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Using Azure AI for Entity Creation (Using Run AI Prompt step)

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Article published on the 26th of August, 2026.

The functionality surrounding the Run AI Prompt step is released in Release Candidate on the 26th of August, 2026.

1. Introduction

In this article, we will demonstrate how you can leverage artificial intelligence functionality in Azure when creating entities such as cases, contracts, or projects in WorkPoint 365.

For demonstration, we will use artificial intelligence in a process to extract case information from an incoming e-mail, and use it to fill an Entity form in WorkPoint Automate so we can create a new case in the system.

For this purpose, we will go through the following steps:

  • Set up an Azure AI Foundry resource and deploy an AI model.
  • Set up a couple of HTTP endpoints in WorkPoint.
  • Set up a WorkPoint Automate process to call the HTTP endpoint and create a new case.

2. Requirements

Following the steps in this article requires that you have already set up the following:

  • An Azure subscription: An Azure subscription is the foundational billing and access boundary in Microsoft Azure.
  • A resource group: A resource group is a logical container within a subscription that holds related Azure resources like virtual machines, databases, and importantly, AI services.
  • An Azure AI Foundry resource: The primary Azure resource type for building, deploying, and managing generative AI models and applications including agents in Azure. You can read about how to set up an Azure AI Foundry resource here.

3. Deploying an AI Model

Before we can use Azure AI in WorkPoint 365 to help us create entities, items, and do other actions, we need to deploy an AI model to our Azure AI Foundry resource.

Assuming you already set up an Azure AI Foundry resource, you can deploy an AI model to the resource by navigating to https://ai.azure.com/resource.

This should open a page similar to the following:

  1. In the left side menu, under "My assets", click "Models + endpoints" (This may also be called "Deployments" and live under a section called "Shared resources").
  1. On the Model deployments page, click "Deploy model" and select "Deploy base model".

This opens the following dialogue:

Here we need to select an AI model of type "Chat completion", because we eventually want to give the AI model a prompt, and have it respond with some message containing data of some sort.

It is important to consider which model to deploy and use, because they may vary greatly in cost of tokens per API call. If you need it to process simple data, and give relatively simple responses, as we do in this example, you should select a fairly inexpensive, simple AI model.

In this instance, we will select the gpt-5-mini model:

  1. We search for and select the gpt-5-mini model.
  2. We then click the "Confirm" button.
  1. In this case we keep the automatically generated name for the deployment, and select the "Standard" deployment type. You can read more about the different deployment types by selecting them, and clicking the links in their individual descriptions.
  2. We then click the "Deploy" button.

Once the model is deployed, you should see it listed in the Model deployments page:

  1. To view more information about the deployment, it's endpoint and it's API key, we click the title.

This should open a page similar to the following:

4. Setting up a credential for Azure AI endpoint

To be able to call the Azure AI model from WorkPoint, we need to configure a credential in WorkPoint, which is what we will configure in this section.

  1. On the model deployment page, click the Copy to Clipboard button for the Key. We need til to configure a credential for calling the AI model endpoint:
  1. In the Http Endpoint Library in the WorkPoint administration, in the Credentials section, select "API Key" and click the "Add" button.
  1. In the "Title" field, write a fitting title for the new credential.
  2. In the "API Key" field, paste the copied Azure AI endpoint Key.
  3. To save the credential, click the "Save" button.
  1. In the confirmation dialogue, click "Yes".

The new credential has now been created:

5. Setting up a HTTP endpoint

In this section, we will set up a HTTP endpoint inside WorkPoint to send a request to the previously configured AI deployment, and get a response back. We will then explore how we can use this setup for entity creation in WorkPoint 365.

We will start back in the Azure AI model deployment page, where we need to copy the following pieces of information:

  1. First, we need the deployment name.
  2. Next, we need the target URI for the AI model. This starts at "https" and ends right before the question-mark.
  3. Lastly, we need the api version, which we can find in the "Getting Started" panel (or in the URI after the question-mark).

Next, we can continue in the Http Endpoint Library in the WorkPoint administration:

  1. In the HTTP endpoint library, we click the "Add" button to set up a new endpoint.
  1. In the "Endpoint Type" selector, select "Azure AI".
  2. In the "Title" field, type a fitting title for the endpoint configuration. This is only used for selecting the endpoint later in the WorkPoint Automate process and is never shown to the end user.
  3. In the "Deployment Name" field, paste the deployment name copied from the AI model deployment page earlier.
  4. In the "API Version" field, paste the api version copied from the AI model deployment page.
  5. In the "Url" field, paste the endpoint  URI copied from the AI model deployment page.
  6. In the "Credential" field, select the credential created in the previous section of this article.
  7. Click the "Test" button to test the configuration. The result should include a response from the AI model either pinging  back or greeting you:
  1. Upon successfully getting a response from the AI model, click the "Autogenerate from test" in the "Schema" section. This will automatically generate a return schema for the endpoint so we can know the general structure of the response inside WorkPoint Automate.
  2. To add the configuration, click the "Add" button.

