing script as user: $domainUser" # Define the folder path to create $folderPath = "C:\Demo" # Check if the folder already exists if (Test-Path -Path $folderPath) { Write-Log "Folder 'Demo' already exists at $folderPath." } else { # Create the folder if it doesn't exist New-Item -Path $folderPath -ItemType Directory Write-Log "Folder 'Demo' created at $folderPath." } Write-Log "Test Ended." Step 3: Configure Azure Function to Run PowerShell Commands on VM In the Azure Function, we will use the Set-AzVMRunCommand or Invoke-AzVMRunCommand cmdlets to execute this script on the Azure VM. For production use Azure Key Vault to store sensitive information like subscription Id, username, password. # Input bindings are passed in via param block. param($Request, $TriggerMetadata) # Log file path $logFilePath = "./Invoke-AzVMRunCommand/LogFiles/Scriptlog.log" #Custom logfile to generate logfile of Azure Function execution. This is optional as Function generates logs. # Function to log messages to the log file function Write-Log { param([string]$message) $timestamp = (Get-Date).ToString("yyyy-MM-dd HH:mm:ss") $logMessage = "$timestamp - $message" # Overwrite log file each time (no history kept) $logMessage | Out-File -FilePath $logFilePath -Append -Force } # Start logging all output into the log file Write-Log "Script execution started." # Define the Subscription Id directly $SubscriptionId = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" # Set your Subscription ID here # Authenticate using System Assigned Managed Identity $AzureContext = Connect-AzAccount -Identity # Set the subscription context explicitly $AzureContext = Set-AzContext -SubscriptionId $SubscriptionId -DefaultProfile $AzureContext # Check and output the current Azure context $azContext = Get-AzContext Write-Log "First Identity CONTEXT AFTER:" Write-Log ($azContext | Format-Table | Out-String) # Define VM details and script $un = 'dctest01\test' # Domain user executing the script. Replace with your domain user. $pw = 'xxxxxxxxxxxxx' # Password for the domain user #Replace with domain user password. For production use Azure Key Vault $vmname = 'workernode01-win' #Replace with your Azure Windows VM # Local Script on Windows VM $scriptPath = "./Invoke-AzVMRunCommand/testing.ps1" #testing.ps1 PowerShell script, which runs on Azure Windows VM for local commands. # Define Resource Group Name $resourceGroupName = "test-ub" #Replace with resource group on Windows VM # Log the user executing the script Write-Log "Executing the command as user: $un" # Delete all previous run commands, except the most recent one $existingRunCommands = Get-AzVMRunCommand -ResourceGroupName $resourceGroupName -VMName $vmname # If any run commands exist, remove them except for the latest one if ($existingRunCommands.Count -gt 0) { Write-Log "Removing previous run commands..." $commandsToRemove = $existingRunCommands | Sort-Object -Property CreatedTime -Descending | Select-Object -Skip 1 foreach ($command in $commandsToRemove) { Remove-AzVMRunCommand -ResourceGroupName $resourceGroupName -VMName $vmname -RunCommandName $command.Name Write-Log "Removed Run Command: $($command.Name)" } } # Generate a random string to append to the script name (e.g., RunPowerShellScript_A1B2C3) $randomString = [System.Guid]::NewGuid().ToString("N").Substring(0, 6) # Define the name for the script to run with the random string appended $scriptName = "RunPowerShellScript_" + $randomString Write-Log "Running script: $scriptName" # Set up and run the VM command using Set-AzVMRunCommand Set-AzVMRunCommand -ResourceGroupName $resourceGroupName ` -VMName $vmname ` -RunCommandName $scriptName ` -Location "East US" ` -ScriptLocalPath $scriptPath ` -RunAsUser $un ` -RunAsPassword $pw ` -Verbose ` -Debug ` -Confirm:$false # End logging Write-Log "End of execution." # Interact with query parameters or the body of the request. $name = $Request.Query.Name if (-not $name) { $name = $Request.Body.Name } $body = "This HTTP triggered function executed successfully. Pass a name in the query string or in the request body for a personalized response." if ($name) { $body = "Hello, $name. This HTTP triggered function executed successfully." } # Add-Type for HttpStatusCode Add-Type -TypeDefinition @" using System.Net; "@ # Associate values to output bindings by calling 'Push-OutputBinding'. Push-OutputBinding -Name Response -Value ([HttpResponseContext]@{ StatusCode = [System.Net.HttpStatusCode]::OK Body = $body }) This script will use Set-AzVMRunCommand to run the PowerShell script on the specified VM (workernode01-win) using domain user credentials (dctest01\test). Important Tweaks: RunCommand has limitations for maximum number