Many customers are modernizing and streamlining the purchasing through Azure Marketplace. Some ISVs license via an upfront payment for 1,2 or 3 years of software and services/SaaS model. If you have transacted many deals through Azure Marketplace, what best practices have you found to be most useful to understand what “renewal” private offers need to be created and which ones are coming due since there is not an auto notification feature for ISVs publishing the private offer?

@justinroyal 

​Many customers are modernizing and streamlining the purchasing through Azure Marketplace. Some ISVs license via an upfront payment for 1,2 or 3 years of software and services/SaaS model. If you have transacted many deals through Azure Marketplace, what best practices have you found to be most useful to understand what “renewal” private offers need to be created and which ones are coming due since there is not an auto notification feature for ISVs publishing the private offer?@justinroyal   Read More

​

Wondering if anyone else has this issue. I added in a quick links web part but when I do into my document library and change the file title it doesn’t automatically update on the web-part.

Is this a caching issue? Or do I always have to update the title once I change it? Below is an example of the second web-part I pulled in with the updated Titles for my files.

​Wondering if anyone else has this issue. I added in a quick links web part but when I do into my document library and change the file title it doesn’t automatically update on the web-part. Is this a caching issue? Or do I always have to update the title once I change it? Below is an example of the second web-part I pulled in with the updated Titles for my files.   Read More

​

Introduction
Caching plays a crucial role in optimizing web performance. When Output Caching is enabled in IIS for specific file extensions or URLs, it deliver 304 Not Modified status code. This status code is essential for managing caching, as it indicates whether a resource has been modified since the last time it was requested.

​Microsoft Tech Community – Latest Blogs –Read More 

Insider canary level win 11 file explorer downloads folder only shows current month files.

All older files are not displayed. Please help

​Insider canary level win 11 file explorer downloads folder only shows current month files.All older files are not displayed. Please help  Read More

​

Server 2019

client Windows 11

A client workstation has 4 DNS servers we expect to work in a “round robin”. The servers are:

accounting

research

dev

admin

The systems we are trying to access exist in the DNS server DEV. When we run NSLOOKUP and set the DNS server to DEV, we are able to look up the systems we are trying to access. When we exit NSLOOKUP and try to ping or access a system through its web portal, the system doesn’t recognize the name.

Any ideas?

Thanks!

​Server 2019client Windows 11 A client workstation has 4 DNS servers we expect to work in a “round robin”. The servers are:accountingresearchdevadmin The systems we are trying to access exist in the DNS server DEV. When we run NSLOOKUP and set the DNS server to DEV, we are able to look up the systems we are trying to access. When we exit NSLOOKUP and try to ping or access a system through its web portal, the system doesn’t recognize the name. Any ideas? Thanks!     Read More

​

Not sure if this is possible as I was presented with an odd request:

I am attempting to write a formula that adds up the percent complete of tasks in the file by UID to calculate the cumulative percent complete for a task representing other tasks throughout the IMS, which are not subtasks of the task with the formula.

Not looking to do the grouping feature, yet know that feature could do that and I could then input the value in the ‘task representing other tasks throughout the IMS, which are not subtasks of the task with the formula.’.

​Not sure if this is possible as I was presented with an odd request: I am attempting to write a formula that adds up the percent complete of tasks in the file by UID to calculate the cumulative percent complete for a task representing other tasks throughout the IMS, which are not subtasks of the task with the formula. Not looking to do the grouping feature, yet know that feature could do that and I could then input the value in the ‘task representing other tasks throughout the IMS, which are not subtasks of the task with the formula.’. Cheers,Cole  Read More

​

Introduction:

I recently wrote a blog that has detail of how to capture the network traces from Client and Server via Wireshark. You can refer to that here.

How to Capture Simultaneous Wireshark Traces from Client and Server for Network Troubleshooting (microsoft.com).

Now, One of the challenges server support teams face is that Wireshark needs to be installed on both the client and server machines. Since many servers host critical applications, installing new tools during business hours is often avoided, or server admins need special approval to install them. So instead of installing new software, why not use a tool that already comes with Windows? This time, we’ll use the built-in Netsh utility.