The http endpoint configuration has now been added to the Http Endpoint Library:

We now have a way of contacting our deployed Azure AI model and we can now move on to the next section of this article.

6. Setting up a Graph endpoint to extract e-mail content

Next, we need to set up a Graph endpoint which can take an e-mail message ID and extract information from it. But before we do so, we need to set up a credential so that we can call the Graph in the first place:

6.1. Setting up a credential

  1. On the Http Endpoint Library page, in the Credentials section, click the "Add" button.
  1. We provide a fitting title for the credential, in this case "MSGraph".
  2. For Credential Type, we select "Azure AD User Delegated". This allows WorkPoint to act on behalf of the signed-in user to access Azure resources, using the user's identity and permissions. This is required for WorkPoint Automate to call a given Graph endpoint using the user's permissions to access various resources they have access to.
  3. In the Tenant field, we provide our Microsoft 365 Tenant ID. You can find yours by going to https://aad.portal.azure.com/.
  4. In the Scope field, we input "https://graph.microsoft.com/.default"
  5. We can now click the "Add" button to add this credential.
  1. If met by a confirmation dialogue, click the "Yes" button.

The new credential should now appear in the Http Endpoint Library:

Next, we can set up the Graph endpoint to extract e-mail information.

6.2. Setting up Graph endpoint

In this section, we will set up a Graph endpoint which, provided an e-mail ID, will extract information from it, for example the e-mail's sender, subject, body preview, etc. When calling this endpoint, the "Express" context object in WorkPoint Automate will provide the e-mail ID.

  1. On the Http Endpoint Library page, in the Http Endpoints section we click the "Add" button to add a new endpoint.
  1. In the "Endpoint Type" selector, we select "Generic".
  2. In the "Title" field, we provide a fitting name, in this case "Read Email".
  3. Next, we add a parameter called "messageId". This is the parameter in which we will eventually pass the e-mail ID from the Express context object in WorkPoint Automate.
  4. In the "Url" field, we provide the following Graph endpoint URI: https://graph.microsoft.com/v1.0/me/messages/[messageId].
  5. In the "Http(s) endpoint action" field, we select "Get".
  6. In the "Header" section, we add a header with the "Prefer" key, and the following value: outlook.body-content-type="text". This ensures that the returned e-mail body will be in plain text instead of HTML.
  7. In the "Query" section, we add a query with the "$select" key, and the following value: sender,sentDateTime,subject,hasAttachments,bodyPreview,receivedDateTime,toRecipients,body
  8. In the "Credentials" field, we select the credential we made in the previous section.

This will allow us to call the Graph and extract the information specified in our $select query.

To test this endpoint and auto-generate the return schema, we will create a simple 1-step user process in WorkPoint Automate which simply prints out the e-mail ID of a selected e-mail in Outlook to a text field on a Custom form:

  1. We create a user process and add a custom form step to it. We then add a text field to the form and expand the options. Then, we click the "Advanced" button for the Value field.
  1. We switch to Advanced mode and input the following adaptive expression:
string(Express[0].ItemId)
Click to copy
  1. We then save the process as draft.
  2. Finally, we publish the process so we can run it from Express365 in Outlook.

We then set up a My Tools button for Express365 to execute the process:

Don't forget to set "Display in app" to Express 365.

Inside Express365, we can then select an e-mail and run the process, which should give us the e-mail ID:

The e-mail ID is shown in the text field:

We can now try and copy this e-mail ID and paste it into the messageId parameter of our Read Email http endpoint:

Clicking the "Test" button near the end of the page should now show the information we requested in the $select query:

  1. To automatically generate the response schema for our endpoint, we click the "Autogenerate from test" button, which fills the Schema field:
  1. We can now click the Save or Add button to save our endpoint.

We now have our two endpoint configurations and credentials:

This is everything we need to set up our entity creation process, so let's look at that next.

7. Setting Up Entity Creation Process

In this section, we will use the two endpoint we created previously to automatically populate an Entity form with data based on an e-mail in Outlook.

