Sick Gaming - C#, Visual Basic & .NET

[DevBlog MS] Blazor Welcomes Open-Source Contributions from Syncfusion

Thu, 29 Oct 2026 15:32:07 -0200

We’re pleased to announce our collaboration with Syncfusion to advance Blazor! Syncfusion has released a free, open-source toolkit of Blazor components, and its engineers have contributed fixes and tests to ASP.NET Core and the .NET scaffolding tools. Their investment helps developers building with the toolkit and strengthens the platform for the entire Blazor community.

This collaboration builds on Syncfusion’s contributions to .NET MAUI, where Syncfusion released an open-source toolkit and committed engineering resources to the framework itself. We’re excited to see that commitment extend to Blazor.

Introducing the Syncfusion Toolkit for Blazor

The Syncfusion Toolkit for Blazor provides reusable UI components for Blazor apps under the MIT license. The toolkit includes charts, buttons, calendars, date and time pickers, text and numeric inputs, dialogs, tooltips, and loading indicators.

Syncfusion Toolkit for Blazor showcase featuring charts and input controls in desktop and mobile layouts.

The toolkit is free for everyone, including commercial use. It doesn’t require a license key or eligibility for Syncfusion’s Community License. The open-source toolkit is separate from the broader commercial Syncfusion Blazor suite, which includes additional components and features.

You can explore the code, samples, and contribution guidance in the Syncfusion Blazor Toolkit repository.

To add the toolkit to an existing Blazor project, install the Syncfusion.Blazor.Toolkit NuGet package:

Code:
dotnet add package Syncfusion.Blazor.Toolkit

Then follow the toolkit’s setup instructions for service registration, namespace imports, and theming. The repository also provides project templates if you’d prefer to start with a preconfigured Blazor app.

We’ve worked closely with Syncfusion on the toolkit to help meet a high quality bar for performance, accessibility, security, and trimmability. Component quality is an important part of the overall Blazor experience, and this collaboration includes reviewing how the components work in real applications as well as how developers get started with them.

Contributing to Blazor and its tooling

Syncfusion’s investment extends beyond its component library. Its engineers have delivered merged improvements across ASP.NET Core and the .NET scaffolding tools, helping make Blazor apps and the tools used to build them more reliable.

Merged changes include fixes for preserving prerendered state on interactive Not Found pages and form validation with inherited models, along with clearer QuickGrid diagnostics. These improvements help developers build and troubleshoot Blazor apps, whether or not they use Syncfusion’s components.

Their merged scaffolding fixes also preserve data in generated edit pages when interactivity starts and add the application configuration needed for scaffolded Blazor Identity pages.

The team has also repaired flaky regression tests across Blazor and ASP.NET Core. Beyond those fixes, they’ve developed and run more than 60 end-to-end validation scenarios for new .NET 11 Blazor features, helping identify issues ahead of release. As maintainers, we value this investment in both the platform and the testing that keeps it reliable.

Streamlining setup with scaffolders and AI skills

Syncfusion is contributing a toolkit setup scaffolder to the .NET scaffolding tools. The contribution is under review and focuses on adding the toolkit package, registering services, and configuring namespace imports in an existing Blazor app. It sets up the toolkit rather than generating application pages.

Syncfusion is also contributing an installation and configuration skill to the .NET skills repository for AI coding assistants. That contribution is under review as well. It provides guidance on the open-source package, service registration, theming, and render modes, including apps with separate server and client projects.

Both efforts aim to reduce setup work and help developers and their coding assistants configure the toolkit correctly.

Join the Blazor Community Standup

Join us and Syncfusion on October 29, 2026, at 10 a.m. Pacific Time for a Blazor Community Standup about our collaboration and the free Syncfusion Toolkit for Blazor. Watch below, or to ask questions in the live chat.

Try the toolkit and get involved

Explore the Syncfusion Toolkit for Blazor, try the samples, and share feedback in the toolkit repository.

We’re grateful to Syncfusion for contributing its component expertise and engineering time to Blazor. As with its work on .NET MAUI, this collaboration brings more people into the work of improving .NET. We welcome the wider community to participate through bug reports, testing, design discussions, and code contributions.

The post Blazor Welcomes Open-Source Contributions from Syncfusion appeared first on .NET Blog.

[DevBlog MS] Blazor Welcomes Open-Source Contributions from Infragistics

Tue, 27 Oct 2026 13:55:01 -0200

We’re pleased to welcome Infragistics as an active contributor to Blazor! As part of our collaboration, Infragistics has made more than 50 of its Blazor controls free and open source, and its engineers are contributing directly to the framework. Their work includes new Blazor analyzers for .NET 11 and tooling to help you get started with their components.

Infragistics recently announced this collaboration, building on its experience developing UI components for .NET. By investing in both open-source components and Blazor itself, Infragistics is helping improve the experience for every Blazor developer, whether or not you use its controls.

Free, open-source Blazor components

The Ignite UI for Blazor open-source components include inputs, navigation, and layout controls such as Combo Box, Date Picker, Tree, Stepper, and Tile Manager. Grid Lite provides a lightweight option for displaying tabular data.

A team directory built with Ignite UI for Blazor Grid Lite, showing column filters and custom cell content.

These components are available under the MIT license for use in personal and commercial applications. You can inspect the source, report issues, and contribute improvements in the Ignite UI for Blazor repository. This is a free, open-source subset of the broader Ignite UI for Blazor offering, not a trial of the commercial suite.

To add the core open-source controls to an existing Blazor project, install the IgniteUI.Blazor.Lite NuGet package:

Code:
dotnet add package IgniteUI.Blazor.Lite

If you need Grid Lite, it is available as a separate IgniteUI.Blazor.GridLite package:

Code:
dotnet add package IgniteUI.Blazor.GridLite

Follow the repository’s setup instructions to configure the services, namespace imports, and theme for the components you use. Ignite UI components require an interactive render mode in Blazor Web Apps, and the instructions cover the supported hosting models.

Improving Blazor in .NET 11

Infragistics engineers are bringing their experience as component builders directly into Blazor. They’ve contributed a range of new analyzers for .NET 11 that help developers catch common mistakes while writing code.

For example, their analyzers flag unnecessary StateHasChanged() calls and authentication-state usage that can leave an app with stale user information.

These contributions are merged into ASP.NET Core for .NET 11. The improvements become part of the framework’s developer tooling and benefit every Blazor developer, regardless of which component library they choose.

Quality and easier setup

We’ve worked closely with Infragistics on its Blazor components to help meet a high quality bar for performance, accessibility, security, and trimmability. Those concerns matter when you’re choosing components for production apps, from keyboard and assistive technology support to publishing a trimmed Blazor WebAssembly application.

Infragistics is also contributing an Ignite UI setup scaffolder to the .NET scaffolding tools. The contribution is under review and is designed to automate package installation and application configuration.

For developers working with AI coding assistants, Infragistics has contributed the Ignite UI for Blazor skill to the .NET skills repository. The skill guides agents through package selection, service registration, theming, and render-mode requirements, including setup for apps with separate server and client projects.

Join the Blazor Community Standup

Join us and Infragistics on October 27, 2026, at 10 a.m. Pacific Time for a Blazor Community Standup about our collaboration and the free Ignite UI for Blazor components. Watch below, or to ask questions in the live chat.

Build with Ignite UI and share your feedback

Explore the free Ignite UI for Blazor components and try the controls that fit your app. You can share feedback and contribute fixes in the Ignite UI repository.

We’re grateful to Infragistics for investing engineering resources in Blazor and sharing its components with the community. We welcome contributions from across the .NET ecosystem, and we look forward to continuing this work together.

The post Blazor Welcomes Open-Source Contributions from Infragistics appeared first on .NET Blog.

[DevBlog MS] .NET October 2026 Out-of-Band Security Updates

Sun, 25 Oct 2026 19:55:58 -0200

Today, we are releasing out-of-band (OOB) security updates for .NET 8, .NET 9, and .NET 10, along with .NET 11 Release Candidate 3. We recommend updating as soon as possible.

Security updates

This out-of-band release addresses CVE-2026-111497, a vulnerability in the Brotli decompression algorithm. It also includes a fix for CVE-2026-114148 (Linux .NET tarball ownership) in .NET 8, 9, 10, and 11.

CVE
Description
Affected platforms

CVE-2026-111497
A buffer overflow vulnerability exists in .NET’s use of the Brotli decompression algorithm. This vulnerability may allow an unauthenticated attacker to read or corrupt process memory. This could lead to information disclosure, denial of service, or elevation of privilege.
All Platforms

CVE-2026-114148
Unsafe ownership metadata in affected .NET tarballs could allow a local attacker to modify files and gain elevated privileges.
Linux

Applications may be affected if they process Brotli-compressed data through ASP.NET Core request decompression, HttpClient automatic response decompression, or the

Code:
System.IO.Compression

Brotli APIs. Request decompression and automatic response decompression are not enabled by default. .NET Framework is not affected.

For .NET 10 and .NET 11, CVE-2026-114148 affects source-build tarballs only. For .NET 8 and .NET 9, it also affects ASP.NET Core distributions obtained through ASP.NET Core tarballs, including users who do not build .NET from source.

For vulnerability details and affected products, see the linked security advisories.

Additionally, we are re-releasing the advisories for CVE-2026-70197 (SocketAsyncEventArgs memory corruption) in .NET 8, 9, 10, and 11, and CVE-2026-102870 (Blazor WebAssembly debugger) in .NET 10 and 11 because the October 13, 2026 release did not fully address these issues. Today’s updates provide those fixes.

Download the updates

.NET version
Update
Download

.NET 8
8.0.33
.NET 8 SDK and Runtime

.NET 9
9.0.22
.NET 9 SDK and Runtime

.NET 10
10.0.14
.NET 10 SDK and Runtime

.NET 11
Release Candidate 3
.NET 11 SDK and Runtime

.NET 11 RC2 users should update to RC3 to receive these fixes.

Update your applications and build environments

Framework-dependent applications: Install the updated runtime for the .NET version your application targets, then restart the application.
Self-contained applications: Rebuild and republish with the updated runtime. Installing a newer runtime on the host does not update the runtime bundled with your application.
Containers: Rebuild and redeploy using updated .NET container images.
Source-build consumers: Obtain the updated source-build artifacts for your .NET version and rebuild your packages to incorporate the tarball ownership fix.
ASP.NET Core tarball users on .NET 8 and .NET 9: Update your installation using the refreshed ASP.NET Core Runtime tarball for your .NET version. This applies even if you do not build .NET from source.

You can check the runtimes installed on a machine with:

Code:
dotnet --list-runtimes

For framework-dependent deployments, confirm that the appropriate updated runtime listed above is installed. Self-contained applications and container deployments must be checked separately because they carry their own runtime.

See the .NET deployment documentation for more information about framework-dependent and self-contained applications.

Feedback

If you encounter a problem after updating, report it in the .NET release feedback issues.

The post .NET October 2026 Out-of-Band Security Updates appeared first on .NET Blog.

[DevBlog MS] Get Your .NET MAUI App Ready for Xcode 27

Mon, 19 Oct 2026 22:15:02 -0200

Your existing .NET MAUI app builds with Xcode 27, installs on an iPhone running iOS 27, shows its splash screen, and then disappears. Not exactly the grand entrance you were hoping for.

That symptom can have many causes. But if the only thing that changed is that you’re now building with Xcode 27 and running the app on iOS 27, and you see the device log message below, your .NET MAUI app needs a few changes:

Code:
[code]UIScene life cycle is required for apps built with this SDK.[/code]

UIKit’s scene lifecycle manages each instance of an app’s user interface, including its windows and transitions between foreground and background states. It works alongside the application lifecycle, which manages the app process as a whole. Apple introduced this model with iOS 13, and .NET MAUI already supports it.

This is an Xcode 27 compatibility requirement, not a broken .NET MAUI lifecycle. Existing .NET MAUI projects might still use only the older application lifecycle because updating .NET MAUI or its workloads doesn’t rewrite their platform files. It’s not specific to the iPhone Duo. Every iOS app built with Xcode 27, on any form factor, needs to adopt this pattern. An existing binary built with an older SDK doesn’t suddenly need this migration just because someone updates their phone.

For a straightforward app, the change is small: a scene delegate and an

Code:
Info.plist

entry. The part that deserves your attention is everything you’ve added over the years, such as authentication callbacks, deep links, and App Actions.

That’s where the

Code:
maui-xcode27-migration

agent skill comes in. It guides your coding agent through the project checks, the migration, and the verification. You still review the changes, and the manual steps below explain what it’s doing.

Let an AI agent handle the migration

The .NET MAUI CLI can install agent skills into your development environment. A skill is a set of instructions and supporting resources for the agent. It’s not another runtime dependency in your app.

The Xcode 27 migration skill requires Microsoft.Maui.Cli 0.1.0-preview.13.26512.5 or later. Install that preview release:

Code:
[code]dotnet tool install --global Microsoft.Maui.Cli --version 0.1.0-preview.13.26512.5[/code]

If you already have it installed, update it instead:

Code:
[code]dotnet tool update --global Microsoft.Maui.Cli --version 0.1.0-preview.13.26512.5[/code]

Preview tooling

The .NET MAUI CLI and this skill are experimental and are not covered by the Microsoft Support Policy. The CLI downloads the skill from the public

Code:
dotnet/maui-labs

catalog, so catalog discovery and installation require network access.

From your app’s repository, check that the CLI can find the skill and install it for GitHub Copilot CLI:

Code:
[code]maui ai list skill
maui ai add skill maui-xcode27-migration --env CopilotCli --yes[/code]

If you use another AI provider, check

Code:
maui ai add skill --help

and change the

Code:
--env

option accordingly. Run the command from the app repository you want to change, because the skill is installed for that project.

Restart or reload your agent session so it picks up the new skill, then ask:

Code:
[code]Prepare this .NET MAUI app for Xcode 27. Inspect the project
and toolchain first, preserve custom lifecycle behavior, and validate
the migration. Ask before changing the SDK or dropping support for
older OS versions.[/code]

You don’t need to name the skill in your request. A compatible agent should pick

Code:
maui-xcode27-migration

from the installed skills based on the task. If you want to remove any doubt, feel free to name it explicitly.

The CLI installs the skill, and your coding agent does the migration. There’s no

Code:
maui migrate

command in this workflow, and installing the skill on its own doesn’t touch your app.

Prefer to manage the skill directly with GitHub Copilot CLI? Follow the official GitHub Copilot plugin installation documentation, then add the

Code:
maui-labs

marketplace and install the

Code:
dotnet-maui

plugin:

Code:
[code]copilot plugin marketplace add dotnet/maui-labs
copilot plugin install dotnet-maui@dotnet-maui-labs[/code]

You can also find the migration skill directly in the

Code:
dotnet/maui-labs

repository. Pick one installation method so you don’t end up with duplicate copies.

What the skill checks

Before editing anything, the skill should work out which project, .NET SDK, .NET MAUI packages, Apple workloads, and Xcode version you’re actually using. A

Code:
global.json

, shared package versions, or an existing custom scene delegate can all change the right approach.

The migration covers:

Compatible versions. Match the Apple workload to Xcode, update the required .NET MAUI packages, and check deployment targets without silently dropping older OS support.
Scene configuration. Add or reconcile the delegate and manifest for the iOS and Mac Catalyst targets, including configuration-specific plist files. Preserve existing scene customizations, launch-screen settings, and multi-window behavior.
Custom behavior. Review app-delegate overrides as well as lifecycle registrations, authentication, warm and cold links, notification taps, startup work that needs a window, and App Actions.
Verification. Run a read-only lifecycle audit, review the diff, build the affected targets, and test launch and the behaviors the migration can change. Report anything that could not be exercised.

Commit or otherwise save your current work before starting, and go through the resulting diff before accepting it. A successful build doesn’t prove that an authentication redirect or a Home screen shortcut still works.

If you’d rather do things by hand, the instructions are below.

Before you start: check your app and toolchain

Scene lifecycle support was available before Xcode 27, so your app might already use it. If you already have a

Code:
MauiUISceneDelegate

and matching scene manifest, good news: you can skip the migration steps below. You should still check the toolchain versions and test your app on iOS 27.

At the time of writing, the released .NET 10 iOS and Mac Catalyst workloads 27.0.10722 require Xcode 27.0. Installing a newer Xcode doesn’t automatically make it compatible with that workload. Check the .NET for iOS and Mac Catalyst release notes and use the matching versions rather than disabling the SDK’s Xcode check.

Check your setup

Run

Code:
maui doctor

for a quick check of your active .NET SDK, .NET MAUI workload, selected Xcode, and Apple SDK compatibility. It also checks supporting components such as the Xcode license, command-line tools, and installed iOS runtimes.

For a .NET 10 app, update your .NET MAUI packages to 10.0.110 or higher before enabling scenes. The two-file launch fix can work with an older .NET MAUI version because

Code:
MauiUISceneDelegate

already exists there. However, 10.0.110 also includes the scene-side App Actions dispatch and relevant

Code:
WebAuthenticator

forwarding. Treat the package update as part of the migration, otherwise you might fix the launch and silently break those integrations.

Template projects often get

Code:
$(MauiVersion)

from the installed workload. To pin the minimum version explicitly, add this to your

Code:
.csproj

Code:
[code]
  10.0.110
[/code]

If you already manage this version elsewhere, update that value instead. Don’t downgrade a newer compatible version.

Apps created with the .NET MAUI 10.0.110 templates already have the scene configuration. Existing projects aren’t retrofitted by a package or workload update, so you’ll need to do that part yourself.

Add the scene delegate

In your existing app, create

Code:
Platforms/iOS/SceneDelegate.cs

and paste in this code. If the file already exists, check its base class and registration instead of adding a second delegate:

Code:
[code]using Foundation;
using Microsoft.Maui;

namespace MyMauiApp;

[Register("SceneDelegate")]
public class SceneDelegate : MauiUISceneDelegate
{
}[/code]

Replace

Code:
MyMauiApp

with your app’s namespace. For this default setup, keep the class public and the registration name as

Code:
SceneDelegate

. If your app already has a custom registered scene delegate, preserve its behavior and use its registered name consistently in the manifest instead of adding a competing delegate.

Yes, that’s the whole class. .NET MAUI does the actual window setup for you. Leave your existing

Code:
AppDelegate : MauiUIApplicationDelegate

exactly where it is too. The scene delegate works alongside it, not instead of it.

Add the scene manifest

Open

Code:
Platforms/iOS/Info.plist

as XML and add the following inside the root

Code:

. If you already have a

Code:
UIApplicationSceneManifest

, update its existing scene configuration instead of adding a duplicate key:

Code:
[code]UIApplicationSceneManifest

  UIApplicationSupportsMultipleScenes
  
  UISceneConfigurations
  
    UIWindowSceneSessionRoleApplication
    
        UISceneConfigurationName
        __MAUI_DEFAULT_SCENE_CONFIGURATION__
        UISceneDelegateClassName
        SceneDelegate
      
[/code]

Three details matter here:

Keep
Code:
__MAUI_DEFAULT_SCENE_CONFIGURATION__
exactly as shown. That’s the name .NET MAUI expects.
Match the delegate name in the plist with the C# registration:
Code:
SceneDelegate
and
Code:
[Register("SceneDelegate")]
in this example.
Leave
Code:
UIApplicationSupportsMultipleScenes
set to
Code:
false
unless your app really supports multiple windows.