About Netsh:

Netsh is a simple command-line tool that helps you view and change your computer’s network settings. You can use Netsh by typing commands in the Netsh command prompt, and you can also include these commands in scripts or batch files to automate tasks. Netsh works for both your local computer and remote computers.  One useful feature of Netsh is that it lets you create a script with several commands, which you can then run all at once on a specific computer. You can also save these scripts in a text file to use later or to apply the same settings to other computers.

Let’s go capture the problem with Netsh.

We will follow basically the three steps to collect the network traces with Netsh.

Start command to start the capture.
Reproduce the issue.
Stop the command and let it collect the events, and zip.

Starting the trace collection:

Open an Administrative Command Prompt or an Administrative PowerShell console: open the Start menu and type CMD or PowerShell in the search bar, then right-click the command prompt or PowerShell and select Run as Administrator.
Run the following command to start the network capture

netsh trace start scenario=netconnection,WFP-IPsec maxSize = 1024 fileMode =circular Persistent=yes capture=yes report=yes tracefile=c:Clientside.etl

If you don’t specify the tracefile parameter, the default location is %LOCALAPPDATA%TempNetTraces

Like this:

Once you have run the command, immediately go and reproduce the issue you are trying to investigate multiple times.
Now you have the issue reproduced and netsh must have data captured, now its time to command it to stop and merge those events for us.
Run the stop command:

Netsh trace stop

This has been done on Client machine, but if you are troubleshooting something to trace the communication between Client And IIS Server(or any host), then same commands should be run on Server with just the filename as ServerSide.etl for visible secretion of Client and Server.

If you see one additional file created with an extension of .cab, don’t worry about this one. As this just holds some related diagnostic information and compresses that information into a CAB file. 

This would generate the ClientSide.etl file which can be opened with Network analyzer tools like NetMon to validate and analyze for the comprehensive review to troubleshoot.

But what if you want to go ahead and try it on your own with Wireshark. And Wireshark does not read .etl files. You can go try the Open source tool etl2pcapng from the Official Microsoft Github repository and convert this etl to pcap and let Wireshark to read it.

Looking for more information about the Netsh? Please refer to the official Microsoft documentation here.

Netsh Command Syntax, Contexts, and Formatting | Microsoft Learn

​Microsoft Tech Community – Latest Blogs –Read More 

Introduction to AI Automation with Azure OpenAI Assistants

Intro

Welcome to the future of automation! In the world of Azure, AI assistants are becoming your trusty sidekicks, ready to tackle the repetitive tasks that once consumed your valuable time. But what if we could make these assistants even smarter? In this post, we’ll dive into the exciting realm of integrating Azure AI assistants with Logic Apps – Microsoft’s powerful workflow automation tool. Get ready to discover how this dynamic duo can transform your workflows, freeing you up to focus on the big picture and truly innovative work.

Azure OpenAI Assistants (preview)

Azure OpenAI Assistants (Preview) allows you to create AI assistants tailored to your needs through custom instructions and augmented by advanced tools like code interpreter, and custom functions. To accelerate and simplify the creation of intelligent applications, we can now enable the ability to call Logic Apps workflows through function calling in Azure OpenAI Assistants. The Assistants playground enumerates and lists all the workflows in your subscription that are eligible for function calling. Here are the requirements for these workflows:

Schema: The workflows you want to use for function calling should have a JSON schema describing the inputs and expected outputs. Using Logic Apps you can streamline and provide schema in the trigger, which would be automatically imported as a function definition.

Consumption Logic Apps: Currently supported consumption workflows.

Request trigger: Function calling requires a REST-based API. Logic Apps with a request trigger provides a REST endpoint. Therefore only workflows with a request trigger are supported for function calling.

AI Automation

So apart from the Assistants API, which we will explore in another post, we know that we can Integrate Azure Logic Apps workflows! Isn’t that amazing ? The road now is open for AI Automation and we are on the genesis of it, so let’s explore it. We need an Azure Subscription and:

Azure OpenAI in the supported regions. This demo is on Sweden Central.Logic Apps consumption Plan.

We will work in Azure OpenAI Studio and utilize the Playground. Our model deployment is GPT-4o.