of executions. To solve the maximum number of executions, we are using delete all previous run commands, except the most recent one. By deleting all previous RunCommand , it will force Azure Windows VM to install the agent again, which will take some time. Run Command execution should generate unique command name which can be performed by generating random string and append with RunCommand name. Step 4: Output and Logs The function will execute the script and return the output, including success or failure logs. The script logs the following: Start and end of the script execution. Which domain user is executing the script. Any errors or successful actions such as mapping drives or creating files. Step 5: Feature and Limitations for Run Commands Microsoft document: RunCommand Known Issues Run scripts in a Windows or Linux VM in Azure with Run Command - Azure Virtual Machines | Microsoft Learn Note The maximum number of allowed Managed Run Commands is currently limited to 25. For certain tasks related to SMB, Azure File Share or Network drive mapping does not work properly on GUI or Windows Remote Desktop Session. Step 6: Troubleshooting RunCommand RunCommand execution can be tracked on Azure Windows VM. Logfiles are generated under C:\WindowsAzure\Logs\Plugins\Microsoft.CPlat.Core.RunCommandHandlerWindows\2.0.14 (Check for latest version path based on version) RunCommandHandler.log Can be used to trace Azure Function execution and PowerShell script on Azure Windows VM. Conclusion With this implementation, you can automate the execution of PowerShell scripts on your Azure VMs using Azure Functions. The domain user authentication allows for secure access to Azure resources while also providing a log file for debugging and auditing purposes. Using Set-AzVMRunCommand and Invoke-AzVMRunCommand, we can execute the script and interact with Azure VMs remotely, all while logging relevant details for transparency and monitoring. Make sure check feature support for both commands for features and limitations.Using NVIDIA Triton Inference Server on Azure Container Apps
TOC Introduction to Triton System Architecture Architecture Focus of This Tutorial Setup Azure Resources File and Directory Structure ARM Template ARM Template From Azure Portal Testing Azure Container Apps Conclusion References 1. Introduction to Triton Triton Inference Server is an open-source, high-performance inferencing platform developed by NVIDIA to simplify and optimize AI model deployment. Designed for both cloud and edge environments, Triton enables developers to serve models from multiple deep learning frameworks, including TensorFlow, PyTorch, ONNX Runtime, TensorRT, and OpenVINO, using a single standardized interface. Its goal is to streamline AI inferencing while maximizing hardware utilization and scalability. A key feature of Triton is its support for multiple model execution modes, including dynamic batching, concurrent model execution, and multi-GPU inferencing. These capabilities allow organizations to efficiently serve AI models at scale, reducing latency and optimizing throughput. Triton also offers built-in support for HTTP/REST and gRPC endpoints, making it easy to integrate with various applications and workflows. Additionally, it provides model monitoring, logging, and GPU-accelerated inference optimization, enhancing performance across different hardware architectures. Triton is widely used in AI-powered applications such as autonomous vehicles, healthcare imaging, natural language processing, and recommendation systems. It integrates seamlessly with NVIDIA AI tools, including TensorRT for high-performance inference and DeepStream for video analytics. By providing a flexible and scalable deployment solution, Triton enables businesses and researchers to bring AI models into production with ease, ensuring efficient and reliable inferencing in real-world applications. 2. System Architecture Architecture Development Environment OS: Ubuntu Version: Ubuntu 18.04 Bionic Beaver Docker version: 26.1.3 Azure Resources Storage Account: SKU - General Purpose V2 Container Apps Environments: SKU - Consumption Container Apps: N/A Focus of This Tutorial This tutorial walks you through the following stages: Setting up Azure resources Publishing the project to Azure Testing the application Each of the mentioned aspects has numerous corresponding tools and solutions. The relevant information for this session is listed in the table below. Local OS Windows Linux Mac V How to setup Azure resources and deploy Portal (i.e., REST api) ARM Bicep Terraform V 3. Setup Azure Resources File and Directory Structure Please open a terminal and enter the following commands: git clone https://github.com/theringe/azure-appservice-ai.git cd azure-appservice-ai After completing the execution, you should see the following directory structure: File and Path Purpose triton/tools/arm-template.json The ARM template to setup all the Azure resources related to this tutorial, including a Container Apps Environments, a Container Apps, and a Storage Account with the sample dataset. ARM Template We need to create the following resources or services: Manual Creation Required Resource/Service Container Apps Environments Yes Resource Container Apps Yes Resource Storage Account Yes Resource Blob Yes Service Deployment Script Yes Resource Let’s take a look at the triton/tools/arm-template.json file. Refer to the configuration section for all the resources. Since most of the configuration values don’t require changes, I’ve placed them in the variables section of the ARM template rather than the parameters section. This helps keep the configuration simpler. However, I’d still like to briefly explain some of the more critical settings. As you can see, I’ve adopted a camelCase naming convention, which combines the [Resource Type] with [Setting Name and Hierarchy]. This makes it easier to understand where each setting will be used. The configurations in the diagram are sorted by resource name, but the following list is categorized by functionality for better clarity. Configuration Name Value Purpose storageAccountContainerName data-and-model [Purpose 1: Blob Container for Model Storage] Use this fixed name for the Blob Container. scriptPropertiesRetentionInterval P1D [Purpose 2: Script for Uploading Models to Blob Storage] No adjustments are needed. This script is designed to launch a one-time instance immediately after the Blob Container is created. It downloads sample model files and uploads them to the Blob Container. The Deployment Script resource will automatically be deleted after one day. caeNamePropertiesPublicNetworkAccess Enabled [Purpose 3: For Testing] ACA requires your local machine to perform tests; therefore, external access must be enabled. appPropertiesConfigurationIngressExternal true [Purpose 3: For Testing] Same as above. appPropertiesConfigurationIngressAllowInsecure true [Purpose 3: For Testing] Same as above. appPropertiesConfigurationIngressTargetPort 8000 [Purpose 3: For Testing] The Triton service container uses port 8000. appPropertiesTemplateContainers0Image nvcr.io/nvidia/tritonserver:22.04-py3 [Purpose 3: For Testing] The Triton service container utilizes this online resource. ARM Template From Azure Portal In addition to using az cli to invoke ARM Templates, if the JSON file is hosted on a public network URL, you can also load its configuration directly into the Azure Portal by following the method described in the article [Deploy to Azure button - Azure Resource Manager]. This is my example. Click Me After filling in all the required information, click Create. And we could have a test once the creation process is complete. 4. Testing Azure Container App In our local environment, use the following command to start a one-time Docker container. We will use NVIDIA's official test image and send a sample image from within it to the Triton service that was just deployed to Container Apps. # Replace XXX.YYY.ZZZ.azurecontainerapps.io with the actual FQDN of your app. There is no need to add https:// docker run --rm nvcr.io/nvidia/tritonserver:22.04-py3-sdk /workspace/install/bin/image_client -u XXX.YYY.ZZZ.azurecontainerapps.io -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg After sending the request, you should see the prediction results, indicating that the deployed Triton server service is functioning correctly. 5. Conclusion Beyond basic model hosting, Triton Inference Server's greatest strength lies in its ability to efficiently serve AI models at scale. It supports multiple deep learning frameworks, allowing seamless deployment of diverse models within a single infrastructure. With features like dynamic batching, multi-GPU execution, and optimized inference pipelines, Triton ensures high performance while reducing latency. While it may not replace custom-built inference solutions for highly specialized workloads, it excels as a standardized and scalable platform for deploying AI across cloud and edge environments. Its flexibility makes it ideal for applications such as real-time recommendation systems, autonomous systems, and large-scale AI-powered analytics. 6. References Quickstart — NVIDIA Triton Inference Server Deploying an ONNX Model — NVIDIA Triton Inference Server Model Repository — NVIDIA Triton Inference Server Triton Tutorials — NVIDIA Triton Inference Server