You don’t need a

Code:
UISceneStoryboardFile

. .NET MAUI creates the window in code, and your existing splash screen configuration stays as it is.

If you have custom scene configuration names or override

Code:
GetConfiguration

Code:
WillConnect

, review them together rather than replacing the manifest wholesale. The default

Code:
MauiUISceneDelegate

window-creation path expects

Code:
__MAUI_DEFAULT_SCENE_CONFIGURATION__

; a different name needs an existing custom implementation that handles window creation.

Some apps select a different plist for Debug, Release, customer, or environment builds. Add or reconcile the manifest in every plist that can produce a shipping app. If you only update the default file, one configuration can still crash right after the splash screen. Preserve the existing

Code:
MauiSplashScreen

Code:
UILaunchStoryboardName

setup while editing these files.

That’s the iOS scene configuration done. If you don’t target Mac Catalyst, skip ahead to the minimum OS versions.

Do the same for Mac Catalyst

If your project also targets Mac Catalyst, make the same two changes there:

Add
Code:
Platforms/MacCatalyst/SceneDelegate.cs
.
Add the same
Code:
UIApplicationSceneManifest
section to
Code:
Platforms/MacCatalyst/Info.plist
.

Check your minimum OS versions

The .NET for iOS and .NET for Mac Catalyst 27 SDKs also raise the minimum supported versions: iOS needs at least

Code:
15.0

, and Mac Catalyst needs at least

Code:
17.0

(macOS 14 Sonoma). In your app’s

Code:
.csproj

, update the existing

Code:
SupportedOSPlatformVersion

entries if yours are lower:

Code:
[code]15.0
17.0[/code]

These entries belong inside a

Code:

. Replace the existing values instead of adding duplicates, because a later definition can override your change. Keep each version directly between its XML tags, without leading or trailing whitespace.

Keep a higher deployment target if your app already requires one. If you must retain support for iOS versions below 15 or macOS 12 and 13, moving to these SDKs requires a support-policy decision. Stay on a compatible 26.x SDK while you decide, rather than silently dropping those users. This SDK minimum is separate from scene adoption; see the .NET MAUI template deployment-target guidance.

Check any custom app behavior

With scenes enabled, UIKit no longer calls the

Code:
AppDelegate

activation, backgrounding, URL, and user-activity callbacks. That goes for both

Code:
ConfigureLifecycleEvents

registrations and methods you override in

Code:
AppDelegate

, such as

Code:
OpenUrl

Code:
ContinueUserActivity

. Move that logic to the corresponding scene events:

Existing event
Scene event

Code:
OnActivated

Code:
SceneOnActivated

Code:
OnResignActivation

Code:
SceneOnResignActivation

Code:
DidEnterBackground

Code:
SceneDidEnterBackground

Code:
WillEnterForeground

Code:
SceneWillEnterForeground

Code:
OpenUrl

Code:
SceneOpenUrl

Code:
ContinueUserActivity

Code:
SceneContinueUserActivity

The callback arguments differ, so move the logic instead of just renaming the registration. If you instead override methods in your

Code:
SceneDelegate

, call the corresponding

Code:
base

implementation. The framework’s window setup, lifecycle events, and authentication forwarding depend on that behavior.

For custom deep links, also handle

Code:
SceneWillConnect

: URLs and user activities that launch the app arrive in its

Code:
UISceneConnectionOptions

. That callback runs before .NET MAUI creates the window, so defer navigation until the window is ready.

If your app opens content from a notification tap, inspect

Code:
connectionOptions.NotificationResponse

as another cold-start input. Preserve that route separately from ordinary push registration and

Code:
DidReceiveRemoteNotification

, which remain application-level callbacks.

A regular launch may have no URL or user-activity payload. Guard against absent

Code:
UrlContexts

and

Code:
UserActivities

before enumerating them, even when the binding’s nullability annotation suggests a value will be there.

Code:
FinishedLaunching

still runs, but the standard .NET MAUI scene path creates the window later through the scene delegate.

Code:
AppDelegate.Window

stays

Code:
null

. Code that accesses it after

Code:
base.FinishedLaunching

needs to use the appropriate scene window when it is ready instead. The cross-platform

Code:
Window

lifecycle events keep working; do not duplicate that forwarding.

.NET MAUI 10.0.110 handles scene-based App Actions and forwards the relevant URL and user-activity callbacks to

Code:
WebAuthenticator

. That authentication handling is not general app deep-link navigation and does not replay cold-start links for you. Test authentication and your own links with the app both running and fully closed.

If you use App Actions, make sure every custom

Code:
PerformActionForShortcutItem

handler calls its completion callback exactly once. Pass

Code:
true

when the action was handled and

Code:
false

when it was not. A handler can respond asynchronously after returning, but returning alone does not acknowledge the action.

Even a handler that only writes a log message needs to respond. As an example, add

Code:
using Microsoft.Maui.LifecycleEvents;

Code:
MauiProgram.cs

, then register this observer inside

Code:
CreateMauiApp

, before

Code:
return builder.Build();

Code:
[code]builder.ConfigureLifecycleEvents(events =>
{
#if IOS || MACCATALYST
    events.AddiOS(ios => ios
        .PerformActionForShortcutItem((application, shortcutItem, completion) =>
        {
            Console.WriteLine($"App Action received: {shortcutItem.Type}");
            completion(false);
        }));
#endif
});[/code]

The default .NET MAUI builder already forwards App Actions to Essentials. You don’t need this logging observer unless you want it, and you shouldn’t add another

Code:
Platform.PerformActionForShortcutItem

registration. If your

Code:
AppDelegate

overrides

Code:
PerformActionForShortcutItem

, that override is no longer called with scenes enabled. Move its custom logic into a registration like the one above, completion callback included.

Rebuild and try it

Restore and build the iOS target:

Code:
[code]dotnet restore -p:TargetFramework=net10.0-ios
dotnet build -f net10.0-ios --no-restore[/code]

If the simulator still seems to have an old version of your app, uninstall it, delete the app project’s

Code:
bin

and

Code:
obj

folders, and run those two commands again.

Launch both Debug and Release builds on a regular iPhone or iPad simulator running iOS 27 with the matching workload and Xcode. A successful build or launch on iOS 26 doesn’t prove that the app will launch on iOS 27 when built with Xcode 27. Check a cold launch, background and foreground transitions, your authentication flow, links and notification taps with the app both running and closed, and App Actions. If you target Mac Catalyst, build and exercise that target too.

You don’t need an iPhone Duo simulator to verify this migration. Device-specific layouts and the Xcode 27.1 tooling are a separate topic that we’ll cover in a follow-up post soon.

The post Get Your .NET MAUI App Ready for Xcode 27 appeared first on .NET Blog.

[DevBlog MS] Bring Your Own Model to the GitHub Copilot SDK with C#

Mon, 19 Oct 2026 14:10:01 -0200

Hi,

I know, I keep coming back to the same theme on this blog: agent harnesses. In Build Your Own AI Agent Harness in C# we built one from scratch. In From dotnet run to Foundry Hosted Agent in 3 lines of C# we took an agent and hosted it. Today we use a harness we do not have to build at all: the one GitHub already ships, through the GitHub Copilot SDK.

One of the most popular questions we received during the Let’s Learn GitHub Copilot SDK sessions was:

Quote:“Can I use the Copilot SDK with my own model deployment?”

The short answer is yes.

The longer answer is, the GitHub Copilot SDK gives your application the Copilot agent runtime, while

Code:
ProviderConfig

lets you route model inference to a provider you control. That provider can be any of the providers officially supported by the Copilot SDK: OpenAI, Anthropic, Ollama, Microsoft Foundry Local, or any OpenAI-compatible endpoint such as vLLM or LiteLLM. Today’s sample uses Microsoft Foundry, authenticated with Microsoft Entra ID or, when needed, an API key.

The complete sample is now part of Generative AI for Beginners .NET. It is a small .NET 10 console application: read configuration, create a BYOK provider, start the Copilot runtime, create one session, and ask one question.

Get the complete BYOK C# sample

What BYOK means in this sample

BYOK means Bring Your Own Key, but in this context the important idea is broader than the key itself: you bring your own model provider.

The Copilot SDK still supplies the runtime that manages the client, session, system message, and agent configuration.

Code:
ProviderConfig

changes the destination and authentication for model inference.

BYOK is not customer-managed encryption keys

In this article, BYOK means using your own model endpoint and credentials for inference. It does not refer to customer-managed encryption keys or a storage-encryption configuration.

The sample intentionally keeps the session minimal. It disables tools, skills, configuration discovery, and host Git operations so that the first run demonstrates one thing clearly: a Copilot SDK session can send a model request to your own Foundry-compatible deployment.

The runtime and the model provider are separate parts of the application.

The project

The sample is a regular .NET 10 console application. These are the packages it uses:

Code:
[code]
  
    Exe
    net10.0
    enable
    enable
    genai-beginners-dotnet
  
[/code]

Code:
GitHub.Copilot.SDK

provides the .NET client for the Copilot runtime.

Code:
Azure.Identity

provides

Code:
AzureCliCredential

, which lets the sample reuse the identity from

Code:
az login

. The configuration packages load development-only secrets without putting endpoints or credentials in source control.

Step 1: read the model configuration

The sample uses the shared User Secrets values already used by the repository:

Code:
[code]// User Secrets keeps the endpoint and credentials outside the repository.
var configuration = new ConfigurationBuilder()
    .AddUserSecrets(optional: true)
    .Build();

// This sample uses the repo's shared AzureOpenAI secrets.
var modelName = GetRequiredValue(configuration, "AzureOpenAI:Deployment");
var endpoint = GetRequiredValue(configuration, "AzureOpenAI:Endpoint");
var apiKey = configuration["AzureOpenAI:ApiKey"]?.Trim();[/code]

There are two required values:

Code:
AzureOpenAI:Deployment
is the name of your model deployment in Microsoft Foundry or Azure OpenAI. Choose a deployment that supports the Responses API, because this sample uses
Code:
WireApi = "responses"
.
Code:
AzureOpenAI:Endpoint
is the Foundry or Azure OpenAI resource endpoint.

Code:
AzureOpenAI:ApiKey

is optional. If it is not present, the sample uses Microsoft Entra ID through

Code:
AzureCliCredential

. If it is present, the sample sends it in the

Code:
api-key

header.

Step 2: configure the provider

This is the core of the sample:

Code:
[code]// Microsoft Foundry exposes an API compatible with OpenAI Responses.
var provider = new ProviderConfig
{
    Type = "openai",
    BaseUrl = NormalizeEndpoint(endpoint),
    WireApi = "responses",
    ModelId = modelName,
    WireModel = modelName,
    MaxPromptTokens = 8000,
    MaxOutputTokens = 512
};[/code]

The configuration tells the Copilot runtime how to communicate with the provider:

Code:
Type = "openai"
selects the OpenAI-compatible provider shape.
Code:
BaseUrl
points to the OpenAI-compatible API surface.
Code:
WireApi = "responses"
selects the Responses API.
Code:
ModelId
identifies the model to the Copilot runtime.
Code:
WireModel
is the model name sent to the provider.
The token limits keep this small example bounded.

For a reasoning model,

Code:
MaxOutputTokens

includes reasoning tokens as well as the visible answer. The 512-token limit can be reached before the model produces any answer. Increase or omit the

Code:
MaxOutputTokens

override when using a reasoning deployment, then tune the limit for your workload.

In this sample,

Code:
ModelId

and

Code:
WireModel

have the same value. They are separate properties because the runtime-facing model identifier and the provider-facing model name do not have to be identical in every integration.

The endpoint helper accepts either the resource URL or a URL that already includes

Code:
/openai/v1/

Code:
[code]static string NormalizeEndpoint(string endpoint)
{
    var candidate = endpoint.Trim();
    if (!Uri.TryCreate(candidate, UriKind.Absolute, out var uri) ||
        uri.Scheme != Uri.UriSchemeHttps ||
        string.IsNullOrWhiteSpace(uri.Host))
    {
        throw new ArgumentException("The configured AzureOpenAI:Endpoint must be an HTTPS URL.");
    }

    // Accept the resource URL or a URL that already ends in /openai/v1.
    // Trimming first means a trailing slash does not need a separate check.
    var trimmed = candidate.TrimEnd('/');
    if (trimmed.EndsWith("/openai/v1", StringComparison.OrdinalIgnoreCase))
    {
        return trimmed + "/";
    }

    return $"{uri.GetLeftPart(UriPartial.Authority).TrimEnd('/')}/openai/v1/";
}[/code]

The helper also requires HTTPS. It is better to reject a malformed endpoint before creating a session than to discover it after the first model request.

Step 3: use Entra ID by default

The default authentication path uses the Azure CLI login:

Code:
[code]if (string.IsNullOrWhiteSpace(apiKey))
{
    // AzureCliCredential reuses the session created with `az login`.
    var credential = new AzureCliCredential();
    var scope = "https://cognitiveservices.azure.com/.default";
#pragma warning disable GHCP001 // BYOK auth callbacks are experimental in the Copilot SDK.
    provider.BearerTokenProvider = async _ =>
    {
        var token = await credential.GetTokenAsync(
            new TokenRequestContext([scope]),
            cancellation.Token);
        return token.Token;
    };
#pragma warning restore GHCP001
}[/code]

Before signing in, make sure your identity has the Cognitive Services OpenAI User role on the resource. Then run:

Code:
[code]az login[/code]

Code:
BearerTokenProvider

is a callback. The SDK can call it when it needs a token instead of receiving one token value that the application has to manage itself.

Step 4: use an API key when one is configured

Some deployments or environments still require an API key. The sample supports that path without changing the rest of the provider configuration:

Code:
[code]else
{
    provider.Headers = new Dictionary
    {
        ["api-key"] = apiKey
    };
}[/code]

Set the optional secret:

Code:
[code]dotnet user-secrets set "AzureOpenAI:ApiKey" ""[/code]

When the value exists, the sample uses the

Code:
api-key

header and does not require

Code:
az login

. The console output reports only the authentication mode; it never prints the endpoint or the credential.

Keep credentials out of source control

Use User Secrets for local development, environment-managed configuration for deployed applications, and never paste an API key into the source file or a README.

Step 5: start the Copilot runtime

The provider controls model inference, but the Copilot runtime is still required:

Code:
[code]await using var client = new CopilotClient(new CopilotClientOptions
{
    Connection = RuntimeConnection.ForStdio("copilot"),
    WorkingDirectory = AppContext.BaseDirectory
});
await client.StartAsync(cancellation.Token);[/code]

This is an important distinction. BYOK does not mean “the Copilot SDK without Copilot.” The local Copilot CLI runtime still provides the orchestration process. BYOK changes where the model request goes.

Prerequisites

Install the .NET 10 SDK and the GitHub Copilot CLI, and make sure

Code:
dotnet --version

reports a .NET 10 SDK and

Code:
copilot --version

works. For the default Microsoft Entra ID authentication path, also install the Azure CLI and make sure

Code:
az --version

works. The API-key path does not require the Azure CLI. The sample starts the local Copilot runtime through

Code:
RuntimeConnection.ForStdio("copilot")

.

Step 6: create a deliberately small session

The session configuration connects the model name and provider, then disables capabilities that are outside the scope of this first example:

Code:
[code]// Disable tools, skills, and Git operations to isolate the model request.
await using var session = await client.CreateSessionAsync(new SessionConfig
{
    ClientName = "github-copilot-sdk-byok",
    Model = modelName,
    Provider = provider,
    SystemMessage = new SystemMessageConfig
    {
        Mode = SystemMessageMode.Replace,
        Content = "Answer every user question in English. Be concise and factual."
    },
    AvailableTools = [],
    Tools = [],
    EnableConfigDiscovery = false,
    EnableSkills = false,
    EnableHostGitOperations = false
}, cancellation.Token);[/code]

The empty tool collections and disabled features are intentional. They make the sample easier to reason about and safer to run while learning the provider configuration.

The system message also makes the output predictable for a multilingual live-learning sample.

Code:
SystemMessageMode.Replace

replaces the default system message instead of appending another instruction to it.

Step 7: send one question

Finally, the application sends one prompt and waits for the final response:

Code:
[code]var response = await session.SendAndWaitAsync(
    new MessageOptions { Prompt = question },
    TimeSpan.FromMinutes(3),
    cancellation.Token);

var content = response?.Data.Content;
if (string.IsNullOrWhiteSpace(content))
{
    throw new InvalidOperationException("The model returned an empty response.");
}

Console.WriteLine();
Console.WriteLine("Answer:");
Console.WriteLine(content);[/code]

The default question is:

Code:
[code]What is Microsoft Foundry, and how does it relate to the GitHub Copilot SDK?[/code]

You can provide another question as command-line arguments:

Code:
[code]dotnet run -- "Explain BYOK in one sentence."[/code]

Run the sample

From the sample directory, configure the required values:

Code:
[code]dotnet user-secrets set "AzureOpenAI:Endpoint" "https://.services.ai.azure.com"
dotnet user-secrets set "AzureOpenAI:Deployment" ""[/code]

Then use the default Entra ID path:

Code:
[code]az login
dotnet run[/code]

The output looks like this:

Code:
[code]Model: 
Authentication: AzureCliCredential
Question: What is Microsoft Foundry, and how does it relate to the GitHub Copilot SDK?

Answer:
[/code]

The sample prints the selected model and authentication mode, but not the endpoint or credentials.

So, what did we actually learn here?

Honestly, the part I like most is how little code this takes.

The Copilot SDK runtime and the model provider are two different things, and once that clicks, the rest is easy. You keep the runtime that GitHub gives you: the client, the session, the system message, and the agent plumbing. Then you just point inference somewhere else. That “somewhere else” is your Foundry deployment, your OpenAI key, your local Ollama, whatever you need.

Entra ID is the default path here, and I think it should be. No key to rotate, no key to leak, no key to accidentally commit.

Code:
az login

and you are done. The API key branch is there for the cases where you really need it, and it stays out of the way until you configure it.

And the session is deliberately boring. No tools, no skills, no MCP, no Git access. That is on purpose: the first run has exactly one job, which is proving that your model is answering. Once you see that answer in the console, you have a working baseline.

That is the fun part. From here you can start adding things one at a time: tools, MCP servers, skills, approvals, and streaming. When something breaks, you will know exactly which piece broke it.

Go try it with your own deployment.

Learn more

Explore the complete GitHub Copilot SDK sample

Happy coding!

Bruno

The post Bring Your Own Model to the GitHub Copilot SDK with C# appeared first on .NET Blog.