The Assistants Playground offers the ability to create and save our Assistants, so we can start working and return later, open the Assistant and continue. We can find the System Message option and the three tools that enhance the Assistants with Code Interpreter, Function Calling ( Including Logic Apps) and Files upload. The following table describes the configuration elements of our Assistants:

Name Description

Assistant nameYour deployment name that is associated with a specific model.InstructionsInstructions are similar to system messages this is where you give the model guidance about how it should behave and any context it should reference when generating a response. You can describe the assistant’s personality, tell it what it should and shouldn’t answer, and tell it how to format responses. You can also provide examples of the steps it should take when answering responses.DeploymentThis is where you set which model deployment to use with your assistant.FunctionsCreate custom function definitions for the models to formulate API calls and structure data outputs based on your specificationsCode interpreterCode interpreter provides access to a sandboxed Python environment that can be used to allow the model to test and execute code.FilesYou can upload up to 20 files, with a max file size of 512 MB to use with tools. You can upload up to 10,000 files using AI Studio.

The Studio provides 2 sample Functions (Get Weather and Get Stock Price) to get an idea of the schema requirement in JSON for Function Calling. It is important to provide a clear message that makes the Assistant efficient and productive, with careful consideration since the longer the message the more Tokens are consumed.

Challenge #1 – Summarize WordPress Blog Posts

How about providing a prompt to the Assistant with a URL instructing it to summarize a WordPress blog post? It is WordPress cause we have a unified API and we only need to change the URL. We can be more strict and narrow down the scope to a specific URL but let’s see the flexibility of Logic Apps in a workflow.

We should start with the Logic App. We will generate the JSON schema directly from the Trigger which must be an HTTP request.

{
“name”: “__ALA__lgkapp002”, // Remove this for the Logic App Trigger
“description”: “Fetch the latest post from a WordPress website,summarize it, and return the summary.”,
“parameters”: {
“type”: “object”,
“properties”: {
“url”: {
“type”: “string”,
“description”: “The base URL of the WordPress site”
},
“post”: {
“type”: “string”,
“description”: “The page number”
}
},
“required”: [
“url”,
“post”
]
}
}

In the Designer this looks like this :

As you can see the Schema is the same, excluding the name which is need only in the OpenAI Assistants. We will see this detail later on. Let’s continue with the call to WordPress. An HTTP Rest API call:

And finally mandatory as it is, a Response action where we tell the Assistant that the Call was completed and bring some payload, in our case the body of the previous step:

Now it is time to open our Azure OpenAI Studio and create a new Assistant. Remember the prerequisites we discussed earlier!

From the Assistants menu create a [+New] Assistant, give it a meaningful name, select the deployment and add a System Message . For our case it could be something like : ” You are a helpful Assistant that summarizes the WordPress Blog Posts the users request, using Functions. You can utilize code interpreter in a sandbox Environment for advanced analysis and tasks if needed “. The Code interpreter here could be an overkill but we mention it to see the use of it ! Remember to save the Assistant. Now, in the Functions, do not select Logic Apps, rather stay on the custom box and add the code we presented earlier. The Assistant will understand that the Logic App named xxxx must be called, aka [“name”: “__ALA__lgkapp002“,] in the schema! In fact the Logic App is declared by 2 underscores as prefix and 2 underscores as suffix, with ALA inside and the name of the Logic App.

Let’s give our Assistant a Prompt and see what happens:

The Assistant responded pretty solidly with a meaningful summary of the post we asked for! Not bad at all for a Preview service.

Challenge #2 – Create Azure Virtual Machine based on preferences

For the purpose of this task we have activated System Assigned managed identity to the Logic App we use, and a pre-provisioned Virtual Network with a subnet as well. The Logic App must reside in the same subscription as our Azure OpenAI resource.

This is a more advanced request, but after all it translates to Logic Apps capabilities. Can we do it fast enough so the Assistant won’t time out? Yes we do, by using the Azure Resource Manager latest API which indeed is lightning fast! The process must follow the same pattern, Request – Actions – Response. The request in our case must include such input so the Logic App can carry out the tasks. The Schema should include a “name” input which tells the Assistant which Logic App to look up:

{
“name”: “__ALA__assistkp02” //remove this for the Logic App Trigger
“description”: “Create an Azure VM based on the user input”,
“parameters”: {
“type”: “object”,
“properties”: {
“name”: {
“type”: “string”,
“description”: “The name of the VM”
},
“location”: {
“type”: “string”,
“description”: “The region of the VM”
},
“size”: {
“type”: “string”,
“description”: “The size of the VM”
},
“os”: {
“type”: “string”,
“description”: “The OS of the VM”
}
},
“required”: [
“name”,
“location”,
“size”,
“os”
]
}
}