The process will consist of the following steps:

  1. Send HTTP request: In this step, we use the Read Email http endpoint which, provided an e-mail ID, returns information from the e-mail. Specifically, we need the "body" information from the e-mail in the next step.
  2. Send HTTP request: In this step, we take the information from the e-mail body we got from the previous step, and let our Azure AI model analyze it and come up with a JSON object we can use as entity data (in this case, Case data).
  3. Entity form: In this form, we will present the data that the AI model reasoned would be fitting for the case we are about to create.
  4. Create entity: This step will create the case based on the data from the Entity form, i.e., the information that the AI model provided.

We start with an empty process which we have titled "Create case from e-mail":

Next, we add a Send HTTP request step:

Next, we give the step a fitting title, description, and name:

In the Options tab, we select the Read Email endpoint and provide the ItemId from the Express context object in the messageId parameter:

Next, we add a Run AI prompt step and give it a fitting title, description, and name:

In the Options tab, we select the Azure AI - gpt-5-mini endpoint and configure a System- and User message:

  1. We select the Azure AI - gpt-5-mini endpoint we configured earlier.
  2. In the "System message" field, we define a system message for the AI model used. System messages are used for giving High-level instructions that define the AI model's behaviour (role, tone, rules, constraints).
  3. In the "User message" field, we define what content we want to pass to the AI for it to analyze and respond to. In this case, we have selected the body content from the ReadEmail step from the process Context.

In the "Response" tab of the Run AI prompt step we can define the structure of the output JSON from the AI model:

  1. In the "JSON object" field we can insert sample JSON data and click the "Generate schema" button. Note that the sample JSON must be unformatted and in one line.
  2. Upon entering valid JSON and clicking the Generate schema button, the "JSON schema" field should receive a JSON schema which the AI model's response will confine to.

To test the process and see what the AI model actually produces for us at this point, let's try adding a custom form with a text field containing the output from the Analyze Email step:

In the Value field of the text field, we use the following adaptive expression:

string(AnalyzeEmail)
Click to copy

At this point, we can save and publish the process and try running it on a fitting e-mail from Express365.

Here we have selected an e-mail which holds information which could reasonably be construed as being related to a case. We have also set up a My Tools button to run the process we are working on, so let's try and run it:

Next, we copy the output from the text field and put it into a text editor for better inspection:

Specifically, we can see that the AI model generated the following case metadata:

"Case": {
		"Title": "Repair request: BX-5060 (leased unit)",
		"wpDepartment": "Service & Repairs",
		"wpDescription": "Customer reports a fault with leased electronic unit BX-5060 that prevents normal use. Customer requests to return the unit for repair and have it sent back promptly to complete the remainder of the leasing period. Customer needs the equipment for a convention in approximately two weeks and requests expedited handling.",
		"wpStartDate": "2026-06-23",
		"wpDueDate": "2026-07-06",
		"wpBudget": "N/A",
		"wpPriority": "High",
		"wpRisk": "High"
	}
Click to copy

We can now use the Case object as step input for an Entity form, by proving the following into the Step Input:

AnalyzeEmail.Case
Click to copy

So now we add an Entity form to the process:

In the Step Input, we have provided the following:

In the Options tab, we hook the Entity form up to our Cases module:

In the Fields tab, we can now set the fields that the AI model does not automatically provide for us. For example, we can set the Case responsible to the current user:

Next, we can add a Create entity step, and feed it the output from the Case Information form as Step Input:

And in the Options tab, we set it up for our Cases module:

Next, for convenience, we go to the Process settings and enable the "Skip start step" option:

Finally, we can save and publish the process:

8. Running the Process

Now we can finally try and select a proper e-mail and run the process on it.

We have already tested with the following e-mail, so let's try it again:

  1. With the e-mail selected, we click our My Tools button to launch the process.

Once the Case information form is presented to us, we can see that the AI model has read our e-mail content, and suggests useful information, such as a title, a description, a case type, and other relevant case information. The information it doesn't automatically provide, we can fill ourselves:

  1. Next, we click the Continue button.

Our case is now being created:

After completion, we see our case has been created:

9. Extension possibilities

In this section, we will continuously introduce valuable extension opportunities to the processes outlined in this article. The goal is to regularly enhance the described functionality with meaningful and innovative additions.

9.1. Setting the Customer field automatically based on e-mail sender

In this article, we will evaluate the e-mail domain of the person who sent an e-mail and match it against customers registered in our Customers module in WorkPoint. If there is a match between the sender's e-mail domain, and the domain registered for a customer, the customer will automatically be set in the Case information form.

Link to article

9.2. Creating tasks suggested by AI

In this article, we extend both this current article, and the article in 8.1. to also create tasks suggested by the AI model on the newly created case.

Link to article

9.3. Send an e-mail reply suggested by AI

In this article, we will go through how to take the suggested reply from an AI model, and use it to send an e-mail response back to the original sender.

Link to article

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