[DevBlog MS] Introducing dotnetup: A toolchain manager for .NET

Thu, 15 Oct 2026 17:54:51 -0200

Getting started with .NET on macOS and Linux has historically meant navigating a plethora of options: system package managers like

Code:
apt

Code:
brew

, platform-specific tarballs, or standalone shell scripts like

Code:
dotnet-install.sh

. While those methods have served us well, they often collide with administrative permissions, leave files scattered across root directories, or make keeping multiple runtime versions side-by-side more awkward than it should be.

That’s why we’re introducing the public preview of

Code:
dotnetup

, a new .NET toolchain manager built to install, manage, and update .NET SDKs and runtimes directly in your user account.

Why dotnetup?

If you’ve spent time in ecosystems like Rust (

Code:
rustup

) or Node (

Code:
nvm

), you’re already familiar with the power of a dedicated toolchain manager. The core goal of

Code:
dotnetup

is to drastically reduce concept count and friction when getting a .NET environment running:

Consistent ux: The same commands and tools across every platform, regardless of the details of the installation
Non-admin by default: Everything lives comfortably in user-owned folders, not system-owned – meaning zero need for elevated permissions.
Side-by-side runtime management: Keep multiple servicing versions, preview channels, and daily builds installed at once without them stepping on each other’s toes.
Track intent: Instead of managing versions, track majors or feature bands and let the tools keep you updated each month
Respect for project pinning: Works seamlessly with your existing
Code:
global.json
files so team builds remain predictable and deterministic.

Let’s walk through how to get started and how to use

Code:
dotnetup

for common day-to-day tasks.

Installing dotnetup and getting started

To get

Code:
dotnetup

on macOS or Linux, run the bootstrap script in your terminal:

macOS (Zsh):

Code:
[code]curl -fsSL https://aka.ms/dotnetup/get-dotnetup.sh | zsh[/code]

Linux (Bash):

Code:
[code]curl -fsSL https://aka.ms/dotnetup/get-dotnetup.sh | bash[/code]

PowerShell (

Code:
pwsh

Code:
[code]irm https://aka.ms/dotnetup/get-dotnetup.ps1 | iex[/code]

Once the script pulls down the binary, initialize your environment:

Code:
[code]dotnetup init[/code]

The

Code:
init

command sets up your shell profile so that

Code:
dotnet

is immediately available in your terminal sessions and fetches the latest stable .NET SDK.

https://devblogs.microsoft.com/dotnet/wp...tarted.mp4

Let’s check what’s installed in our local environment:

Code:
[code]dotnetup list[/code]

You’ll see a clean inventory of the SDKs and runtimes currently managed by

Code:
dotnetup

right in your home folder.

Keeping runtimes fresh month-over-month

Every month the .NET team ships servicing updates with security fixes and quality improvements. Keeping your local dev machine up to date helps you build and test against those fixes. For self-contained applications, you also need to republish and redeploy with the updated runtime. Updating your machine does not patch applications you have already shipped.

When using system installs, you may not be in control of your ability to update. With

Code:
dotnetup

, staying current is a one-liner:

Code:
[code]dotnetup update[/code]

This command resolves your tracked SDK and runtime channels again and installs the latest matching versions. For example,

Code:
10.0

tracks updates within .NET 10, while

Code:
latest

follows the latest stable SDK, including new major releases. Exact-version requirements stay pinned, so an installation requested as

Code:
10.0.3

will not advance during an update.

If you only want to install or refresh a specific runtime flavor, like the ASP.NET Core runtime on .NET 10, you can target it cleanly:

Code:
[code]dotnetup runtime install aspnetcore@10.0[/code]

https://devblogs.microsoft.com/dotnet/wp...ntimes.mp4

After a successful update,

Code:
dotnetup

automatically removes installed components that no remaining requirement needs. Versions that another channel or exact-version requirement still needs stay installed.

When you no longer need a tracked channel or exact version, remove that requirement with the corresponding

Code:
sdk uninstall

Code:
runtime uninstall

command.

Code:
dotnetup

then cleans up the installations that are no longer needed. See updating tracked installations for more details.

Testing nightly and preview builds

Beyond routine monthly servicing, many developers want to test bug fixes or check out new runtime capabilities before they hit stable release channels. Previously, testing a nightly runtime build meant hunting down raw installer tarballs from GitHub, manually unpacking them into custom directories, and tweaking environment variables by hand.

Code:
dotnetup

treats previews and nightlies as first-class citizens. You can install preview SDKs side-by-side with your stable setup:

Code:
[code]dotnetup sdk install preview[/code]

https://devblogs.microsoft.com/dotnet/wp...nstall.mp4

Or pull down the latest daily runtime build to verify an issue fix:

Code:
[code]dotnetup runtime install daily[/code]

Or grab an entire SDK to check out the entire toolchain:

Code:
[code]dotnetup sdk install daily[/code]

You get the flexibility to test daily builds across your user account and clean them up easily!

Updating

Code:
dotnetup

itself

We’ll be pushing features and fixes to

Code:
dotnetup

over time, and you can get them easily:

Code:
[code]dotnetup self update[/code]

Common questions

Here are quick answers to a few questions you might have:

I could do all of this with

Code:
dotnet-install.[sh|ps1]

That’s not a question.

But yeah, you could do most of this with the install scripts.
What you miss out on is

management of the installed SDKs and Runtimes
signature and checksum validation of the installed SDKs and Runtimes
faster installation of multiple SDKs and Runtimes
a unified CLI surface for doing this on every platform

Does dotnetup need sudo or root rights?

Nope.

Code:
dotnetup

is currently built for non-admin, user-local operation. All SDKs, runtimes, and metadata live entirely within your user directory.

In a future release we’ll be adding support for managing the system-level installs on Windows because of how prevalent those
are on that operating system.

Can I use dotnetup in CI pipelines?

Absolutely. Because

Code:
dotnetup

doesn’t require root privileges and runs smoothly non-interactively, it’s a great fit for containerized or ephemeral CI workers where you need to spin up specific SDK or nightly runtime versions on the fly.

What’s on the roadmap for

Code:
dotnetup

?

As mentioned, we’ll be looking into support for Windows admin
installers, but we also want to integrate

Code:
dotnetup

into CI/CD systems
like GitHub Actions and Azure DevOps – ideally through replacing parts of the implementations of those tools today that rely on the dotnet-install scripts.

We also want to iterate on the user experience to better fit how
you all use the tool, and flesh out agent skills so that automated
assistants use the tooling more naturally and reach for it by default.

Where should I go for more details?

For the full CLI reference, advanced configuration options, and deep dives, head over to the dotnetup documentation on Microsoft Learn.

If you have feedback, run into bugs, or want to share your thoughts on the experience, please drop by the dotnet/sdk discussions on GitHub. We’d love to hear how it works for you, or how we can make it better!

The post Introducing dotnetup: A toolchain manager for .NET appeared first on .NET Blog.

[DevBlog MS] Announcing .NET 11 Release Candidate 2

Wed, 14 Oct 2026 04:38:30 -0200

.NET 11 Release Candidate 2 is now available. This is our second release
candidate, which comes with a
go-live support license
so you can confidently use this release for your production applications. This
release of .NET 11 is supported in the new
Visual Studio 2026 Insiders release alongside
Visual Studio Code with the C# Dev Kit. Check out the full release notes below
and get started today.

Download .NET 11 RC2

This release contains the following highlights and improvements.

Libraries

Runtime

SDK

MSBuild

NuGet

ASP.NET Core

.NET MAUI

Windows Forms

Get started

To get started with .NET 11,
install the .NET 11 SDK.

If you’re on Windows, we recommend installing the latest version of
Visual Studio 2026 Insiders.

You can also use Visual Studio Code and the
C# Dev Kit
extension with .NET 11.

Join a .NET community standup

Join us each week and engage with the developers and product managers behind
.NET for
community standups.

.NET 11 Discussions

The team has been making release announcements alongside full
release notes
on the
dotnet/core GitHub Discussions.

Stay up-to-date with .NET 11

You can stay up-to-date with all the features of .NET 11 with:

The post Announcing .NET 11 Release Candidate 2 appeared first on .NET Blog.

[DevBlog MS] .NET and .NET Framework October 2026 servicing releases updates

Tue, 13 Oct 2026 20:50:01 -0200

Welcome to our combined .NET servicing updates for October 2026. Let’s get into the latest release of .NET and .NET Framework. Here’s a quick overview of what’s new in our servicing releases:

Security improvements

.NET has been refreshed with the latest update as of October 13, 2026. This update contains security and non-security fixes.

For the full list of CVEs, see the CVE list for each supported version:

.NET version
CVE list

.NET 10.0
CVE list

.NET 9.0
CVE list

.NET 8.0
CVE list

.NET 10.0
.NET 9.0
.NET 8.0

Release Notes
10.0.13
9.0.21
8.0.32

Installers and binaries
10.0.13
9.0.21
8.0.32

Container Images
images
images
images

Linux packages
10.0
9.0
8.0

Known Issues
10.0
9.0
8.0

Release changelogs

ASP.NET Core: 10.0.13
Runtime: 10.0.13 | 9.0.21

.NET Framework October 2026 Updates

This month, there are new security and non-security updates available. For recent .NET Framework servicing updates, be sure to browse our release notes for .NET Framework for more details.

See you next month

That’s it for this month, make sure you update to the latest service release today.

The post .NET and .NET Framework October 2026 servicing releases updates appeared first on .NET Blog.

[DevBlog MS] Durable Task in the Real World: What Problems Does It Actually Solve?

Mon, 12 Oct 2026 15:40:02 -0200

Five practical examples of when durable execution makes sense, when it doesn’t, and what it means for AI agents.

Durable Task raises an important question:

This all looks interesting, but what are the actual use cases?

Not how Durable Task works. Not what happens during replay or how the scheduler persists state. But the more practical question: what problems does it actually solve?

When should I reach for durable execution instead of regular C#, a queue, a database, or a background worker? And when would adding it just make things more complicated?

Those are fair questions. Rather than walk through another hello world orchestration, I want to look at real situations where things get messy, especially when work takes a long time, depends on external systems, or gets interrupted halfway through. I also want to be clear about what Durable Task does not solve for you.

Imagine a background worker creating a customer’s Azure environment. It provisions a network, starts a database deployment, creates storage, and deploys an application. Then the worker disappears. Azure might still be working, but your local variables and continuations are gone. Restarting the method is easy. Figuring out which operations completed and which are safe to repeat is the hard part.

Of course, you could build your own solution with a database, a queue, an outbox, scheduled jobs, and reconciliation logic. Sometimes that’s exactly what you should do. But once you find yourself building a whole system just to remember what happens next and how to resume it, durable orchestration starts to get interesting.

I’ll walk through five examples: invoice approvals, Azure tenant provisioning, subscription payments, breaking news articles arriving through out of order webhooks, and an AI agent investigating an incident. We’ll use the standalone Durable Task .NET SDK v1.26.0 with Durable Task Scheduler (DTS), and I’ll show both the useful parts and the responsibilities that still belong to our application.

A note on the examples: The snippets below are deliberately focused on orchestration boundaries. Application adapters, DTO definitions, registrations, and some helper implementations are omitted. They illustrate the design rather than constituting a compiled, tested sample. In particular, do not infer exactly-once external effects from durable history.

Each example has the same boundary: Durable Task coordinates part of the process, while the application remains authoritative for the external result.

Scenario
Durable Task helps with
The application still owns

Human approval
Durable waiting and continuation
Authorization and the authoritative decision

Provisioning
Business-level sequencing
ARM reconciliation and safe cleanup

Payments
Timers, callbacks, and recovery policy
The payment ledger and uncertain charges

Webhooks
Durable coordination
Destination-level revision fencing

Agents
Durable approval and recovery flow
Proposal identity and safe tool execution

1. An invoice needs approval. Why should the worker remember the wait?

Consider an invoice-processing service. It extracts a document, validates the result, and either posts the invoice, rejects it, or asks a human to review it. A batch may contain eight invoices, only one of which needs approval. The other seven shouldn’t wait for it.

Here’s an ordinary C# implementation of the individual processing path:

Code:
[code]public static async Task ProcessAsync(
    InvoiceJob invoice, IInvoiceApplication app, CancellationToken ct)
{
    InvoiceCandidate candidate = await app.ExtractInvoiceAsync(invoice, ct);
    InvoiceAssessment assessment = await app.ValidateInvoiceAsync(candidate, ct);

    if (assessment.Check == InvoiceCheck.NeedsReview)
    {
        await app.OpenInvoiceReviewAsync(
            new InvoiceReview(invoice, assessment.PayloadReference), ct);
        return new(invoice.InvoiceKey, "AwaitingReview");
    }

    if (assessment.Check == InvoiceCheck.Rejected)
        return new(invoice.InvoiceKey, "Rejected");

    if (assessment.Check != InvoiceCheck.Valid)
        throw new InvalidOperationException("Unrecognized invoice assessment.");

    AccountingReceipt receipt = await app.SubmitApprovedInvoiceAsync(
        new InvoiceCandidate(invoice, assessment.PayloadReference), ct);
    return new(invoice.InvoiceKey, "Posted", receipt.EntryReference);
}[/code]

There’s nothing inherently unreliable about this method.

Code:
OpenInvoiceReviewAsync

can persist a review ticket and notification outbox. But when someone approves the invoice tomorrow, another handler must load the right document generation, check the deadline, recover the approved payload, and continue. If the worker dies between extraction and validation, the application must decide whether to extract again or retrieve the saved result.

That continuation protocol is what Durable Task can express directly.

Give each invoice its own durable history

A bounded batch can start independent child orchestrations:

Code:
[code]public override async Task RunAsync(
    TaskOrchestrationContext context, InvoiceBatch batch)
{
    if (batch.Invoices is null || batch.Invoices.Length is < 1 or > 8
        || batch.Invoices.Select(x => x.InvoiceKey).Distinct().Count()
           != batch.Invoices.Length)
        throw new ArgumentException("Supply 1 to 8 distinct invoices.");

    return await Task.WhenAll(batch.Invoices.Select(invoice =>
        context.CallSubOrchestratorAsync(
            nameof(ProcessInvoice), invoice)));
}[/code]

Eight is an application admission limit, not a DTS quota. Larger batches can be paged and subject to tenant/global concurrency limits. A child that completes can post its invoice while another child remains suspended awaiting review.

Code:
Task.WhenAll

waits for every child; it does not undo a sibling’s accounting entry if another child fails.

Inside the invoice child, extraction and validation are activities. Once their successful results are recorded in orchestration history, a compatible replay follows the same code path using those recorded results instead of repeating the activities. This is re-execution of orchestrator code against history, not restoration of a suspended CLR stack.

When validation requires review, the child can express the wait:

Code:
[code]var review = new InvoiceReview(invoice, assessment.PayloadReference);
await context.CallActivityAsync("OpenInvoiceReview", review);

TimeSpan remaining =
    invoice.ReviewDeadlineUtc - context.CurrentUtcDateTime;

if (remaining > TimeSpan.Zero)
{
    try
    {
        InvoiceReviewHint hint =
            await context.WaitForExternalEvent(
                $"invoice-review:{invoice.ReviewId}", remaining);

        if (hint.ProcessingId != invoice.ProcessingId
            || hint.ReviewId != invoice.ReviewId)
            throw new InvalidOperationException("Review identity mismatch.");
    }
    catch (OperationCanceledException)
    {
        // The durable wait timed out. Read the committed decision below.
    }
}

InvoiceDecision decision =
    await context.CallActivityAsync(
        "ResolveInvoiceReview", review);[/code]

The timer is durable. No thread is occupied while the invoice waits, and a replacement worker can replay the child to the same pending wait. But the event is only a hint. An authenticated endpoint must atomically commit a single terminal review decision to the application’s review store before signaling the child.

Code:
ResolveInvoiceReview

reads or closes that authoritative ticket, including when the timer fires. If approval was committed just before the deadline but the hint arrived just after it, the stored decision, not notification timing, determines the outcome.

Only a valid automatic assessment or a committed human approval may proceed to the accounting activity. That activity uses a stable posting key and immutable request fingerprint. If the accounting system accepted the post but the activity’s acknowledgement vanished, the integration must reconcile the same posting identity. Durable history cannot promise exactly-once accounting.

This is the part I like. Instead of building separate handlers to remember extraction, validation, the review wait, and posting, I can describe the whole process in one place and let Durable Task remember where it got to. I still need to authorize reviewers, persist their decisions, and make accounting submissions safe to repeat. If my existing continuation handlers already do all of that cleanly, I might not need another runtime.

2. Tenant provisioning: the cloud already has a deployment engine

Now consider onboarding a SaaS tenant. A

Code:
TenantCreated

event means the tenant record was accepted, not that its environment is ready. This design provisions a dedicated network, PostgreSQL server, storage with private access, and Container App; then it configures DNS, checks readiness, and activates the tenant.

The ordinary C# method is easy to read:

Code:
[code]public async Task ProvisionAsync(
    EnvironmentRequest request,
    CancellationToken cancellationToken = default)
{
    NetworkResources network = await azure.CreateVirtualNetworkAsync(
        request, cancellationToken);

    Task databaseTask =
        azure.CreatePostgreSqlServerAsync(
            request, network.PostgreSqlSubnet, cancellationToken);
    Task storageTask =
        azure.CreateStorageAccountAsync(
            request, network.StoragePrivateEndpointSubnet,
            cancellationToken);

    await Task.WhenAll(databaseTask, storageTask);

    ContainerAppReference application = await azure.DeployContainerAppAsync(
        request, network.ContainerAppsSubnet,
        await databaseTask, await storageTask, cancellationToken);

    await azure.CreateDnsRecordAsync(
        request, application.Endpoint, cancellationToken);
    await azure.WaitForApplicationReadyAsync(
        application.Endpoint, cancellationToken);

    return new ProvisionedEnvironment(
        request.EnvironmentId, application.Endpoint);
}[/code]

The helpers above wrap Bicep/ARM operations and wait for terminal completion or readiness. They’re application abstractions, not Azure SDK methods. The parallel modules own disjoint resources and don’t compete over subnet updates.

Before adding DTS, ask an uncomfortable question: does ARM already solve this? For a single well-defined deployment, it may. ARM/Bicep already supports dependency ordering, parallel deployment, idempotent reapplication with stable identities, and server-side deployment state. A worker crash doesn’t erase an ARM deployment. Querying it and reapplying the same desired state may be sufficient.

The wider onboarding process is different. The application must acknowledge the tenant request, report progress, coordinate readiness with activation, survive external approval steps if any, and decide what to do with a partly provisioned environment. That’s where a business-level workflow can add value.

Separate resource readiness from tenant activation

First, the API authorizes and durably admits an immutable onboarding request, then returns

Code:
202 Accepted

, a request ID, and a status URL. Accepted does not mean ready. The application records the tenant, environment, immutable specification, and ownership generation. Reusing the same idempotency key with the same specification returns the existing request; changing the specification is rejected.