And the actual screenshot from the Trigger, observe the absence of the “name” here:

Now as we have number of options, this method allows us to keep track of everything including the user’s inputs like VM Name , VM Size, VM OS etc.. Of Course someone can expand this, since we use a default resource group and a default VNET and Subnet, but it’s also configurable! So let’s store the input into variables, we Initialize 5 variables. The name, the size, the location (which is preset for reduced complexity since we don’t create a new VNET), and we break down the OS. Let’s say the user selects Windows 10. The API expects an offer and a sku. So we take Windows 10 and create an offer variable, the same with OS we create an OS variable which is the expected sku:

if(equals(triggerBody()?[‘os’], ‘Windows 10’), ‘Windows-10’, if(equals(triggerBody()?[‘os’], ‘Windows 11’), ‘Windows-11’, ‘default-offer’))

if(equals(triggerBody()?[‘os’], ‘Windows 10’), ‘win10-22h2-pro-g2’, if(equals(triggerBody()?[‘os’], ‘Windows 11’), ‘win11-22h2-pro’, ‘default-sku’))

As you understand this is narrowed to Windows Desktop only available choices, but we can expand the Logic App to catch most well know Operating Systems.

After the Variables all we have to do is create a Public IP (optional) , a Network Interface, and finally the VM. This is the most efficient way i could make, so we won’t get complains from the API and it will complete it very fast ! Like 3 seconds fast ! The API calls are quite straightforward and everything is available in Microsoft Documentation. Let’s see an example for the Public IP:

And the Create VM action with highlight to the storage profile – OS Image setup:

Finally we need the response which can be as we like it to be. I am facilitating the Assistant’s response with an additional Action “Get Virtual Machine” that allows us to include the properties which we add in the response body:

Let’s make our request now, through the Assistants playground in Azure OpenAI Studio. Our prompt is quite clear: “Create a new VM with size=Standard_D4s_v3, location=swedencentral, os=Windows 11, name=mynewvm02”. Even if we don’t add the parameters the Assistant will ask for them as we have set in the System Message.

Pay attention to the limitation also . When we ask about the Public IP, the Assistant does not know it. Yet it informs us with a specific message, that makes sense and it is relevant to the whole operation. If we want to have a look of the time it took we will be amazed :

The sum of the time starting from the user request till the response from the Assistant is around 10 seconds. We have a limit of 10 minutes for Function Calling execution so we can built a whole Infrastructure using just our prompts.

Conclusion

In conclusion, this experiment highlights the powerful synergy between Azure AI Assistant’s Function Calling capability and the automation potential of Logic Apps. By successfully tackling two distinct challenges, we’ve demonstrated how this combination can streamline workflows, boost efficiency, and unlock new possibilities for integrating intelligent decision-making into your business processes. Whether you’re automating customer support interactions, managing data pipelines, or optimizing resource allocation, the integration of AI assistants and Logic Apps opens doors to a more intelligent and responsive future. We encourage you to explore these tools further and discover how they can revolutionize your own automation journey.

References:

Getting started with Azure OpenAI Assistants (Preview)Call Azure Logic apps as functions using Azure OpenAI AssistantsAzure OpenAI Assistants function callingAzure OpenAI Service modelsWhat is Azure Logic Apps?Azure Resource Manager – Rest Operations