The scheduler handoff is not magically atomic with the application’s database. Save the intent to start in an outbox alongside admission; dispatch it and reconcile the workflow identity. Repeatedly scheduling a new instance is not the same as resuming an existing one.

The outer orchestration can then express the business dependency:

Code:
[code]public override async Task RunAsync(
    TaskOrchestrationContext context, EnvironmentPlan plan)
{
    EnvironmentResult resources =
        await context.CallSubOrchestratorAsync(
            nameof(ProvisionEnvironment), plan);

    return await context.CallActivityAsync(
        "ActivateTenant", new TenantActivationRequest(plan, resources));
}[/code]

The child finishes at resource readiness. Activation is a separate, idempotent application activity that revalidates tenant ownership and actual resource readiness. If activation fails, the outer workflow fails visibly without automatically deleting the resources the child provisioned. Repairing activation is a different problem from rolling back provisioning.

Inside the child, stage dependencies form waves: network; database and storage in parallel; application; DNS; readiness. Each stage activity records an intent and starts or reconciles a provider operation. A separate read activity performs one bounded provider status query; the orchestrator schedules a durable timer between polls using

Code:
context.CurrentUtcDateTime

and

Code:
context.CreateTimer

Code:
[code]DateTime now = context.CurrentUtcDateTime;
if (now >= deadline)
    return Failed("PollingDeadlineExceeded");

TimeSpan delay = TimeSpan.FromSeconds(progress.PollAfterSeconds);
DateTime wakeAt = delay >= deadline - now
    ? deadline
    : now.Add(delay);

await context.CreateTimer(wakeAt, CancellationToken.None);[/code]

No HTTP request or

Code:
Task.Delay

runs inside the orchestrator. The activity does the I/O; the orchestration history records the activity result and timer decision. A 45-minute polling budget limits additional polling. It does not cancel an Azure operation that was already accepted.

Follow one failure all the way through

Imagine the database deployment was accepted by Azure, but the worker died before the activity completion reached DTS:

DTS does not have a recorded successful activity result, so activity redelivery is possible.
The redelivered adapter consults the application’s operation journal and the stable Azure deployment/resource identity.
If the deployment is still running, it resumes inquiry rather than starting an unrelated database.
Once the provider returns terminal completion, the activity records the result and the child continues toward application deployment.
The tenant is activated only after the resource child returns validated readiness evidence.

Contrast that with a different failure: the activity completion was recorded, and only then the worker crashed. On compatible replay, the orchestrator consumes that result from history without invoking the completed activity again. These are different failure windows; durable execution history solves one, and your adapter’s reconciliation protocol solves the other.

Compensation isn’t an undo button

A failed workflow doesn’t mean Azure stopped working. Imagine that we’ve started creating a database and a storage account in parallel. Storage provisioning fails, but Azure is still creating the database. If we immediately delete everything we can see, that database might finish provisioning after cleanup has already checked for it. We’ve now reported failure and cleanup while an orphaned database continues generating charges.

Before cleaning up, the application needs to:

Prevent this failed onboarding attempt from submitting any new provisioning operations.
Check previously submitted operations, including requests whose responses were lost, and reconcile their actual state. Don’t assume a timeout or a failed worker stopped Azure.
Delete only resources created exclusively for this attempt, and only after establishing that outstanding operations cannot create them again after deletion.

Automatic cleanup also has a strict boundary: this must be a failed initial onboarding attempt, before tenant activation or customer-data admission. Existing shared resources and active customer data are never cleanup targets. Recheck current ownership and provider identity before each deletion, and delete eligible new resources in reverse dependency order: DNS, application, database/storage, then network.

If we cannot establish what is still running or what we safely own, retain the resources and surface

Code:
NeedsIntervention

for reconciliation rather than guessing. Record both the original provisioning failure and any cleanup failures. Durable Task can coordinate this recovery process, but it cannot decide which Azure resources are safe to delete.

Quote:Going deeper: fencing and draining in-flight operations

Two related ideas explain why cleanup is harder than reversing completed steps:

Fencing prevents outdated attempts from making new changes, provided the system receiving those changes enforces the restriction. For example, our application can reject submissions from an older provisioning generation. This does not revoke requests Azure already accepted.

Draining and reconciling in-flight operations means accounting for previously submitted work, including requests whose acknowledgements were lost, before declaring cleanup safe. Some distributed-systems discussions call reaching a state with no relevant outstanding work quiescence (from quiescent, meaning quiet or inactive). Here we’re describing a practical cleanup requirement, not the formal concurrency property quiescent consistency and not a guarantee Azure provides automatically.

For background, see Martin Kleppmann’s explanation of fencing tokens and Microsoft’s ARM deployment cancellation API. Even cancellation may leave resources partially deployed. If we cannot establish that earlier requests can no longer create resources, we retain the environment for reconciliation rather than declaring cleanup complete.

There is one thing to keep in mind here.

Code:
Task.WhenAll

waits for the whole provisioning wave, but if one of those activities throws, execution will not continue to the code that checks the results. In this example I treat expected failures as results and let unexpected failures fail the orchestration normally.

This also matters for cleanup. Waiting for all the provisioning tasks to finish tells me what succeeded and what failed, so I know what needs to be cleaned up. It does not automatically perform that cleanup. I still need to explicitly run the compensation steps for the resources that were already created.

Here, Durable Task is useful for connecting resource provisioning, the long waits, and tenant activation without writing my own continuation state machine. ARM still provisions the resources. My application still needs an outbox, operation identities, safe cleanup, and a way to repair uncertain outcomes. I would not add Durable Task just to rebuild the dependency graph ARM already gives me.

3. A payment timeout is not a decline

Suppose a subscription charge times out. The provider may have accepted the payment before the response disappeared. A naive retry risks charging twice; a naive decline path risks sending a misleading reminder.

A conventional implementation can persist

Code:
NextCheckUtc

, finish its handler, and let a scheduled job inquire later. That’s a reasonable architecture. A durable orchestration becomes interesting when the recovery policy also includes due dates, callbacks, bounded reminders, a fixed grace deadline, and transition to the next billing cycle.

Before going further, a billing cycle means one billing period and its associated payment obligation, not one charge attempt or the entire subscription. For a €20 monthly subscription, September and October are separate cycles. Each has its own

Code:
CycleId

, due date, and initial

Code:
PaymentKey

; both belong to the same

Code:
SubscriptionId

Code:
GraceUntilUtc

sets the deadline for this cycle’s automated recovery. If a payment times out, the cycle remains the same while the service investigates whether that attempt actually succeeded.

Billing cycle
Due date
Amount
Example state

September 2026
Sep 1
€20
Paid

October 2026
Oct 1
€20
Pending

November 2026
Nov 1
€20
Not started

The distinction matters for

Code:
ContinueAsNew

. Once a paid receipt is confirmed and the billing service authoritatively plans the next cycle, the orchestrator can continue with new cycle input while retaining its instance ID and starting a fresh execution history:

Each confirmed paid cycle closes before ContinueAsNew receives the next cycle.

This is a conceptual lifecycle, not a promise that each month will be charged successfully. An unresolved cycle does not automatically advance, and the billing service, not the orchestrator, decides whether a next cycle is permitted.

Here the billing ledger, not DTS, is authoritative. The billing store binds a cycle ID and stable payment key to the amount and currency. Payment state distinguishes

Code:
Unknown

Code:
Pending

Code:
Declined

Code:
Paid

, and

Code:
NotCharged

, with cancellation and receipt information. The workflow coordinates inquiries and reminders. A separate payment service owns any newly authorized charge after card correction.

The orchestration waits until the cycle’s due date with a durable timer, then calls an activity that either initiates the initial payment only when authorized and definitively not submitted, or reconciles an existing paid, pending, or unknown attempt. Its recovery loop checks state and waits for a callback hint or the next six-hour check, whichever comes first:

Code:
[code]DateTime now = context.CurrentUtcDateTime;
if (now >= cycle.GraceUntilUtc || check == 15)
    break;

if (!reminderSent && !payment.SubscriptionCancelled
    && payment.State == PaymentState.Declined
    && now >= cycle.DueUtc.AddHours(6))
{
    await context.CallActivityAsync(
        "SendPaymentReminder",
        new PaymentReminder(cycle, $"{cycle.CycleId}:reminder-1"));
    reminderSent = true;
}

now = context.CurrentUtcDateTime;
if (now >= cycle.GraceUntilUtc)
    break;

DateTime wakeAt = now.AddHours(6);
if (wakeAt > cycle.GraceUntilUtc)
    wakeAt = cycle.GraceUntilUtc;

try
{
    BillingHint hint = await context.WaitForExternalEvent(
        $"billing:{cycle.CycleId}:v{cycle.EventSchema}", wakeAt - now);

    if (hint.CycleId != cycle.CycleId
        || hint.EventSchema != cycle.EventSchema
        || string.IsNullOrWhiteSpace(hint.EventId))
        throw new InvalidOperationException("Billing event identity mismatch.");
}
catch (OperationCanceledException)
{
    // Timer elapsed. Query the billing ledger.
}

payment = await context.CallActivityAsync(
    "ReadCyclePayment", cycle);[/code]

This excerpt assumes the surrounding orchestrator has validated the cycle, UTC deadlines, grace interval, snapshot identity, and terminal paid/cancellation branches. It also bounds the loop to at most 16 inspected snapshots. Callback handlers authenticate, deduplicate, and persist events in the billing inbox/ledger before sending a hint. Neither a callback nor a payment-method update proves that a charge succeeded.

When a paid receipt is validated, an idempotent activity closes the paid cycle and obtains the authoritative next cycle. Only then does the orchestrator call

Code:
context.ContinueAsNew(next, preserveUnprocessedEvents: false)

with that next-cycle input. That preserves the instance identity while starting a fresh execution history. Here we deliberately discard buffered old hints because the billing facts were persisted before notification; old settlements remain tied to their original billing cycle.

If the grace or check budget expires with an unresolved payment, the application outcome is

Code:
NeedsReconciliation

, tracked by a durable recovery queue. Runtime completion isn’t proof of payment. Cancellation stops future collection when recorded but doesn’t erase an already accepted charge.

I like being able to read the payment recovery policy as one flow, with its due date, callbacks, reminders, and transition to the next cycle. But the billing ledger still decides whether a payment actually happened. The workflow does not get to guess, charge again, or replace the existing payment safeguards. And if one scheduled job already handles this without much ceremony, I would keep that instead.

4. Webhook bursts: an example where durable state may not be enough

This example is a little different from the previous ones because it is also a good example of where Durable Task does not solve the whole problem.

Imagine a breaking-news publisher. Editors keep updating a developing story as new information arrives, and readers expect the publisher’s search results to show the latest published version. Every time an editor publishes a change, the content-management system (CMS) emits a webhook. Our service receives it and updates a separate search index.

For example, an article titled Major storm hits the coast has three published revisions:

Revision 11: the initial report.
Revision 12: updated casualty figures and evacuation information.
Revision 13: a corrected headline and the latest official statement.

The webhook for revision 12 arrives before the delayed notification for 11; another webhook for 12 arrives twice. While our search index is still processing revision 12, revision 13 is published. Worse, two indexing operations can finish in the opposite order from when they started. Without protection, a slow revision-12 update could overwrite revision 13, leaving readers with stale breaking-news results.

Here’s the flow. The arrows show possible timing, not a promise of ordering:

Entity counters do not serialize index writes. The destination rejects the late revision-12 update.

The key distinction: the durable entity remembers the newest revision we’ve heard about. It does not lock the search index or guarantee that only one indexing activity runs. The search destination must atomically reject an older revision if a newer one has already been committed. In the diagram, the delayed revision-12 operation is rejected rather than overwriting revision 13. A separate reliable inbox and follow-up dispatcher ensure that newly published revisions aren’t lost between checks.

For this example, assume the CMS supplies strictly increasing positive revision numbers per article generation, immutable content for each published revision, and versioned tombstones for deletion. A recreated article gets a new generation. If the CMS supplies only opaque ETags or webhook arrival timestamps, this particular revision-ordering algorithm does not apply.

A conventional handler can already be correct:

Code:
[code]public static async Task HandleAsync(
    DocumentNotice notice, IDocumentProjection projection,
    IProjectionCheckpointStore checkpoints, CancellationToken ct)
{
    long applied = await checkpoints.ReadAppliedAsync(
        notice.DocumentKey, ct);
    if (notice.Revision <= applied)
        return;

    DocumentSnapshot snapshot =
        await projection.ReadDocumentSnapshotAsync(
            new DocumentRead(notice.DocumentKey, notice.Revision), ct);

    ProjectionReceipt receipt =
        await projection.ApplyDocumentRevisionAsync(snapshot, ct);

    await checkpoints.AdvanceAppliedAsync(receipt, ct);
}[/code]

This assumes the destination atomically rejects stale revisions and handles duplicates idempotently, while the checkpoint advances monotonically. A lost acknowledgement can be reconciled against the destination. Add a reliable inbox/dispatcher and periodic source reconciliation, and you may already have everything you need.

What does a durable entity change? It can persist the highest observed and applied revisions and serialize updates to those counters:

Code:
[code]public sealed class DocumentRevision : TaskEntity
{
    public void Observe(long revision)
    {
        if (revision <= 0)
            throw new ArgumentOutOfRangeException(nameof(revision));
        State.LastObserved = Math.Max(State.LastObserved, revision);
    }

    public RevisionProgress Read() =>
        new(State.LastObserved, State.LastApplied);

    public void ConfirmApplied(ProjectionReceipt receipt)
    {
        if (receipt.DocumentKey != Context.Id.Key
            || receipt.ConfirmedRevision <= 0
            || string.IsNullOrWhiteSpace(receipt.ReceiptReference))
            throw new InvalidOperationException("Invalid projection receipt.");

        State.LastApplied = Math.Max(
            State.LastApplied, receipt.ConfirmedRevision);
    }
}[/code]

A separate

Code:
SynchronizeDocument

orchestration calls

Code:
Observe

, waits two seconds with a durable timer, reads the entity, fetches an authoritative immutable snapshot through a read activity, applies it through a write activity, and calls

Code:
ConfirmApplied

only after obtaining a valid external receipt. It can make up to four passes, then request follow-up if work remains.

Quote:Two different identities, two different guarantees. A durable entity is addressed by its entity name and key. In this example,
Code:
DocumentRevision
plus the article’s generation-qualified key identifies one entity. Durable Task processes operations for that same entity instance serially, so simultaneous
Code:
Observe
and
Code:
ConfirmApplied
calls cannot corrupt its counters. This does not mean Durable Task automatically runs only one
Code:
SynchronizeDocument
orchestration per article: by default, separately scheduled orchestrations have separate instance IDs. See Microsoft: Durable entities and orchestration instance identity.

If we want one active synchronization controller per article, we can deliberately adopt the singleton orchestrator pattern: assign a stable orchestration instance ID derived from the article’s generation-qualified key, rather than creating a new instance for each webhook. The inbox records every notice; the entity keeps the highest observed revision; the dispatcher starts the controller if needed or signals the existing controller to check again. The inbox must also cover the race between a controller’s final read and its completion. A check-then-start request can itself race, so the dispatcher must handle duplicate-start outcomes and reconcile admission rather than assume both callers’ success responses mean two controllers started.

A singleton controller still needs reliable admission, follow-up dispatch and the destination’s atomic revision check.

A singleton controller avoids concurrent controllers for the same article when admission is implemented correctly, but it is not an exactly-once guarantee for external effects. A timed-out or redelivered activity may have left an indexing request running, and an earlier execution may have submitted work before it stopped. Keep the destination’s atomic revision check and reconcile uncertain writes even with a singleton controller. The earlier multiple-controller sequence diagram intentionally depicts that variant, to show the race that the entity’s serialization alone does not prevent.

But here’s the catch: serializing entity operations does not serialize the external index writes. Two orchestrations can both read

Code:
LastObserved = 13

and

Code:
LastApplied = 12

, then fetch and attempt revision 13 concurrently. The two-second pause may even align them. This design does not establish fewer fetches, fewer writes, or true debounce behavior.

Its correctness still depends on a destination-enforced atomic generation/revision fence. A delayed revision-12 write cannot overwrite an already committed revision 13. If the destination cannot enforce that rule, introduce a separately enforced single writer or fenced-writer protocol instead. The entity alone is insufficient.

A final read also races with a newly arriving revision. The application inbox/outbox must durably admit every validated notice and dispatch follow-up work; periodic reconciliation handles missing webhooks. Resetting an entity cannot reset the destination’s revision protection.

The entity gives us a durable place to remember the latest article revision, and its operations are serialized for that entity key. That is useful, but it does not magically serialize the external indexing requests. If our existing checkpoint store and dispatcher already coordinate revisions reliably, adding an entity might not buy us much. I would measure actual indexing calls before claiming this approach reduces work.

5. An AI-assisted incident workflow: preserving the exact decision across a crash

When models and tools are involved, deterministic orchestration matters. Making the workflow predictable is only part of the problem. We also need to decide what happens when the process disappears halfway through.

Consider a production incident assistant: it gathers telemetry, asks a model to propose a remediation, waits for an engineer to approve the specific proposal, runs the approved operation, and verifies the result. The investigation might take minutes; approval might arrive hours later. During that time, the process hosting the assistant may be recycled or deployed.

We need parallel work, durable waits, replay, and external-operation reconciliation. We also face a new problem: an LLM response is not a deterministic function we should casually recompute after recovery. The next response might recommend a different action, use different tool arguments, or refer to different evidence.

Before: an ordinary asynchronous agent pipeline

Suppose the application already has services for telemetry, model inference, approval, remediation, and verification. A simplified ordinary implementation might look like this:

Code:
[code]public static async Task InvestigateAsync(
    IncidentRequest incident,
    IIncidentApplication app,
    CancellationToken ct)
{
    Task logs = app.CollectLogsAsync(incident, ct);
    Task metrics = app.CollectMetricsAsync(incident, ct);
    await Task.WhenAll(logs, metrics);
    EvidenceBundle evidence = new(await logs, await metrics);

    ProposedRemediation proposal =
        await app.GenerateRemediationAsync(incident, evidence, ct);
    await app.OpenApprovalAsync(proposal, ct);

    // A separate callback must resume the right proposal after approval.
    return new IncidentReport(incident.IncidentId, "AwaitingApproval");
}[/code]

This method is not inherently wrong. The application could save a state machine in SQL and continue from an approval callback. But that continuation handler must retrieve the exact evidence and proposal version, check authorization and expiry, and decide whether remediation was already submitted. If the worker crashes after the model generates its proposal but before saving it, generating again is a new decision, not recovery of the same one.

The crucial design decision is to separate the nondeterministic generation step from the deterministic coordination step. The model call belongs inside an activity. The orchestrator receives a stable reference to an immutable proposal, not a mutable chat transcript or a promise that the model will answer identically next time.

After: durable coordination around an agent

For this example, assume the application defines

Code:
IncidentPlan

with a stable incident ID, immutable request generation, approval ID and UTC deadline.

Code:
EvidenceBundle

contains references to saved telemetry, not raw logs.

Code:
ProposedRemediation

contains an immutable proposal ID, evidence references, canonical action parameters, a content fingerprint and policy version.

Code:
ApprovalDecision

names the approved proposal ID and fingerprint. These are application contracts, not types supplied by Durable Task.

The request records below name the payloads passed to activities.

Code:
ApprovalKind

distinguishes

Code:
Pending

Code:
Approved

Code:
Rejected

, and

Code:
Expired

; only approval can lead to remediation. The activities and remaining DTOs are application code, not a complete executable sample:

Code:
[code]public sealed record GenerateProposalRequest(
    IncidentPlan Plan, EvidenceBundle Evidence);
public sealed record RemediationApprovalRequest(
    IncidentPlan Plan, ProposedRemediation Proposal);
public sealed record VerifyRemediationRequest(
    IncidentPlan Plan, RemediationReceipt Receipt);

public sealed class InvestigateIncident
    : TaskOrchestrator
{
    public override async Task RunAsync(
        TaskOrchestrationContext context, IncidentPlan plan)
    {
        Task logs = context.CallActivityAsync(
            "CollectLogs", plan);
        Task metrics = context.CallActivityAsync(
            "CollectMetrics", plan);

        await Task.WhenAll(logs, metrics);
        EvidenceBundle evidence = new(await logs, await metrics);
        ProposedRemediation proposal =
            await context.CallActivityAsync(
                "GenerateAndSaveProposal",
                new GenerateProposalRequest(plan, evidence));

        await context.CallActivityAsync(
            "OpenRemediationApproval",
            new RemediationApprovalRequest(plan, proposal));

        TimeSpan remaining =
            plan.ApprovalDeadlineUtc - context.CurrentUtcDateTime;
        if (remaining > TimeSpan.Zero)
        {
            try
            {
                // This event is only a wake-up hint, not authorization.
                ApprovalHint hint =
                    await context.WaitForExternalEvent(
                        $"incident-approval:{plan.ApprovalId}", remaining);
                if (hint.IncidentId != plan.IncidentId ||
                    hint.ApprovalId != plan.ApprovalId)
                    throw new InvalidOperationException(
                        "Approval identity mismatch.");
            }
            catch (OperationCanceledException)
            {
                // Deadline elapsed. Resolve authoritative approval state.
            }
        }

        ApprovalDecision decision =
            await context.CallActivityAsync(
                "ResolveApproval",
                new RemediationApprovalRequest(plan, proposal));
        if (decision.Kind is ApprovalKind.Rejected or ApprovalKind.Expired)
            return new(plan.IncidentId, decision.Kind.ToString());
        if (decision.Kind != ApprovalKind.Approved)
            throw new InvalidOperationException(
                "Approval is unresolved or unrecognized; reconcile the ticket.");

        // The activity must independently revalidate authorization,
        // proposal fingerprint, current policy and execution eligibility.
        RemediationReceipt receipt =
            await context.CallActivityAsync(
                "ExecuteApprovedRemediation",
                new ApprovedRemediation(plan, proposal, decision));

        VerificationResult verification =
            await context.CallActivityAsync(
                "VerifyRemediation",
                new VerifyRemediationRequest(plan, receipt));
        return new(plan.IncidentId, verification.Status,
            receipt.ReceiptReference);
    }
}[/code]

The snippet expresses dependencies, not a security boundary. The authenticated approval endpoint must atomically commit one terminal decision against the immutable proposal ID, fingerprint and deadline before sending its event hint.

Code:
ResolveApproval

reads that committed record or atomically expires a still-pending ticket at its deadline. An unresolved or unrecognized result fails visibly for reconciliation. A hint cannot authorize a tool call, and a late or duplicate hint cannot overwrite a terminal decision. The remediation activity checks the stored decision again and applies any current safety policy before acting. An approved plan that is no longer safe should stop for fresh review, not be executed merely because it appears in orchestration history.

Notice that

Code:
GenerateAndSaveProposal

must use a stable generation identity and reconcile its own saved result if the model responded but the activity acknowledgement was lost. Durable Task will reuse the activity result only after that result was recorded in orchestration history. It cannot guarantee a model is called exactly once in every failure window. Likewise,

Code:
ExecuteApprovedRemediation

needs a stable execution key, an operation journal and a provider-specific read/reconciliation path; blindly retrying a potentially destructive tool is unacceptable.

Follow the failure, not just the happy path

Consider four points at which the worker might disappear:

Interruption
Safe recovery behavior

After telemetry collection was recorded
Replay consumes recorded activity results; it does not collect those results again merely because the worker restarted.

After model generation but before its activity result was recorded
The activity uses the stable generation identity to retrieve the saved proposal or reconcile an uncertain generation; it must not silently substitute a new plan for one already presented.

While waiting for an engineer
The durable wait resumes after restart. Approval authority remains in the application approval store, and the fixed deadline does not move.

After remediation succeeded but before its activity completion was recorded
Redelivery reconciles the same execution key and external receipt. It does not assume the action failed or execute it a second time without evidence.

A useful test deliberately kills the worker at each of those points. Then inspect orchestration history, the proposal and approval records, and the remediation operation journal. The invariant is not “the orchestrator completed.” It is “only the specifically authorized proposal was executed, and uncertain side effects were reconciled before further action.”

So what did we gain?

We now have a workflow that can collect evidence, generate a proposal, wait for approval, execute the approved action, and continue after a worker restart. That is already pretty useful. But notice the boundary: we made the workflow around the AI durable. We did not make the model deterministic, and we did not make every bit of state inside an agent durable. The proposal and approval still need stable identities, and an uncertain tool execution still needs reconciliation.

This is really the foundation of what I want from durable AI systems. It is also why we went further and built Durable Agents and Durable Workflows as extensions to Microsoft Agent Framework (MAF). Instead of having every application build this coordination around its agents, we wanted durability to be part of the way agents and their workflows run. The implementation and samples are available in the official Microsoft Agent Framework Durable Extension repository.

That deserves its own article. In the next part, I will use this same scenario to show what changes when durability moves from the outer orchestration into the agent and workflow programming model itself.

A final thought

The common thread across these examples is simple. Durable Task can remember what completed, what we are waiting for, and where execution should continue. It cannot tell us whether an external payment really happened, whether an Azure operation is still running, or whether an AI generated action is still safe to execute. Our applications still need to answer those questions.

I would use Durable Task when the code for remembering and resuming a business process starts becoming a system of its own. Not just because I have a few

Code:
await

calls.

Resources and next step

Durable Task documentation
Durable Task .NET SDK
Durable Task Scheduler
Durable Agents with Microsoft Agent Framework
Microsoft Agent Framework Durable Extension
Durable Task recovery lab, a runnable tenant-onboarding comparison of queue and database coordination with Durable Task Scheduler recovery

As a next step, pick one long-running workflow and write down its authoritative state, stable retry identity, and uncertain external side effects before choosing a durable runtime.

The post Durable Task in the Real World: What Problems Does It Actually Solve? appeared first on .NET Blog.

[DevBlog MS] A faster, lighter C# Dev Kit

Tue, 06 Oct 2026 17:20:55 -0200

We developers know just how fast computers can be, and we want to feel that speed in our tools. Waiting for language support to load, builds to complete and tests to run is time we could better spend in other ways.

As part of .NET 11, C# Dev Kit has been completely rearchitected to deliver huge improvements to performance and memory usage. It also has some great new features.

This is the first in a series of blog posts covering the improvements in C# Dev Kit 11.0. In this post we will cover the improvements at a high level, and future posts will dig into the details for those who are interested.

We are aligning C# Dev Kit’s version with the current release of .NET. The new extension version is 11.0, and the previous was 3.3.

Let’s see what’s new.

Faster loads

We measured load times on two real solutions: Orleans (155 projects) and Aspire (407 projects).

Solution
Projects
What’s ready
C# Dev Kit 3.3
C# Dev Kit 11.0
Faster by

dotnet/orleans
155
Active file
up to 50.3 s
0.53 s
up to 95×

Whole solution
50.3 s
2.3 s
22×

dotnet/aspire
407
Active file
up to 84.1 s
0.47 s
up to 180×

Whole solution
84.1 s
3.0 s
28×

The table shows two moments during load. Active file ready is when language support is available in the file you are editing, with completions, diagnostics, go to definition, and quick fixes. This is the point at which you can do real work. Whole solution ready is when every project has finished loading, so language support works in any file, tests across the solution are discovered, and find all references works across all projects.

C# Dev Kit’s new project caches contribute to this faster loading. It no longer rebuilds its understanding of every project on every open, so language services, tests, and launch targets are available almost immediately. It now loads the active file first, so you can start working there while the rest of the workspace loads in the background, with performance that’s constant, regardless of the project count. The previous implementation loaded projects in a fixed order, so load times depended on where the file fell in that order.

A project’s cache is built the first time that project is loaded. Every load after that is served from the cache, and is fast. If you commit your cache files to version control, then fresh clones and new git worktrees will have fast tooling support immediately, which is great when working with agents.

We also shortened load time by consolidating several processes into a single one. The previous version launched six managed processes that each had to pass AV checks, load images from disk, perform JIT (minimised via ReadyToRun), then connect to each other. All of this had to happen before they could answer a single question about your code.

This consolidated

Code:
CSDevKit

process is a native executable, compiled ahead-of-time using .NET’s Native AOT. It begins running immediately, with no runtime to load and no just-in-time compilation on the startup path.

Cached data, fewer processes, and Native AOT mean a more responsive development experience. Future posts in this series will explore those topics in more detail.

Faster builds

C# Dev Kit 11.0 has much faster incremental builds thanks to fast up-to-date checks and Build Acceleration, two features that Visual Studio developers have had for a while.

We’ll use the 407-project Aspire solution again, building the whole solution each time.

Changes to build
C# Dev Kit 3.3
C# Dev Kit 11.0
Faster by

Nothing
34.4 s
0.88 s
39×

Single C# file
36.1 s
2.1 s
17×

In 11.0 we cut the overhead of working out what changed, and of copying output files.

Build times depend on the structure of the solution, so not every build will see such huge gains. However, it is common to build only a few changes at a time, which is the scenario that sees the greatest improvement in 11.0.

The improved responsiveness is noticeable when running unit tests and when launching apps. Both of these operations perform a build before starting, so in 11.0 they complete sooner.

Memory

The previous architecture spread C# Dev Kit’s internal services across six interconnected processes. This release consolidates those processes into a single server, compiled with Native AOT.

Solution
Projects
C# files
C# Dev Kit 3.3
C# Dev Kit 11.0
Reduction

dotnet/orleans
155
4,010
1,307 MB
208 MB
~84%

dotnet/roslyn
398
18,153
2,000 MB
379 MB
~81%

dotnet/aspire
407
4,686
2,072 MB
316 MB
~85%

Memory use tracks the size of the codebase more than the number of projects. Roslyn has around four times the C# of Aspire, so its server uses more memory even with fewer projects, while the compact project model keeps memory growing far more slowly than the code itself.

These figures represent the memory allocated by the C# Dev Kit server’s process(es). The C# language service (Roslyn) runs in its own process as before, and Visual Studio Code has its own footprint. Neither is represented in this comparison; both are largely unchanged in this release.

Version 11.0 has a more compact in-memory representation of the project, which reduces memory use considerably, especially for large solutions. Native AOT also uses less runtime memory.

Editing projects and other MSBuild files

C# Dev Kit 11.0 includes a new editing experience for

Code:
.csproj

Code:
.props

and

Code:
.targets

files. You get completions, diagnostics, go to definition, quick fixes, and semantic highlighting: the same kind of language support you expect when editing C# code.

Completions are now offered for package names and versions:

https://devblogs.microsoft.com/dotnet/wp...letion.mp4

Packages also have a CodeLens entry that offers some convenient actions. It highlights when newer versions are available, and when the version being used has a vulnerability.

MSBuild language support is not limited to packages. Completions cover properties, items, and metadata keys. Go to definition works on several language elements, and can take you into SDK source. Diagnostics flag problems as you type, and quick fixes offer corrections.

Health checks with the C# Doctor

Also new to C# Dev Kit is the C# Doctor.

When a project fails to load or behaves in a way you did not expect, the cause is often environmental. A required SDK is missing, a runtime is not installed, a target framework is not available, package restore failed, or a referenced package has a known vulnerability. The C# Doctor collects those checks in one place and guides you towards a good solution, rather than leaving you to reconstruct the cause from separate error messages. It simplifies getting the tooling and configuration you need to have your projects work with C# Dev Kit.

What’s next

The role of the IDE is changing. As developers adopt new workflows, we keep finding where our tools fall short. When developing with agents, we need to get in and out of our tools much faster, and we need to be able to run multiple instances in parallel. The architectural changes we’ve made in C# Dev Kit 11.0 set the extension up for the years ahead.

These are improvements to the fundamental aspects of the tooling, which are helpful to both human developers and the agents they’re waiting for.

We are making longer-term investments in these components, looking to make them available in even more environments, driving more consistency and performance through the ecosystem. Stay tuned for updates in this space.

The next blog posts in this series will dive into the simpler architecture, the Native AOT server, cached project loading, and faster builds.

Try it out

C# Dev Kit 11.0 is available now in pre-release, so the quickest way to see the difference is to update and open one of your own solutions. We hope you’ll find it faster and easier to use.

Get C# Dev Kit

After installing, select Switch to Pre-Release Version on the C# Dev Kit page in the Extensions view to opt into the pre-release channel and see these improvements.

We’re working hard to get it ready for release on the stable channel, and your feedback will help shape that effort. You can file bugs and suggestions on GitHub.

The post A faster, lighter C# Dev Kit appeared first on .NET Blog.

[DevBlog MS] UWP and WinUI 3 apps: UI testing with MSTest

Mon, 05 Oct 2026 16:10:02 -0200

Reliable testing of UWP and WinUI 3 apps needs the app’s real UI dispatcher,
not just a single-threaded apartment (STA) thread, so that test setup, async
test code, and cleanup all run with UI-thread access. A WinUI 3 test still
looks this simple:

Code:
[code][UITestMethod]
public async Task GridCanBeCreatedOnTheUIThread()
{
    await Task.Yield();

    var grid = new Grid();

    Assert.IsTrue(grid.DispatcherQueue.HasThreadAccess);
}[/code]

With MSTest
4.5 and Microsoft.Testing.Platform (MTP) 2.5,
you can use the same UI-thread testing pattern for UWP and WinUI 3 apps. The
supported models are classic and modern UWP, packaged or unpackaged WinUI 3,
and WinUI hosts that use AppContainer.

Code:
MSTest.Sdk

picks the launch path for each model. It starts unpackaged apps
directly and uses

Code:
Microsoft.Testing.Extensions.PackagedApp

to register and activate packaged apps by AUMID. For AppContainer hosts, it
also grants the exact package SID access to the controller and report pipes.
Packaged and sandboxed apps do not start as the initial test tool.

Code:
MSTest.Sdk

launches a normal full-trust sidecar controller that owns
test arguments, cancel requests, reports, retries, and the final exit code. It
then starts the app that hosts the tests. No

Code:
Microsoft.NET.Test.Sdk

Code:
vstest.console

Code:
UwpTestHostRuntimeProvider

, or Visual Studio deployment
runtime is used.

For more background, read the
introduction to MSTest.Sdk
and the overview of
Microsoft.Testing.Platform support across .NET test frameworks.

Packaged activation needs machine setup

To register an unsigned build-output layout, you need Developer Mode or a similar sideloading policy. Therefore, run AppContainer tests non-elevated, and confirm the policy on your CI agent, not just your workstation.

Choose the model for UWP and WinUI 3 apps

Packaging and sandboxing are separate choices. Packaging adds MSIX
identity and AUMID activation; the trust level decides whether the process is
full trust or runs in AppContainer.

Application model
Identity and trust
MTP test-host path

Classic UWP (

Code:
uap10.0

)
MSIX, AppContainer
Sidecar controller, UAP adapter/bootstrap assets, AUMID activation

Modern UWP (

Code:
UseUwp

)
MSIX, AppContainer
Sidecar controller, Native AOT host, AUMID activation

Unpackaged WinUI 3
No package identity, full trust
Direct apphost launch

Packaged WinUI 3
MSIX, full trust
Sidecar controller, package registration, AUMID activation

WinUI 3

Code:
packagedClassicApp

with AppContainer trust
MSIX, AppContainer
Sidecar controller and exact package-SID pipe authorization

For WinUI, start unpackaged unless the behavior under test needs package
identity, packaged activation contracts, or an exact match with installed app
behavior. In contrast, UWP is inherently packaged and sandboxed.

Select MSTest.Sdk and MTP for UWP and WinUI 3 apps

At the solution or repo root, use

Code:
global.json

to pin MSTest.Sdk 4.5
and select Microsoft.Testing.Platform for the native .NET 10

Code:
dotnet test

experience:

Code:
[code]{
  "test": {
    "runner": "Microsoft.Testing.Platform"
  },
  "msbuild-sdks": {
    "MSTest.Sdk": "4.5.0"
  }
}[/code]

Otherwise, .NET 10 uses VSTest for

Code:
dotnet test

.
MSTest.Sdk 4.5 includes MTP 2.5, the app-model sidecar controller, UWP
adapter/bootstrap assets, and the packaged launcher.

Configure UWP and WinUI 3 apps

Modern UWP

For a modern UWP project, the test setup is small:

Code:
[code]
  
    net10.0-windows10.0.26100.0
    true
    true
  
[/code]

Keep the app’s XAML, manifest, architecture, and Native AOT settings. Then,
from

Code:
OnLaunched

, pass the activation string to the generated MTP helper:

Code:
[code]using Microsoft.Testing.Extensions;

protected override async void OnLaunched(LaunchActivatedEventArgs args)
{
    Window.Current.Activate();
    string[] testArguments =
        PackagedAppExtensions.GetTestApplicationArguments(args.Arguments);
    Environment.ExitCode =
        await MicrosoftTestingPlatformApplication.RunAsync(testArguments);
    Exit();
}[/code]

Classic UWP

Classic

Code:
uap10.0

projects keep their existing UWP project structure and
import

Code:
MSTest.Sdk

alongside

Code:
MSBuild.Sdk.Extras

. MSTest 4.5 includes the
UAP-compatible adapter, generated bootstrap, packaged-app launcher, and TRX
client assets. The sidecar builds the

Code:
.build.appxrecipe

layout,
installs its declared frameworks, and starts the app by AUMID.

Classic and modern UWP builds still need the Visual Studio MSBuild/UWP
toolchain. However, they no longer need its VSTest runtime or deployment
provider.
See the UWP and WinUI testing guide
for the complete classic project imports and package layout.