​Introduction to AI Automation with Azure OpenAI Assistants IntroWelcome to the future of automation! In the world of Azure, AI assistants are becoming your trusty sidekicks, ready to tackle the repetitive tasks that once consumed your valuable time. But what if we could make these assistants even smarter? In this post, we’ll dive into the exciting realm of integrating Azure AI assistants with Logic Apps – Microsoft’s powerful workflow automation tool. Get ready to discover how this dynamic duo can transform your workflows, freeing you up to focus on the big picture and truly innovative work.Azure OpenAI Assistants (preview)Azure OpenAI Assistants (Preview) allows you to create AI assistants tailored to your needs through custom instructions and augmented by advanced tools like code interpreter, and custom functions. To accelerate and simplify the creation of intelligent applications, we can now enable the ability to call Logic Apps workflows through function calling in Azure OpenAI Assistants. The Assistants playground enumerates and lists all the workflows in your subscription that are eligible for function calling. Here are the requirements for these workflows:Schema: The workflows you want to use for function calling should have a JSON schema describing the inputs and expected outputs. Using Logic Apps you can streamline and provide schema in the trigger, which would be automatically imported as a function definition.Consumption Logic Apps: Currently supported consumption workflows.Request trigger: Function calling requires a REST-based API. Logic Apps with a request trigger provides a REST endpoint. Therefore only workflows with a request trigger are supported for function calling.AI AutomationSo apart from the Assistants API, which we will explore in another post, we know that we can Integrate Azure Logic Apps workflows! Isn’t that amazing ? The road now is open for AI Automation and we are on the genesis of it, so let’s explore it. We need an Azure Subscription and:Azure OpenAI in the supported regions. This demo is on Sweden Central.Logic Apps consumption Plan.We will work in Azure OpenAI Studio and utilize the Playground. Our model deployment is GPT-4o.The Assistants Playground offers the ability to create and save our Assistants, so we can start working and return later, open the Assistant and continue. We can find the System Message option and the three tools that enhance the Assistants with Code Interpreter, Function Calling ( Including Logic Apps) and Files upload. The following table describes the configuration elements of our Assistants:Name DescriptionAssistant nameYour deployment name that is associated with a specific model.InstructionsInstructions are similar to system messages this is where you give the model guidance about how it should behave and any context it should reference when generating a response. You can describe the assistant’s personality, tell it what it should and shouldn’t answer, and tell it how to format responses. You can also provide examples of the steps it should take when answering responses.DeploymentThis is where you set which model deployment to use with your assistant.FunctionsCreate custom function definitions for the models to formulate API calls and structure data outputs based on your specificationsCode interpreterCode interpreter provides access to a sandboxed Python environment that can be used to allow the model to test and execute code.FilesYou can upload up to 20 files, with a max file size of 512 MB to use with tools. You can upload up to 10,000 files using AI Studio.The Studio provides 2 sample Functions (Get Weather and Get Stock Price) to get an idea of the schema requirement in JSON for Function Calling. It is important to provide a clear message that makes the Assistant efficient and productive, with careful consideration since the longer the message the more Tokens are consumed.Challenge #1 – Summarize WordPress Blog PostsHow about providing a prompt to the Assistant with a URL instructing it to summarize a WordPress blog post? It is WordPress cause we have a unified API and we only need to change the URL. We can be more strict and narrow down the scope to a specific URL but let’s see the flexibility of Logic Apps in a workflow.We should start with the Logic App. We will generate the JSON schema directly from the Trigger which must be an HTTP request.   {
“name”: “__ALA__lgkapp002”, // Remove this for the Logic App Trigger
“description”: “Fetch the latest post from a WordPress website,summarize it, and return the summary.”,
“parameters”: {
“type”: “object”,
“properties”: {
“url”: {
“type”: “string”,
“description”: “The base URL of the WordPress site”
},
“post”: {
“type”: “string”,
“description”: “The page number”
}
},
“required”: [
“url”,
“post”
]
}
}   In the Designer this looks like this : As you can see the Schema is the same, excluding the name which is need only in the OpenAI Assistants. We will see this detail later on. Let’s continue with the call to WordPress. An HTTP Rest API call: And finally mandatory as it is, a Response action where we tell the Assistant that the Call was completed and bring some payload, in our case the body of the previous step: Now it is time to open our Azure OpenAI Studio and create a new Assistant. Remember the prerequisites we discussed earlier!From the Assistants menu create a [+New] Assistant, give it a meaningful name, select the deployment and add a System Message . For our case it could be something like : ” You are a helpful Assistant that summarizes the WordPress Blog Posts the users request, using Functions. You can utilize code interpreter in a sandbox Environment for advanced analysis and tasks if needed “. The Code interpreter