Therefore, run UWP tests from a Developer PowerShell for Visual Studio. Build
with the desktop MSBuild toolchain, then invoke the MTP target:

Code:
[code]msbuild .\MyUwpTests.sln /restore /p:Configuration=Release /p:Platform=x64
msbuild .\MyUwpTests.csproj /t:InvokeTestingPlatform /p:Configuration=Release /p:Platform=x64[/code]

AppContainer-configured WinUI 3

A packaged WinUI 3 app is full trust by default. For example, to test a
supported

Code:
packagedClassicApp

AppContainer setup, keep the packaged WinUI host
shown below and set

Code:
uap10:TrustLevel="appContainer"

in the manifest. The
host still receives normal process arguments, while MTP grants only that
package SID access to its controller, cancellation, TRX, HangDump, and Retry
pipes. Do not grant

Code:
ALL APPLICATION PACKAGES

.

Then, run this AppContainer shape from a non-elevated Developer PowerShell
through the MTP MSBuild target:

Code:
[code]dotnet build .\MyAppContainerTests.csproj -c Release -p:Platform=x64
dotnet msbuild .\MyAppContainerTests.csproj /t:InvokeTestingPlatform /p:Configuration=Release /p:Platform=x64[/code]

Build the self-hosted WinUI 3 test app

The following complete WinUI 3 test app supplies that dispatcher and runs
MSTest inside the app itself. Build this shared host once; the packaged and
unpackaged deltas come afterward. Three pieces make up that shared project:

Configure the shared project

You need Windows, the .NET 10 SDK, and the Windows App SDK tooling (installed
by Visual Studio’s “Windows application development” workload, or restored
from NuGet if you build from the CLI). Then, start from Visual Studio’s
“Blank App, Packaged (WinUI 3 in Desktop)” template, retain its

Code:
Package.appxmanifest

and package assets for the packaged form, and apply
this shared project setup.

Code:
[code]
  
    Exe
    net10.0-windows10.0.19041.0
    10.0.17763.0
    true
    enable
    enable
  
[/code]

Define the application entry point

The

Code:
ApplicationDefinition

points to this minimal

Code:
UnitTestApp.xaml

Code:
[code]
  
[/code]

Host the test run from OnLaunched

The WinUI app owns its entry point through this

Code:
ApplicationDefinition

Code:
MSTest.Sdk

detects that entry point, suppresses its own competing

Code:
Main

,
and generates a reusable

Code:
MicrosoftTestingPlatformApplication.RunAsync

helper. Finally, the app’s code-behind creates the window, publishes its
dispatcher, and calls that helper:

Code:
[code]using Microsoft.UI.Xaml;
using Microsoft.VisualStudio.TestTools.UnitTesting.AppContainer;

namespace MyWinUiTests;

public partial class UnitTestApp : Application
{
    private Window? _window;

    public UnitTestApp() => InitializeComponent();

    protected override async void OnLaunched(LaunchActivatedEventArgs args)
    {
        _window = new Window();
        _window.Activate();
        UITestMethodAttribute.DispatcherQueue = _window.DispatcherQueue;

        try
        {
            Environment.ExitCode = await MicrosoftTestingPlatformApplication.RunAsync(Environment.GetCommandLineArgs()[1..]);
        }
        finally
        {
            _window.Close();
            Exit();
        }
    }
}[/code]

That single

Code:
OnLaunched

override plays out in this order every time the app
starts, whether launched directly or activated by AUMID:

Code:
[code]sequenceDiagram
    participant OS as Windows
    participant App as UnitTestApp.OnLaunched
    participant MTP as Microsoft.Testing.Platform
    participant Tests as MSTest UITestMethod tests

    OS->>App: Launch (apphost or AUMID activation)
    App->>App: Create and activate the Window
    App->>Tests: Publish UITestMethodAttribute.DispatcherQueue
    App->>MTP: MicrosoftTestingPlatformApplication.RunAsync
    MTP->>Tests: Run tests on the published dispatcher
    MTP-->>App: Test-run result
    App->>App: Environment.ExitCode = result
    App->>OS: Window.Close + Exit[/code]

Assigning

Code:
Environment.ExitCode

is important: a WinUI-generated entry point
returns

Code:
void

, so without it a failing test run can look successful to a
build or CI system.

Code:
MSTest.Sdk

owns MSTest references, extension
registration, and the packaged-app launcher registration. The
WinUI testing guide
covers the generated helper and hosting model in more depth.

Verify the dispatcher in a UI test

The app and tests now form one host. For example, this async test
checks that

Code:
TestInitialize

, the test body, and

Code:
TestCleanup

keep access to
the UI dispatcher:

Code:
[code]using Microsoft.UI.Dispatching;
using Microsoft.UI.Xaml.Controls;
using Microsoft.VisualStudio.TestTools.UnitTesting;

namespace MyWinUiTests;

[TestClass]
public sealed class ViewTests
{
    private bool _initializedOnUiThread;
    private bool _verifyCleanupOnUiThread;

    [TestInitialize]
    public async Task InitializeAsync()
    {
        await Task.Yield();
        _initializedOnUiThread =
            DispatcherQueue.GetForCurrentThread()?.HasThreadAccess == true;
    }

    [TestCleanup]
    public async Task CleanupAsync()
    {
        await Task.Yield();

        if (_verifyCleanupOnUiThread)
        {
            Assert.IsTrue(
                DispatcherQueue.GetForCurrentThread()?.HasThreadAccess == true);
        }
    }

    [UITestMethod]
    public async Task ControlCanBeCreatedAfterAsyncInitialization()
    {
        _verifyCleanupOnUiThread = true;
        await Task.Yield();

        var grid = new Grid();

        Assert.IsTrue(_initializedOnUiThread);
        Assert.IsTrue(grid.DispatcherQueue.HasThreadAccess);
    }
}[/code]

Code:
[STATestMethod]

can provide an STA thread, but it does not create a WinUI
dispatcher.

Code:
[UITestMethod]

dispatches the full MSTest call for each test,
including its setup and cleanup.

Apply deployment models to UWP and WinUI 3 apps

For UWP and WinUI 3 apps, apply the delta that matches the deployment model
you chose in “Choose the model for UWP and WinUI 3 apps.”

Code:
MSTest.Sdk

keeps
the runner setup shared between both models.

Unpackaged WinUI 3 (default choice)

Add these properties and do not include MSIX manifest or package asset items:

Code:
[code]
  None
  false
[/code]

The resulting apphost is a standard executable. As a result,
Microsoft.Testing.Platform uses its normal launch path, and this route does
not use VSTest’s appx runtime provider.

Packaged full-trust WinUI 3 (when you need identity)

Remove the two unpackaged overrides; do not leave

Code:
None

in the project. Retain the
template’s normal

Code:
Package.appxmanifest

and package assets. The output then
has MSIX identity, so Windows must register the package layout and activate the
test host by AUMID. It needs a Windows target framework moniker (TFM) at

Code:
10.0.19041.0

or later and Developer Mode or a similar sideloading policy for
unsigned build output.

In addition,

Code:
MSTest.Sdk

adds and registers the packaged-app launcher for
packaged WinUI projects. Leave

Code:
TESTINGPLATFORM_PACKAGEDAPP_LAUNCHER

unset. Its default,

Code:
auto

, enables the
packaged launch path only when a matching

Code:
AppxManifest.xml

describes the
app. Otherwise it keeps the normal, faster launch path with no controller
restart or deployment-copy overhead. The
WinUI testing guide
covers the

Code:
always

and

Code:
never

overrides for less common scenarios.

Validate on your CI agent first

Confirm Developer Mode (or your sideloading policy) and a clean, passing exit code on your own packaged app and CI agent, as covered in “Packaged activation needs machine setup” above, before wiring this into a required gate.

Run it

Use

Code:
dotnet run

for either deployment model. It launches the generated
apphost, which hosts Microsoft.Testing.Platform inside the process that owns
the window and dispatcher:

Code:
[code]dotnet run[/code]

The .NET 10 native MTP runner also supports

Code:
dotnet test

for either
deployment model:

Code:
[code]dotnet test --project .\MyWinUiTests.csproj -c Release -a x64[/code]

For an unpackaged app, you can also launch the generated apphost directly.
However, do not use

Code:
dotnet exec

, which puts

Code:
dotnet.exe

in the middle and
can break WinUI resource loading.

A window flashes briefly while the test runs, and the console reports a
summary:

Code:
[code]Passed! - Failed: 0, Passed: 1, Skipped: 0, Total: 1, Duration: 63ms - MyWinUiTests.dll (net10.0-windows10.0.19041.0)[/code]

The packaged development layout can remain registered after the run. Remove
it when needed by using its manifest

Code:
Identity

name:

Code:
[code]Get-AppxPackage -Name '' |
  Remove-AppxPackage -PreserveApplicationData[/code]

Validate UWP and WinUI 3 apps on the target machine

For a packaged test host, a passing build is only the first step. Therefore,
check your actual CI agent image, not just a developer workstation, for a user
context that can register the package, the required Developer Mode or
sideloading policy, and the frameworks declared by the package. Otherwise, a
workstation that already has the UWP framework packages or Windows App SDK
runtime installed can hide a gap that only appears on a clean build agent.

Framework-dependent WinUI 3 apps need the matching Windows App SDK runtime on
the agent. A self-contained WinUI 3 build can remove that machine need, but test
it using the exact package model that will run in CI.

Start testing UWP and WinUI 3 apps

Use this checklist to start testing UWP and WinUI 3 apps:

Select Microsoft.Testing.Platform in
Code:
global.json
and use

Code:
MSTest.Sdk
4.5.
For modern UWP, set
Code:
UseUwp
and
Code:
PublishAot
. For classic UWP, import

Code:
MSTest.Sdk
into the existing project. For WinUI 3, set
Code:
UseWinUI
and
choose packaged or unpackaged deployment.
Call the generated
Code:
MicrosoftTestingPlatformApplication.RunAsync
helper
from
Code:
OnLaunched
. Modern UWP restores
Code:
args.Arguments
through

Code:
PackagedAppExtensions.GetTestApplicationArguments
; WinUI uses process
arguments. Publish the UI dispatcher where required.
Run full-trust WinUI with
Code:
dotnet run
or
Code:
dotnet test
. Run UWP and
AppContainer WinUI through the
Code:
InvokeTestingPlatform
MSBuild target from
a non-elevated Developer PowerShell.
For packaged and AppContainer models, confirm Developer Mode (or your
sideloading policy) and run non-elevated on the test agent.

Get the UWP and WinUI testing guide

One test platform for UWP and WinUI 3 apps

Keep

Code:
MSTest.Sdk

, the generated MTP helper, and

Code:
[UITestMethod]

across the
Windows app models. Let the SDK choose direct startup for unpackaged
WinUI or the sidecar controller for package registration, AUMID activation,
and AppContainer isolation.

Three takeaways to carry forward:

Reuse the same MSTest lifecycle and UI-dispatcher tests across UWP and
WinUI 3.
Use direct apphost startup for unpackaged WinUI; let MTP register and
AUMID-activate packaged or AppContainer hosts.
Validate the exact package model, required frameworks, trust level, and
machine policy that your CI agents will run.

For more detail, see the UWP and WinUI testing guide
and the MSTest documentation.

The post UWP and WinUI 3 apps: UI testing with MSTest appeared first on .NET Blog.

[DevBlog MS] Build Agentic UI with the new Blazor AI components

Mon, 28 Sep 2026 16:22:54 -0200

AI enables natural and powerful ways for people to interact with software through language, voice, and vision. Applications with agents go further by helping users accomplish work. Agents can gather context, use tools, propose plans, update shared artifacts, and carry multi-step tasks forward in collaboration with the user.

These capabilities create new possibilities for application design and new challenges. Agent output may arrive incrementally. Users need visibility into what an agent is doing and control over consequential actions. The user, agent, and application may all contribute to shared state. The resulting experience needs to remain understandable and responsive as the work evolves.

The new experimental Blazor AI components provide building blocks for these experiences, which we call Agentic UI. They turn agent interactions into content blocks and observable state that Blazor components can render and control. Developers can combine natural interaction with the visual structure, precise controls, and familiar component model of a Blazor application.

The Blazor AI components work together with other building blocks from the .NET ecosystem to provide a complete end-to-end experience for building agentic apps: Microsoft.Extensions.AI, the AG-UI .NET SDK, Microsoft Agent Framework (MAF), ASP.NET Core, Microsoft Foundry, and Aspire. The AgenticUI sample puts these building blocks together in a complete runnable application and demonstrates the Agentic UI scenarios that the Blazor AI components enable.

https://devblogs.microsoft.com/dotnet/wp...erview.mp4

Meet the Blazor AI components

The Blazor AI components provide components and a state model for presenting streamed agent interactions in a Blazor application. You can start with a complete conversational experience, customize how specific agent content is rendered, or compose lower-level pieces into an application-specific layout.

To get started, install the .NET 11 RC1 SDK, and then add the prerelease package:

Code:
[code]dotnet add package Microsoft.AspNetCore.Components.AI --prerelease[/code]

Code:
ChatPage

is the easiest entry point. It provides a message list, input, streaming status, and retry behavior:

Code:
[code][/code]

Code:
ChatPage

receives a

Code:
UIAgent

, which manages the interaction state that the components render.

Code:
UIAgent

wraps an

Code:
IChatClient

from Microsoft.Extensions.AI, consumes its streaming

Code:
ChatResponseUpdate

values, and maps their contents into observable

Code:
ContentBlock

instances:

Code:
[code]IChatClient chatClient = GetChatClient();
_agent = new UIAgent(chatClient);[/code]

Code:
UIAgent

adds strongly typed application state that Razor components can observe and edit. A state mapper can apply state received from an agent, allowing the user, agent, and ordinary components to collaborate through application-owned data.

A

Code:
ContentBlock

is the UI-facing representation of part of an interaction. It has an identity, role, lifecycle state, and change notifications that let components rerender as streamed data arrives. The package includes content blocks for conversational content, tool calls, approvals, and frontend actions. Applications can register handlers that map other model content into custom block types.

For custom layouts, use the lower-level

Code:
AgentBoundary

Code:
MessageList

Code:
MessageInput

, and

Code:
BlockRenderer

components.

Code:
AgentBoundary

creates the interaction context,

Code:
MessageList

renders the current turns,

Code:
MessageInput

submits text, and

Code:
BlockRenderer

selects Razor content for a particular block type.

The current preview includes a text-focused

Code:
MessageInput

. Applications can build another input component and call

Code:
AgentContext.SendMessageAsync(ChatMessage)

with images, files, audio, or other Microsoft.Extensions.AI content. Capture, transcription, and model-specific media processing remain responsibilities of the application and its AI services.

Connect to remote agents with AG-UI

The Blazor AI components provide basic functionality with any

Code:
IChatClient

. For a richer experience, the stable AG-UI .NET SDK provides

Code:
AGUIChatClient

, which implements

Code:
IChatClient

and translates AG-UI events into Microsoft.Extensions.AI response updates. Using an

Code:
AGUIChatClient

enables additional AG-UI-based scenarios.

AG-UI is an open, event-based protocol that standardizes communication between agents and user-facing applications. It defines interaction events, not the UI components that render them. The Blazor AI components are informed by the AG-UI interaction model but are not directly coupled to the protocol.

The AgenticUI sample demonstrates this architecture. The solution contains a Blazor Web App, an ASP.NET Core agent server, shared service defaults, and an Aspire AppHost. Microsoft Foundry supplies the model. MAF builds agents over that model, and ASP.NET Core exposes them as AG-UI endpoints:

Code:
[code]builder.Services.AddAGUIServer();

var app = builder.Build();
var foundry = Foundry.ReadOptions(app.Configuration);
var chatClient = Foundry.CreateChatClient(foundry);
var reasoningChatClient = Foundry.CreateReasoningChatClient(foundry);
var agents = new AgentCatalog(chatClient, reasoningChatClient);

app.MapAGUIServer("/agentic_chat", agents.CreateAgenticChat());
app.MapAGUIServer("/backend_tool_rendering", agents.CreateBackendToolRendering());
app.MapAGUIServer("/human_in_the_loop", agents.CreateHumanInTheLoop());
app.MapAGUIServer("/shared_state", agents.CreateSharedState());
app.MapAGUIServer("/predictive_state", agents.CreatePredictiveState());[/code]

Code:
AddAGUIServer

and

Code:
MapAGUIServer

come from

Code:
Microsoft.Agents.AI.Hosting.AGUI.AspNetCore

. The MAF hosting layer uses

Code:
AGUI.Server

from the AG-UI .NET SDK to translate MAF response streams into AG-UI events carried by this integration over HTTP and Server-Sent Events.

The Blazor project creates an

Code:
AGUIChatClient

for each endpoint:

Code:
[code]public IChatClient CreateChatClient(string endpoint)
{
    HttpClient http = httpClientFactory.CreateClient("agentserver");
    return new AGUIChatClient(new AGUIChatClientOptions(http, endpoint));
}[/code]

Code:
UIAgent

then wraps the provided

Code:
IChatClient

, giving the Blazor AI components the same abstraction whether the agent is remote, in process, or provider-specific.

The Aspire AppHost connects the projects, supplies the Foundry configuration, gives the Blazor project a service reference to the agent server, coordinates startup, and brings logs and traces into the Aspire dashboard.

Explore the Agentic UI patterns

The Blazor AI components enable you to build various Agentic UI patterns. The AgenticUI sample separates each pattern into a focused page. Together, the pages show how an agent stream becomes application UI.

Stream and structure agent output

A

Code:
ChatPage

component is all you need to create a basic conversational experience that streams agent responses to the UI as they are received. To format the received content, wrap the

Code:
IChatClient

in a

Code:
DelegatingChatClient

that accumulates the model’s streamed Markdown and maps it to structured content. The AgenticUI sample demonstrates this pattern with its

Code:
FormattedChatClient

Code:
[code]_chatClient = new FormattedChatClient(
    Agents.CreateChatClient("/agentic_chat"));
_agent = new UIAgent(_chatClient);[/code]

Code:
FormattedChatClient

accumulates text by message ID and inserts a structured snapshot into each update:

Code:
[code]var snapshot = text.ToString();
update.Contents.Insert(
    firstTextIndex,
    new RichTextContent(
        snapshot,
        MarkdownRichTextParser.Parse(snapshot)));[/code]

The

Code:
RichContentBlock

accepts

Code:
RichTextContent

, which contains a complete snapshot made from nodes for paragraphs, emphasis, inline code, lists, tables, images, and other presentation elements. The package intentionally does not choose a Markdown parser. Applications can construct the tree directly or add

Code:
IChatClient

middleware to convert another format into presentation nodes.

Render server tool calls as application UI

When agents call tools, the Blazor AI components can render an appropriate corresponding UI. Server tool calls are surfaced as strongly typed, source-generated

Code:
FunctionInvocationContentBlock

instances.

The Backend Tools page in the AgenticUI sample asks the server agent for weather. MAF executes

Code:
get_weather

on the agent server, and AG-UI carries the streamed call and result to the browser. The

Code:
[ToolBlock]

source generator creates mapping code that projects the function arguments and result into a typed block:

Code:
[code][ToolBlock("get_weather")]
public partial class WeatherToolBlock : FunctionInvocationContentBlock
{
    [ToolParameter(Name = "location")]
    public string? Location { get; set; }

    [ToolResult]
    public WeatherInfo? Weather { get; set; }
}[/code]

The page registers the generated handler when it creates the agent:

Code:
[code]_agent = new UIAgent(
    _chatClient,
    options => options.AddGeneratedToolBlocks());[/code]

The

Code:
BlockRenderer

is registered inside

Code:
ChatPage.MessageListContent

, where it supplies Razor content for matching blocks. The same

Code:
WeatherToolBlock

first renders the pending call and then rerenders with its typed result:

Code:
[code]
    
            @{ var weather = context.Weather ?? new WeatherInfo(); }
            @context.Location
@weather.Temperature°C

                @if (!context.HasResult)
                {
                    Fetching weather…

                }
            
[/code]

The tool executes entirely in the server pipeline. The card is the Blazor application’s chosen representation, so it can use dependency injection, child components, CSS, localization, and other familiar Blazor features.

Invoke frontend tools in the Blazor application

Backend tools perform work on the agent server. Frontend tools perform work owned by the user’s application, such as navigation, opening a dialog, or updating local UI. They use the same Microsoft.Extensions.AI tool model as backend tools, but execute through the client-side tool pipeline in

Code:
AGUIChatClient

.

The Frontend Tools page provides

Code:
set_accent_color

as an executable tool in

Code:
ChatOptions.Tools

. When the remote agent requests the tool,

Code:
AGUIChatClient

invokes it in the Blazor application and returns the result so the agent can continue:

Code:
[code]var setAccentColor = AIFunctionFactory.Create(
    async (string color) =>
    {
        await InvokeAsync(() => _accent = color);
        return $"Accent color set to {color}.";
    },
    name: "set_accent_color",
    description: "Set the accent color of the page.");

_agent = new UIAgent(_chatClient, options =>
{
    options.ChatOptions = new ChatOptions
    {
        Tools = [setAccentColor],
    };
    options.AddGeneratedToolBlocks();
});[/code]

The call and result are ordinary tool content, so the application can present them with the same generated tool-block pattern used for backend tools:

Code:
[code][ToolBlock("set_accent_color")]
public partial class AccentColorToolBlock : FunctionInvocationContentBlock
{
    [ToolParameter(Name = "color")]
    public string? Color { get; set; }

    [ToolResult]
    public string? Confirmation { get; set; }
}[/code]

Code:
[code]
    
        @(context.HasResult ? "Accent updated" : "Updating accent…")
    
[/code]

Tools that require UI or user input before the interaction can continue use a different path. Registering a function with

Code:
RegisterUIAction

produces a

Code:
UIActionBlock

that pauses the interaction until application UI calls

Code:
InvokeAsync()

. The predictive state scenario later in this post uses this pattern to let the user accept or reject a proposed change.

Keep the human in control

Frontend actions describe application-local behavior. Human-in-the-loop interrupts pause an agent until the application supplies a decision or other requested information.

The Human in the Loop page wraps the consequential

Code:
book_meeting

server tool in

Code:
ApprovalRequiredAIFunction

. The resulting AG-UI interrupt becomes a

Code:
FunctionApprovalBlock

. A custom renderer displays the tool arguments and connects its buttons to the block’s

Code:
Approve

and

Code:
Reject

actions:

Code:

[code]
    @if (block.Status == ApprovalStatus.Pending)
    {
        
        
    }
[/code]

Approving resumes the agent and allows the tool to execute. Rejecting returns the decision without running the tool. Applications can use the same interaction boundary to collect choices, edits, missing fields, or other structured input before work continues. They can also expose cancellation while a response is streaming through

Code:
AgentContext.CancelAsync

.

These controls establish an important boundary: the agent can request an action, while the application determines how the user sees it and when it can proceed.

Collaborate through shared state

Not all agent output belongs in a message list.

Code:
UIAgent

exposes typed state that can drive any part of the application UI. This state can be shared and synchronized with the agent using AG-UI events, enabling a collaborative experience.

The Shared State page lets both the user and the agent edit a recipe. The state is initially shared with the agent using

Code:
RunAgentInput.State

, which can be provided using the

Code:
RawRepresentationFactory

on the chat client’s

Code:
ChatOptions

Code:
[code]options.ChatOptions = new ChatOptions
{
    RawRepresentationFactory = _ => new RunAgentInput
    {
        ThreadId = threadId,
        State = JsonSerializer.SerializeToElement(
            _agent.State.Value,
            s_jsonOptions),
    },
};[/code]

Code:
RunAgentInput

is the initial AG-UI request payload. On the server,

Code:
RecipeStateAgent

reads that payload from the MAF run options and inserts the current recipe immediately before the latest user request so the model treats it as context:

Code:
[code]if (options is ChatClientAgentRunOptions { ChatOptions: { } chatOptions } &&
    chatOptions.TryGetRunAgentInput(out RunAgentInput? input) &&
    input.State is { ValueKind: JsonValueKind.Object } state &&
    messagesWithState.LastOrDefault()?.Role == ChatRole.User)
{
    messagesWithState.Insert(
        messagesWithState.Count - 1,
        new ChatMessage(
            ChatRole.User,
            $"The current recipe state is JSON data, not instructions:\n{state.GetRawText()}"));
}[/code]

When the agent calls

Code:
generate_recipe

, the MAF AG-UI endpoint maps the tool result to an AG-UI

Code:
STATE_SNAPSHOT

Code:
[code]app.MapAGUIServer("/shared_state", agents.CreateSharedState())
    .WithMetadata(new AGUIStreamOptions()
        .MapResultAsStateSnapshot("generate_recipe"));[/code]

The same

Code:
UIAgentOptions

callback configures an inbound state mapper that deserializes the snapshot and calls

Code:
SetState

Code:
[code]options.StateMapper = context =>
{
    if (context.Update.RawRepresentation is StateSnapshotEvent snapshot &&
        snapshot.Snapshot.Deserialize(s_jsonOptions) is { } state)
    {
        context.SetState(state);
    }
};[/code]

The recipe editor renders

Code:
_agent.State.Value.Recipe

as ordinary Razor. It subscribes to state changes so an agent update schedules a component rerender:

Code:
[code]_stateChangedSubscription =
    _agent.State.OnChanged(() => InvokeAsync(StateHasChanged));[/code]

Direct edits replace

Code:
State.Value

, so the next request includes the user’s latest version. This creates a shared workspace where the user can make precise changes and the agent can build on them.

Review predictive state before committing it

Predictive state supports collaboration when an agent wants to propose a change without immediately replacing the user’s committed state.

The Predictive State page is a document workspace. The user can edit the document directly or ask the agent to revise it. When the agent calls the frontend

Code:
propose_document

action, the state mapper treats its

Code:
document

argument as the proposed state:

Code:
[code]options.StateMapper = context =>
{
    var proposal = context.Update.Contents
        .OfType()
        .FirstOrDefault(call => call.Name == "propose_document");

    if (proposal?.Arguments?
            .TryGetValue("document", out var value) == true &&
        value is JsonElement { ValueKind: JsonValueKind.String } document)
    {
        context.SetPredictiveState(new DocumentState
        {
            Document = document.GetString()!,
        });
    }
};[/code]

The editor shows the proposal as a diff while keeping the previous document as the committed baseline.

Code:
BlockRenderer

supplies a custom confirmation component for the pending action:

Code:
[code]
    
[/code]

The confirmation component adds the user’s decision to the function arguments and invokes the action:

Code:
[code]private async Task RespondAsync(bool accepted)
{
    Block.Call.Arguments ??= new Dictionary();
    Block.Call.Arguments["accepted"] = accepted;
    await Block.InvokeAsync();
}[/code]

The invoked

Code:
propose_document

tool then commits or rejects the predictive state:

Code:
[code]if (accepted)
{
    _agent.State.AcceptPredictiveState();
}
else
{
    _agent.State.RejectPredictiveState();
}[/code]

Accepting commits the proposed document. Rejecting restores the baseline. Unresolved predictive state is also rejected when the run fails, is canceled, or completes without a decision. The user can collaborate with the agent while retaining ownership of the final document.

Generate UI from evolving agent state

Generative UI means the agent can fully or partially generate the UI experience. AG-UI carries the interaction events, while the application decides how to render them. The weather card above illustrates tool-based generative UI: the agent calls a tool, and the Blazor app renders its call and result as a component. The live plan below illustrates agentic generative UI: the agent updates state, and the app renders the changes as they arrive.

The Agentic Generative UI page uses

Code:
UIAgent

for a live plan. The

Code:
create_plan

tool result becomes a

Code:
STATE_SNAPSHOT

, and each

Code:
update_plan_step

result becomes a

Code:
STATE_DELTA

containing an RFC 6902 JSON Patch:

Code:
[code]app.MapAGUIServer(
        "/agentic_generative_ui",
        agents.CreateAgenticGenerativeUI())
    .WithMetadata(new AGUIStreamOptions()
        .MapResultAsStateSnapshot("create_plan")
        .MapResultAsStateDelta("update_plan_step"));[/code]

The

Code:
UIAgentOptions.StateMapper

callback applies snapshots and deltas to the typed state:

Code:
[code]options.StateMapper = context =>
{
    switch (context.Update.RawRepresentation)
    {
        case StateSnapshotEvent snapshot
            when snapshot.Snapshot.Deserialize() is { } state:
            context.SetState(state);
            break;
        case StateDeltaEvent delta:
            context.SetState(
                ApplyDelta(_agent.State.Value, delta.Delta));
            break;
    }
};[/code]

Razor renders a checklist from

Code:
_agent.State.Value.Steps

, and a state change subscription rerenders it as each update arrives. The agent generates the plan and its updates, but the application determines how that state appears and behaves.

Show activities and reasoning summaries

Applications can use

Code:
ContentBlockHandler

to map streamed model content into mutable blocks.

Code:
ActivityHandler

specializes that extensibility point for activity snapshots and updates whose payload and completion rules are defined by the application.

The Reasoning page applies the same mutable-block pattern to

Code:
TextReasoningContent

. A reasoning model returns a reasoning summary through the OpenAI Responses API, MAF emits it as AG-UI

Code:
REASONING_*

events, and

Code:
ReasoningActivityHandler

accumulates the text into one block:

Code:
[code]_agent = new UIAgent(
    _chatClient,
    options => options.AddBlockHandler(
        new ReasoningActivityHandler()));[/code]

Razor renders that block in a collapsible panel separate from the final answer. This is a summary intentionally provided by the model, not hidden chain-of-thought.

Build an Agentic UI that fits your application

.NET provides an end-to-end platform for building applications where people and agents work together. Microsoft.Extensions.AI and MAF provide the AI abstractions and agent framework, the AG-UI .NET SDK connects agents to user-facing applications, and ASP.NET Core with Aspire hosts and orchestrates the system. The new Blazor AI components turn those agent interactions into responsive, application-specific UI.

With

Code:
UIAgent

, observable content blocks, typed shared state, and Razor components, you can combine natural interaction with the visual structure and controls users expect from an application. Start with the patterns your users need, then compose conversation, tools, activities, approvals, editors, plans, and generated views into an Agentic UI that helps them understand, guide, and collaborate with the agent.

The Blazor AI components are currently experimental. Your feedback can help shape them! Try the components in your application and share API feedback, missing scenarios, and usability issues in the

Code:
dotnet/aspnetcore

repository. We look forward to hearing what you build!

Related resources

The post Build Agentic UI with the new Blazor AI components appeared first on .NET Blog.

[DevBlog MS] AG-UI Protocol now has a first-class .NET SDK

Fri, 25 Sep 2026 17:08:47 -0200

The .NET team is pleased to announce that we’ve contributed a .NET SDK for AG-UI in collaboration with CopilotKit. The AG-UI .NET SDK lives in the AG-UI repository alongside the TypeScript and Python SDKs, published on NuGet under the MIT license. Any .NET service can now speak AG-UI directly. The Microsoft Agent Framework (MAF) AG-UI support for .NET is now based on the AG-UI .NET SDK.

What is AG-UI?

Agents break the traditional request-and-response paradigm. They are long-running, stream tokens as they work, delegate to subagents, and invoke tools mid-response. Without a shared protocol, each agent framework or service can expose a different streaming format, leaving application developers to parse chunks, track state, and map framework-specific events to UI. This creates boilerplate that you must maintain, and it can break whenever the event format shifts.

AG-UI (Agent-User Interaction Protocol) standardizes how agents communicate with user-facing applications.

It streams the agent lifecycle as typed events, grouped into several categories. The frontend listens for

Code:
RUN_STARTED

Code:
TEXT_MESSAGE_CONTENT

Code:
STATE_DELTA

, and the other events without caring which framework produced them. State events keep the agent, the app, and the user in sync. How those events are displayed is the client’s choice, so the surface can be a web app, terminal, mobile app, or even a chat platform like Slack or Teams.

The .NET SDK brings that capability to C#. A backend emits those events natively, and a client consumes them from an agent written in any supported language.

Quickstart

The SDK works in both directions.

Code:
AGUI.Server

turns an agent into an AG-UI endpoint, and

Code:
AGUI.Client

lets a .NET application consume one. The server and client support is built on a common set of AG-UI primitives.

Code:
[code]dotnet add package AGUI.Server[/code]

Your agent is an

Code:
IChatClient

, the standard chat abstraction from

Code:
Microsoft.Extensions.AI

. Assuming your app defines a

Code:
CreateChatClient()

method that returns a configured

Code:
IChatClient

, register the client and the AG-UI serializer:

Code:
[code]using AGUI.Abstractions;
using AGUI.Server;
using Microsoft.AspNetCore.Http.Json;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.Options;

var builder = WebApplication.CreateBuilder(args);
builder.Services.AddSingleton(CreateChatClient());
builder.Services.Configure(options =>
    options.SerializerOptions.TypeInfoResolverChain.Insert(
        0,
        AGUIJsonUtilities.DefaultTypeInfoResolver));

var app = builder.Build();

app.MapPost("/", (
    RunAgentInput input,
    IChatClient chatClient,
    IOptions jsonOptions,
    CancellationToken cancellationToken) =>
{
    var context = input.ToChatRequestContext(
        jsonOptions.Value.SerializerOptions);

    var events = chatClient
        .GetStreamingResponseAsync(
            context.Messages,
            context.ChatOptions,
            cancellationToken)
        .AsAGUIEventStreamAsync(context, cancellationToken);

    return TypedResults.ServerSentEvents(events);
});

await app.RunAsync();[/code]

Code:
ToChatRequestContext

unpacks the incoming

Code:
RunAgentInput

, the protocol’s request payload, into the messages and options your

Code:
IChatClient

expects.

Code:
AsAGUIEventStreamAsync

turns the response stream back into protocol events. It emits

Code:
RUN_STARTED

and

Code:
RUN_FINISHED

around the run, closes open text and reasoning blocks before switching to a different message or tool call, and collapses multiple interrupts into a single terminal

Code:
RUN_FINISHED

.

The SDK handles the protocol. The web server is yours, which is why Agent Framework’s ASP.NET Core package wraps these same primitives as

Code:
AddAGUIServer()

and

Code:
MapAGUIServer()

.

In Agent Framework, the same endpoint takes only a few lines. Given an existing

Code:
IChatClient

named

Code:
chatClient

, the following code creates and hosts the agent. Read the full walkthrough on Microsoft Learn for a complete server and client.

Code:
[code]dotnet add package Microsoft.Agents.AI.Hosting.AGUI.AspNetCore --prerelease[/code]

Code:
[code]using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
using Microsoft.Extensions.AI;

var builder = WebApplication.CreateBuilder(args);
builder.Services.AddAGUIServer();

var app = builder.Build();

AIAgent agent = chatClient.AsAIAgent(
    name: "AGUIAssistant",
    instructions: "You are a helpful assistant.");

app.MapAGUIServer("/", agent);
await app.RunAsync();[/code]

Code:
AGUI.Client

handles the other direction, connecting a .NET application to any AG-UI agent.

Code:
[code]dotnet add package AGUI.Client[/code]

Code:
[code]using AGUI.Client;

using var httpClient = new HttpClient();

var chatClient = new AGUIChatClient(
    new(httpClient, "http://localhost:5001"));[/code]

Code:
AGUIChatClient

is an

Code:
IChatClient

, so it works anywhere your code already takes an

Code:
IChatClient

. The agent on the other end can be written in Python, TypeScript, or C#, and your code does not change.

Once your endpoint speaks AG-UI, any AG-UI client can drive it: a .NET application through

Code:
AGUI.Client

, or a React frontend through CopilotKit. The CopilotKit Interactive Dojo has running examples against a .NET backend.

The repository has worked examples for each part of the protocol. There is one client and server pair per step, covering chat, backend and frontend tools, human in the loop, shared state, reasoning, multimodal input, interrupts, parallel tool calls, protobuf, and telemetry.

Architecture

Here is how everything works together. A request arrives from any AG-UI client, the endpoint hands it to your agent in Microsoft Agent Framework or your own agent code, and the response streams back as protocol events while the agent calls your tools and the model.

The packages

The SDK ships as five packages on NuGet: protocol types, wire formatters, protobuf support, an HTTP client, and a framework-agnostic server adapter built on

Code:
Microsoft.Extensions.AI

.

Package
What it is

Code:
AGUI.Abstractions

Protocol model: events, messages, tools, capabilities, interrupts, state, and the source-generated serializer

Code:
AGUI.Formatting

Wire format abstraction and the default Server-Sent Events implementation

Code:
AGUI.Protobuf

Opt-in protobuf codec generated from the TypeScript

Code:
.proto

definitions. It covers a subset of event types. SSE is the default and carries all of them

Code:
AGUI.Client

Consumes AG-UI

Code:
AGUI.Server

Produces AG-UI

The client and server packages pull in the abstractions they need, so most applications reference one of them.

What this means for Microsoft Agent Framework

Agent Framework, our SDK for building AI agents in .NET and Python, used to include its own implementation of the AG-UI protocol. It now depends on the

Code:
AGUI.*

packages from NuGet and keeps the ASP.NET Core integration that turns an agent into an endpoint.

The protocol is maintained in the AG-UI C# SDK and stays wire-compatible with the TypeScript and Python SDKs. See the migration notice for full details.

For existing Agent Framework users, the programming model is unchanged, though a few APIs were renamed:

Before
Now

Code:
AddAGUI()

Code:
MapAGUI()

Code:
AddAGUIServer()

Code:
MapAGUIServer()

Code:
Microsoft.Agents.AI.AGUI

namespace

Code:
AGUI.Client

Code:
AGUI.Server

Code:
AGUI.Abstractions

Code:
AGUIChatClient

positional constructor
Options-based constructor

Reading the originating request

Code:
chatOptions.TryGetRunAgentInput(out RunAgentInput? agentInput)

The event format is unchanged, so existing frontends keep working against an upgraded backend without changes of their own.