here could be an overkill but we mention it to see the use of it ! Remember to save the Assistant. Now, in the Functions, do not select Logic Apps, rather stay on the custom box and add the code we presented earlier. The Assistant will understand that the Logic App named xxxx must be called, aka [“name”: “__ALA__lgkapp002“,] in the schema! In fact the Logic App is declared by 2 underscores as prefix and 2 underscores as suffix, with ALA inside and the name of the Logic App.Let’s give our Assistant a Prompt and see what happens:The Assistant responded pretty solidly with a meaningful summary of the post we asked for! Not bad at all for a Preview service.Challenge #2 – Create Azure Virtual Machine based on preferencesFor the purpose of this task we have activated System Assigned managed identity to the Logic App we use, and a pre-provisioned Virtual Network with a subnet as well. The Logic App must reside in the same subscription as our Azure OpenAI resource.This is a more advanced request, but after all it translates to Logic Apps capabilities. Can we do it fast enough so the Assistant won’t time out? Yes we do, by using the Azure Resource Manager latest API which indeed is lightning fast! The process must follow the same pattern, Request – Actions – Response. The request in our case must include such input so the Logic App can carry out the tasks. The Schema should include a “name” input which tells the Assistant which Logic App to look up:  {
“name”: “__ALA__assistkp02” //remove this for the Logic App Trigger
“description”: “Create an Azure VM based on the user input”,
“parameters”: {
“type”: “object”,
“properties”: {
“name”: {
“type”: “string”,
“description”: “The name of the VM”
},
“location”: {
“type”: “string”,
“description”: “The region of the VM”
},
“size”: {
“type”: “string”,
“description”: “The size of the VM”
},
“os”: {
“type”: “string”,
“description”: “The OS of the VM”
}
},
“required”: [
“name”,
“location”,
“size”,
“os”
]
}
}  And the actual screenshot from the Trigger, observe the absence of the “name” here:   Now as we have number of options, this method allows us to keep track of everything including the user’s inputs like VM Name , VM Size, VM OS etc.. Of Course someone can expand this, since we use a default resource group and a default VNET and Subnet, but it’s also configurable! So let’s store the input into variables, we Initialize 5 variables. The name, the size, the location (which is preset for reduced complexity since we don’t create a new VNET), and we break down the OS. Let’s say the user selects Windows 10. The API expects an offer and a sku. So we take Windows 10 and create an offer variable, the same with OS we create an OS variable which is the expected sku:  if(equals(triggerBody()?[‘os’], ‘Windows 10’), ‘Windows-10’, if(equals(triggerBody()?[‘os’], ‘Windows 11’), ‘Windows-11’, ‘default-offer’))     if(equals(triggerBody()?[‘os’], ‘Windows 10’), ‘win10-22h2-pro-g2’, if(equals(triggerBody()?[‘os’], ‘Windows 11’), ‘win11-22h2-pro’, ‘default-sku’))  As you understand this is narrowed to Windows Desktop only available choices, but we can expand the Logic App to catch most well know Operating Systems.After the Variables all we have to do is create a Public IP (optional) , a Network Interface, and finally the VM. This is the most efficient way i could make, so we won’t get complains from the API and it will complete it very fast ! Like 3 seconds fast ! The API calls are quite straightforward and everything is available in Microsoft Documentation. Let’s see an example for the Public IP: And the Create VM action with highlight to the storage profile – OS Image setup: Finally we need the response which can be as we like it to be. I am facilitating the Assistant’s response with an additional Action “Get Virtual Machine” that allows us to include the properties which we add in the response body: Let’s make our request now, through the Assistants playground in Azure OpenAI Studio. Our prompt is quite clear: “Create a new VM with size=Standard_D4s_v3, location=swedencentral, os=Windows 11, name=mynewvm02”. Even if we don’t add the parameters the Assistant will ask for them as we have set in the System Message. Pay attention to the limitation also . When we ask about the Public IP, the Assistant does not know it. Yet it informs us with a specific message, that makes sense and it is relevant to the whole operation. If we want to have a look of the time it took we will be amazed :The sum of the time starting from the user request till the response from the Assistant is around 10 seconds. We have a limit of 10 minutes for Function Calling execution so we can built a whole Infrastructure using just our prompts.ConclusionIn conclusion, this experiment highlights the powerful synergy between Azure AI Assistant’s Function Calling capability and the automation potential of Logic Apps. By successfully tackling two distinct challenges, we’ve demonstrated how this combination can streamline workflows, boost efficiency, and unlock new possibilities for integrating intelligent decision-making into your business processes. Whether you’re automating customer support interactions, managing data pipelines, or optimizing resource allocation, the integration of AI assistants and Logic Apps opens doors to a more intelligent and responsive future. We encourage you to explore these tools further and discover how they can revolutionize your own automation journey.References:Getting started with Azure OpenAI Assistants (Preview)Call Azure Logic apps as functions using Azure OpenAI AssistantsAzure OpenAI Assistants function callingAzure OpenAI Service modelsWhat is Azure Logic Apps?Azure Resource Manager – Rest Operations  Read More

​

Windows updates are essential for keeping your devices secure and up to date with the latest security, performance, and reliability improvements. One of the top customer requests we receive is to enable Windows updates during provisioning in the out-of-box experience (OOBE), so that devices are fully patched and ready to use as soon as they are enrolled with mobile device management (MDM).

In the coming weeks, the Windows MDM enrollment experience will be updated to automatically enable quality updates during OOBE. Quality updates are monthly updates that provide security and reliability fixes, as well as enhancements to existing features. These updates are critical for the performance and security of your devices, and we want to make sure they’re delivered as soon as possible. Please note that not every monthly quality update will be made available through the OOBE. Microsoft will determine the availability of these updates based on the value of the update and how it relates to a device setup situation.

What’s changing

With the upcoming October Windows update, all Windows 11, version 22H2 and higher, devices that are enrolled with an MDM, e.g. Microsoft Intune, will automatically download and install quality updates during OOBE. This will apply to all MDM-enrolled devices, regardless of whether they’re pre-registered with Windows Autopilot or not. The updates will be applied before the user reaches the desktop, ensuring that the device is fully patched before logging in.

The new experience will look like this:

After the device connects to the internet and checks for updates, if there are available quality updates found, the device displays a message on the updates page stating that updates are available and being installed.
The device then downloads and installs the quality updates in the background, while showing installation progress.
Once the updates are installed, the device restarts and continues to the desktop. The user then signs in to the device and the device completes enrollment.

Please note that this change only applies to quality updates. Feature updates, which are major updates that introduce new functionality, and driver updates, which provide hardware-specific fixes or enhancements will not be applied during OOBE but will be managed by your MDM according to your policies.

Impacts and what this means for you

While we believe that this change will improve the Windows enrollment experience and provide more security and reliability for your devices, we also want to make you aware of some potential impacts and what you need to do to prepare.

Additional time in OOBE

Quality update installation during OOBE adds some additional time to the device setup process, depending on when the device was most recently updated, internet speed, and device performance. We recommend notifying your vendors and customers of this additional time, and plan accordingly for your device deployment scenarios.

Organizations using temporary passwords

With the additional time for setup, if using Temporary Access Pass (TAP), the passcode may expire before the user signs onto the desktop. To avoid this, we recommend that you extend the validity period of the temporary passwords during enrollment.

Summary

There may be instances where the update is not initiated if the Windows Update for Business (WUfB) policies that block or delay updates are applied to the device before reaching the New Device Update Page (NDUP). This is particularly possible if app installations significantly delay the Enrollment Status Page (ESP).

At this time, there’s no option to control or disable quality updates during OOBE. As mentioned earlier in this blog, we’re exploring when all monthly quality updates can be available and manageable during OOBE to provide the best overall experience.

We hope that this change will improve your Windows Autopilot experience and provide more security and reliability for your devices. If you have any feedback or questions, please let us know in the comments or reach out on X @IntuneSuppTeam.

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Happy Monday and what a way to kick off the week! Just started back this morning after a 10-week Sabbatical (THANK YOU MICROFT!) and was greeted with the kickoff of the Microsoft 365 Copilot Wave 2 presented by Microsoft’s Satya Nadella and Jared Spataro. The streaming Wave 2 kickoff centered on 3 main areas of focus: 1- Copilot Pages, 2- Copilot in Microsoft 365 Apps, and 3- Copilot Agents. If you missed the session no worries. I have grabbed a link to the recording, the follow-up Copilot blog post, some individual deep dive videos as well as some additional content to help you on your Copilot Wave 2 journey.

Watch the recording of today’s announcements on LinkedIn by clicking here.
Check out the blog post “Microsoft 365 Copilot Wave 2: Pages, Python in Excel, and agents”
Watch videos on:

Copilot Agents in Sharepoint

Copilot Studio Agent Builder
Copilot Pages
Prioritize my Inbox in Outlook
Python in Excel
Narrative Builder in PowerPoint

Get powerful Microsoft 365 Copilot adoption resources available to help your organization on its Copilot journey
Microsoft 365 Copilot home page 

Thanks for visiting – Michael Gannotti   LinkedIn

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