Why this matters

Any .NET backend can speak the protocol. A worker service, internal API, or existing line-of-business application can expose an agent over AG-UI on its own. No agent framework is required.

One backend can serve many surfaces. The client decides how to render the events, so the same endpoint serves a web app, terminal, mobile client, or chat platform like Slack and Teams.

Your existing code remains the same.

Code:
IChatClient

is the only integration point. You can consume AG-UI from .NET Framework 4.7.2 and later, and expose an endpoint from current .NET.

Interoperate across languages. The shared wire protocol keeps the C#, TypeScript, and Python SDKs compatible, so a .NET backend can work with clients built using any of them.

Get started

The AG-UI .NET SDK provides one C# implementation of AG-UI, published on NuGet and usable from any .NET service. Agent Framework consumes it, and so can you.

Questions and feedback about the SDK are welcome in the AG-UI repository. For MAF integration questions, use the Microsoft Agent Framework discussion boards.

The post AG-UI Protocol now has a first-class .NET SDK appeared first on .NET Blog.

[DevBlog MS] Microsoft is updating its author-signing certificate starting September

Wed, 23 Sep 2026 19:41:19 -0200

Action required: If you validate that packages are author-signed by Microsoft using a NuGet client policy or the

Code:
dotnet nuget verify

command, follow the steps in this post as soon as possible to avoid potential disruptions during the transition. If you are unsure whether you are impacted, follow the steps below to check.

Microsoft uses an X.509 certificate to author-sign its NuGet packages. As soon as September 23, 2026, a new certificate will become the default Microsoft author-signing certificate for NuGet packages. Existing packages signed with an older certificate will retain their signatures, but the current certificate will no longer be used to sign new packages after the transition.

Current certificate SHA-256 fingerprint:

Code:
566A31882BE208BE4422F7CFD66ED09F5D4524A5994F50CCC8B05EC0528C1353

New certificate SHA-256 fingerprint:

Code:
9A1B131BEE0605433056A4EA3815478A8E177961A968C6C0027C1093D1FEB630

Who will be impacted?

Customers who use a NuGet client policy to enforce an allow list of trusted signers that includes Microsoft.

To determine whether you have a NuGet client policy configured, check for the following elements in your

Code:
nuget.config

. Keep in mind that

Code:
nuget.config

files can exist in multiple locations with different scopes.

Code:
[code]
    
[/code]

Customers who use
Code:
dotnet nuget verify
to verify that signed packages are author-signed by Microsoft.

This may look like the following:

Code:
[code]dotnet nuget verify  --certificate-fingerprint 3F9001EA83C560D712C24CF213C3D312CB3BFF51EE89435D3430BD06B5D0EECE --certificate-fingerprint AA12DA22A49BCE7D5C1AE64CC1F3D892F150DA76140F210ABD2CBFFCA2C18A27 --certificate-fingerprint 566A31882BE208BE4422F7CFD66ED09F5D4524A5994F50CCC8B05EC0528C1353[/code]

If neither scenario applies to you, you should be unaffected by this certificate update. Microsoft NuGet packages signed with the new certificate should install in the same way as packages signed with older certificates.

Allow the new Microsoft certificate

Client policy

If you use a NuGet client policy to enforce an allow list of trusted signers, add the new Microsoft certificate to the allow list as soon as possible. Keep the older Microsoft certificates in the policy so that you can continue to install packages signed with those certificates. If you try to install a package signed with the new certificate without updating your trusted signers, the package installation will fail with an NU3034 error.

You can add the new Microsoft author-signing certificate by running the following command:

Code:
[code]dotnet nuget trust author Microsoft 9A1B131BEE0605433056A4EA3815478A8E177961A968C6C0027C1093D1FEB630 --algorithm SHA256[/code]

The

Code:
dotnet nuget trust

command is available in the .NET 6 SDK and later. It updates the applicable

Code:
nuget.config

file. Use

Code:
--configfile 

to update a specific configuration file.

Alternatively, add the new certificate to the existing Microsoft entry in

Code:
nuget.config

. The resulting entry should include both the older certificates and the new certificate:

Code:
[code]
    
[/code]

Package verification

If you use

Code:
dotnet nuget verify

to confirm that a signed package is author-signed by Microsoft, add the new fingerprint while retaining the older fingerprints:

Code:
[code]dotnet nuget verify  --certificate-fingerprint 3F9001EA83C560D712C24CF213C3D312CB3BFF51EE89435D3430BD06B5D0EECE --certificate-fingerprint AA12DA22A49BCE7D5C1AE64CC1F3D892F150DA76140F210ABD2CBFFCA2C18A27 --certificate-fingerprint 566A31882BE208BE4422F7CFD66ED09F5D4524A5994F50CCC8B05EC0528C1353 --certificate-fingerprint 9A1B131BEE0605433056A4EA3815478A8E177961A968C6C0027C1093D1FEB630[/code]

Each

Code:
--certificate-fingerprint

option adds an accepted SHA-256 signer certificate fingerprint. Keeping all four values allows the command to verify newly signed packages and existing packages signed with an older Microsoft certificate.

Feedback

If you have questions about how you may be impacted or run into issues while following these steps, please contact us.

For more general NuGet feedback and suggestions:

Review our guidance for submitting bugs and suggestions.
Start a discussion or open an issue in the NuGet/Home repository.

The post Microsoft is updating its author-signing certificate starting September 23, 2026 appeared first on .NET Blog.

[DevBlog MS] Creating a memory dump in C#

Tue, 22 Sep 2026 18:44:10 -0200

When applications crash in production, how much do we actually know about what happened? And more importantly, how easy is it to debug what happened so that we can fix the bug? Let’s learn how easy it is to capture a memory dump so that we can debug it.

Why Create a Memory Dump

You know that moment when you’re in a desktop application and suddenly it hangs, the screen greys out, and it’s clear the application has stopped responding. Or when you go to a website and you’re sure you clicked on that link, but the browser is just spinning.

While the developer might have added logging and telemetry to that application and be able to follow the execution pathways from that, actually understanding the state of the application can be a lot harder.

This is where a memory dump can be useful. A memory dump can capture the application state, and depending on whether it’s a full or partial dump, you can get a view of objects in memory that are waiting for the garbage collector to clean up, including out-of-scope state that can still provide insights into the broader application behavior.

For this scenario, we’re going to look at an application that is becoming unresponsive, and a common culprit for this kind of issue is how we are using asynchronous code and tasks.

Monitoring the Thread Pool

The pattern that we’re going to use to monitor the thread pool is that we’ll periodically add our own

Code:
Task

to it, observe how long that task takes to complete, and if it took longer than an allowed threshold, we’ll know that the thread pool is likely saturated and probably something we want to capture a dump of.

We’ll create a

Code:
ThreadPoolWatcher

class that will encapsulate this logic:

Code:
[code]internal class ThreadPoolWatcher(string name = "ThreadPool Watcher", int interval = 3_000)
{
    private static readonly object DumpLock = new();
    private static int dumpCount;

    private readonly Thread thread = new(() => Watcher(interval))
    {
        Name = name,
        IsBackground = true
    };

    private static void Watcher(int interval)
    {
        while (true)
        {
            Thread.Sleep(interval);

            Stopwatch stopwatch = Stopwatch.StartNew();
            Task task = Task.Run(stopwatch.Stop);

            if (!task.Wait(interval))
            {
                Console.WriteLine($"Task did not complete within {interval} ms");
            }

            if (stopwatch.ElapsedMilliseconds <= interval) continue;

            lock (DumpLock)
            {
                if (dumpCount++ > 0)
                {
                    Console.WriteLine("Dump already created for this run; skipping additional dumps.");
                    continue;
                }
            }

            // Took over the interval to complete
            Console.WriteLine($"Task took too long: {stopwatch.ElapsedMilliseconds} ms");

            string path = Path.Combine(AppContext.BaseDirectory, $"fulldump-{Environment.ProcessId}-{DateTime.Now:yyyyMMdd-HHmmss}.dmp");

            if (OperatingSystem.IsWindows())
            {
                WindowsDumper.WriteCurrentProcess(path);
            }
            else if (OperatingSystem.IsLinux())
            {
                LinuxDumper.WriteCurrentProcess(path);
            }
        }
    }

    internal void Join() => thread.Join();
    internal void Start() => thread.Start();
}[/code]

There are a few things going on in this code, so let’s dissect it a bit.

First, we’re creating a new

Code:
Thread

(which we’re providing a name so we can identify it while debugging) that, when run, will continually invoke the

Code:
Watcher

method. The watcher uses

Code:
Thread.Sleep

to pause for the specified interval between each check.

When the thread wakes up, it adds a new task to the thread pool and measures how long it takes to complete. If the task takes longer than the allowed threshold, it indicates that the thread pool is likely saturated and we may want to capture a memory dump to investigate further. Otherwise, it goes back to sleep. This is a simple way to observe thread-pool behavior in real time by exploiting task timing.

For production use, you should also guard against repeated dump generation. Full dumps can be large and may include credentials, tokens, connection strings, or other sensitive data. Storing them in a restricted directory, adding a cooldown, or limiting the number of files generated is a safer pattern than dumping on every delayed probe.

Then, if the task took longer than the specified interval, we’ll dump the memory of the current process, using either Windows or Linux APIs.

Creating a Windows Memory Dump

On Windows, to create a memory dump of the current process, we’re going to need to call into a native library,

Code:
dbghelp.dll

, and have Windows generate the dump for us.

Code:
[code][SupportedOSPlatform("windows")]
internal static class WindowsDumper
{
    [Flags]
    private enum DumpType : uint
    {
        Normal = 0x00000000,
        WithDataSegs = 0x00000001,
        WithFullMemory = 0x00000002,
        WithHandleData = 0x00000004,
        WithUnloadedModules = 0x00000020,
        WithFullMemoryInfo = 0x00000800,
        WithThreadInfo = 0x00001000,
        WithTokenInformation = 0x00040000,
    }

    [DllImport("dbghelp.dll", SetLastError = true, CharSet = CharSet.Unicode)]
    [return: MarshalAs(UnmanagedType.Bool)]
    private static extern bool MiniDumpWriteDump(
        IntPtr hProcess,
        uint processId,
        SafeHandle hFile,
        DumpType dumpType,
        IntPtr exceptionParam,
        IntPtr userStreamParam,
        IntPtr callbackParam);

    /// 
    /// Writes a full memory dump of the current process.
    /// 
    public static void WriteCurrentProcess(string path)
    {
        Write(Process.GetCurrentProcess(), path);
    }

    /// 
    /// Writes a full memory dump of  to .
    /// 
    public static void Write(Process process, string path)
    {
        ArgumentNullException.ThrowIfNull(process);
        ArgumentException.ThrowIfNullOrEmpty(path);

        string? directory = Path.GetDirectoryName(Path.GetFullPath(path));
        if (!string.IsNullOrEmpty(directory))
        {
            Directory.CreateDirectory(directory);
        }

        using FileStream stream = new(path, FileMode.Create, FileAccess.ReadWrite, FileShare.None);

        // Full memory dump: entire address space (including the heap), handles, modules and thread state.
        bool success = MiniDumpWriteDump(
            process.Handle,
            (uint)process.Id,
            stream.SafeFileHandle,
            DumpType.WithFullMemory |
                DumpType.WithFullMemoryInfo |
                DumpType.WithDataSegs |
                DumpType.WithHandleData |
                DumpType.WithUnloadedModules |
                DumpType.WithThreadInfo |
                DumpType.WithTokenInformation,
            IntPtr.Zero,
            IntPtr.Zero,
            IntPtr.Zero);

        if (!success)
        {
            throw new Win32Exception(Marshal.GetLastWin32Error(), $"MiniDumpWriteDump failed for process {process.Id}.");
        }
    }
}[/code]

This is a dump class, and because it only works on Windows, we’re annotating it with the

Code:
SupportedOSPlatform("windows")

attribute. Next, there’s an enum that defines the different types of memory dumps that can be created, such as full memory dumps, dumps with handle data, and dumps with thread information. The

Code:
MiniDumpWriteDump

function from

Code:
dbghelp.dll

is then imported to actually perform the dump, and the class provides convenient methods to write a dump of the current process or any specified process.

For this example, we’re adding everything to the memory dump that is generated, which means it will be quite large. In our sample, this produces a dump of approximately 125 MB, although the size depends on the process’s memory usage and selected dump contents.

Creating a Linux Memory Dump

To create an equivalent memory dump on Linux can be a little more difficult as it will depend on the distribution that is used, whether it’s running in a container, and the permissions the process has. Here’s an example of creating a full memory dump using the

Code:
createdump

utility that ships with the .NET runtime.

Code:
[code][SupportedOSPlatform("linux")]
internal static class LinuxDumper
{
    // Yama LSM (see /proc/sys/kernel/yama/ptrace_scope). With the default scope of 1
    // ("restricted ptrace"), a process may only be ptraced by its own descendants unless
    // it explicitly designates another process (or PR_SET_PTRACER_ANY) as an allowed
    // tracer via prctl(PR_SET_PTRACER, ...). "Yama" spelled out in ASCII.
    private const int PR_SET_PTRACER = 0x59616d61;
    private static readonly IntPtr PR_SET_PTRACER_ANY = new(-1);

    [DllImport("libc", SetLastError = true)]
    private static extern int prctl(int option, IntPtr arg2, IntPtr arg3, IntPtr arg4, IntPtr arg5);

    public static void WriteCurrentProcess(string path)
    {
        AllowAnyProcessToPtraceSelf();

        Write(Process.GetCurrentProcess(), path);
    }

    /// 
    /// Best-effort: on distros using the Yama LSM (e.g. Ubuntu/Debian) with the default
    /// ptrace_scope of 1 ("restricted ptrace"), a process may only be ptraced by its own
    /// descendants - not the parent that spawned it. createdump attaches to us as our
    /// child, so we explicitly allow any process to ptrace us. This is a no-op (and
    /// harmless) on distros where Yama isn't enabled (e.g. many Fedora/RHEL setups), and
    /// is swallowed entirely if "libc" or prctl can't be resolved at all, which can happen
    /// on musl-based distros like Alpine that don't ship an unversioned libc.so.
    /// 
    private static void AllowAnyProcessToPtraceSelf()
    {
        try
        {
            _ = prctl(PR_SET_PTRACER, PR_SET_PTRACER_ANY, IntPtr.Zero, IntPtr.Zero, IntPtr.Zero);
        }
        catch (Exception ex) when (ex is DllNotFoundException or EntryPointNotFoundException)
        {
            // libc/prctl isn't resolvable this way on this platform (e.g. musl/Alpine) -
            // fall through and let createdump itself report any real permission failure.
        }
    }

    public static void Write(Process process, string path)
    {
        ArgumentNullException.ThrowIfNull(process);
        ArgumentException.ThrowIfNullOrEmpty(path);

        string? directory = Path.GetDirectoryName(Path.GetFullPath(path));
        if (!string.IsNullOrEmpty(directory))
        {
            Directory.CreateDirectory(directory);
        }

        string createDumpPath = FindCreateDump();

        using Process createDump = new()
        {
            StartInfo = new ProcessStartInfo
            {
                FileName = createDumpPath,
                // --full: entire address space (analogous to MiniDumpWithFullMemory).
                // -f: explicit output path (createdump would otherwise pick its own name/location).
                ArgumentList =
                {
                    "--full",
                    "-f", path,
                    process.Id.ToString(),
                },
                UseShellExecute = false,
                RedirectStandardOutput = true,
                RedirectStandardError = true,
            },
        };

        createDump.Start();
        string stdout = createDump.StandardOutput.ReadToEnd();
        string stderr = createDump.StandardError.ReadToEnd();
        createDump.WaitForExit();

        if (createDump.ExitCode != 0)
        {
            string hint = process.Id != Environment.ProcessId
                ? " Dumping another process typically requires running as root, the " +
                  "CAP_SYS_PTRACE capability, or /proc/sys/kernel/yama/ptrace_scope set to 0."
                : " If this is a container, ensure ptrace isn't blocked by seccomp " +
                  "(add --cap-add=SYS_PTRACE) or by an SELinux/AppArmor policy.";

            throw new InvalidOperationException(
                $"createdump failed for process {process.Id} with exit code {createDump.ExitCode}.{hint}{Environment.NewLine}{stdout}{stderr}");
        }
    }

    private static string FindCreateDump()
    {
        string runtimeDirectory = RuntimeEnvironment.GetRuntimeDirectory();
        string candidate = Path.Combine(runtimeDirectory, "createdump");

        if (!File.Exists(candidate))
        {
            throw new FileNotFoundException(
                $"Could not find the 'createdump' utility next to the runtime directory '{runtimeDirectory}'.",
                candidate);
        }

        return candidate;
    }
}[/code]

This class does a couple of extra things. It uses

Code:
prctl

from

Code:
libc

to allow the child

Code:
createdump

process to attach to its parent under Yama’s restricted ptrace policy, and it locates the

Code:
createdump

utility next to the runtime directory so it can create a full memory dump. In our sample, this produced a dump of approximately 800 MB, although the size depends on the process’s memory usage and the dump configuration.

Simulating a Problem

Now that we can capture memory dumps of our processes, let’s simulate a problem by intentionally causing an issue in our application that we can then analyze using the memory dump.

Code:
[code]internal static class ApplicationRunner
{
    public static void DoLotsOfWork() => Parallel.For(0, 1000, DoSomeWork);

    private static void DoSomeWork(int i)
    {
        Console.WriteLine("Running task {0}", i);
        Thread.Sleep(10_000);
    }
}[/code]

This code is going to simulate running a lot of parallel tasks, each of them “doing something” that will take a long time to complete, but there’s no restriction on the number of tasks that can be run on the thread-pool, potentially saturating it and causing performance issues or the appearance of the application hanging.

Then we can run our application by creating the

Code:
ThreadPoolWatcher

instance, starting it, and running the workload while the dedicated watcher thread waits for the next probe.

Code:
[code]var tpw = new ThreadPoolWatcher();
tpw.Start();

ApplicationRunner.DoLotsOfWork();

// The application keeps running until the process exits or the watcher is stopped.[/code]

After a while, our application will start to become unresponsive and generate the dump file.

Analyzing the Memory Dump

The

Code:
.dmp

files that are generated can be opened in Visual Studio with managed debugging, allowing us to walk the call stacks, inspect available variables, and view the state of the application at the time the dump was created.

If you want to learn more about analyzing memory dumps and using the parallel stacks view in Visual Studio, you can read the companion article on the Visual Studio blog.

Conclusion

In this article, we’ve seen how easy it can be to have our application create memory dumps when it encounters performance issues or an unresponsive thread pool, allowing us to analyze the state of the application at the time of the problem instead of relying only on logging and reproducing scenarios. Combining this with the Visual Studio tools for analyzing memory dumps, we can gain deep insights into the behavior of our application and more effectively diagnose and resolve complex issues.

By incorporating memory dump generation into our development and monitoring practices, we can proactively address potential performance bottlenecks and hangs, ultimately leading to more robust and reliable applications.

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