Published Release Notes

Tabbed templates in portal authoring

Portal authoring in Pega Platform™ now includes tabbed templates that provide you with the tools to build complex user interfaces with less effort. The new templates help you save screen space and display the contents of your application in smaller, meaningful chunks.

For more information, see Configuring a tab-based landing page.

Support for calling a data transform from a case life cycle in App Studio and initializing data at case creation

You can now call a data transform from a case life cycle in a low-code way in App Studio. For example, when a customer wants to order the same product again, your application can reuse the shipping information from their previous order. Additionally, by configuring data initialization for a case type, you can prepopulate fields at case creation. For example, an application for a delivery service can apply the customer's preferred delivery options when a new case starts. These enhancements save time for developers because the system can initialize data and prepopulate fields in a case by reusing existing logic, and for customers, who do not need to manually enter their details every time.

For more information, see Calling a data transform from a case and Initializing data at case creation.

Update to Intelligent Virtual Assistant official names

To use better and more consistent names for conversational channels in Pega Platform™ and to help distinguish conversational channels from Pega Unified Messaging Edition™, Pega Intelligent Virtual Assistant™ (IVA) for Unified Messaging is now called IVA for Digital Messaging. Furthermore, the IVA for Web Chatbot is now officially called IVA for Legacy Webchat.

For more information, see Creating a Digital Messaging channel and Pega Intelligent Virtual Assistant features.

Text predictions simplify the configuration of text analytics for conversational channels

Enable text analytics for your conversational channels, such as email and chatbot, by configuring text predictions that manage the text models for your channels. This new type of prediction in Prediction Studio consolidates the AI for analyzing the messages in your conversational channels in one place and replaces the text analyzer rule in Dev Studio.

Through text predictions, you can efficiently configure the outcomes that you want to predict by analyzing the text in your channels:

• Topics (ticket booking, subscription cancellation, support request)
• Sentiments (positive, neutral, negative)
• Entities (people, organizations, airport codes)
• Languages

You can train and build the models that predict these outcomes through an intuitive process, and then monitor the outcomes through user-friendly charts.

For more information, see Predict customer needs and behaviors by using text predictions in your conversational channels.

Upgrade impact

Channels that you configured with text analyzers in the previous version of your system continue to work in the same manner after the upgrade to the current version. When you edit and save the configuration of an existing channel, the text analyzer rule is automatically upgraded to a text prediction. The associated text prediction is now an object where you can manage and monitor the text analytics for your channel. When you create a new channel in the upgraded system, the system automatically creates a text prediction for that channel.

What steps are required to update the application to be compatible with this change?

1. Enable the asynchronous model building and reporting in text predictions through job schedulers that use the System Runtime Context (SRC) by adding your application to the SRC.
   For more information, see Automating the runtime context management of background processes.
2. Enable model building in text predictions by configuring background processing nodes.
   For more information, see Assigning decision management node types to Pega Platform nodes.

Ability to train the text analytics model based on email attachments

You can now train the text analytics model for Pega Email Bot™ based on training data from email attachments. As a result, you improve the natural language processing (NLP) analysis for your email bot so that your system detects the correct language, sentiment, topics, and entities from both the body of emails and their attachments. By default, you train the text analytics model based only on training data from the body of emails.

For more information, see Enabling training the model based on email attachments.

Data propagation for questionnaires in case types

Pega Platform™ now provides a tool to prepopulate answers in a questionnaire that runs in a case by propagating data from a parent case. At run time, specified questions in a questionnaire that users complete as part of case processing, can display answers by using information that users already provided in the parent case. As a result, you speed up the development of your application and help users process cases and complete questionnaires faster.

For more information, see:

• Deliver user-friendly questionnaires by prepopulating answers (8.6)
• Running a questionnaire in a case

Deprecation of EAR deployments in Pega Platform 8.6

Starting with Pega Platform 8.6, EAR deployments of Pega Platform are deprecated. In the upcoming 8.7 release, EAR deployments will not be supported. The following rules that require EAR deployments will also be deprecated and no longer supported in Pega Platform 8.7:

• Connect EJB
• Connect JCA
• JCA Resource Adapter
• JMS MDB Listener
• Service EJB

With this deprecation, you can use the latest tools and keep your application up to date. 

Upgrade impact

In Pega Platform 8.6, you can still create rules that require EAR deployments and update existing rules. However, after an upgrade to Pega Platform 8.7, rules that require EAR deployments are no longer supported.

What steps are required to update an application to be compatible with this change?

To prepare your application for this change well in advance of the Pega Platform 8.7 release, see the following table for a list of alternative rules and for guidance on modernizing your application.

For information on the supported platforms that are affected by this deprecation, see Pega Platform 8.6 Support Guide.

Upgrading to the secure threading mechanism for email bots

In Pega Platform™ version 8.6, Pega Email Bot™ includes a more secure threading mechanism to help track emails from customers and other stakeholders in separate threads for an email triage case.

Upgrade impact

If you upgrade from Pega Platform version 8.5 or earlier, in which you configured an Email channel, perform the following steps to ensure that your system uses the new secure threading mechanism:

• Update the service email rule that the system uses to send an email reply as the initial acknowledgment.
• Update the email reply template in the data transform rule that the system uses when a customer service representative (CSR) sends the reply.

For more information about creating an initial acknowledgment email and email reply template, see Creating outbound email templates. For more information about the secure threading mechanism, see Use a secure threading mechanism in emails.

What steps are required to update the application to be compatible with this change?

For the initial acknowledgment email used by your email bot, update the service method for your email listener rule. On the Response tab for this service email rule, expand the Message contents section. In the Message data section, you specify the rule that defines the structure of the content of the email body. In Pega Platform version 8.6, you use for this purpose the pyEmailAcknowledgement correspondence rule that takes into account the selected built-in template. This template includes the security code tag that the system uses for the secure threading mechanism. If your application uses a different rule in the Message data section, update this definition to match one of the built-in correspondence template rules, for example, EmailAckTemplate_Clear.

The pySetEmailBotReplyTemplate data transform rule sets the name of the email correspondence rule that the system uses as the email reply template. If you do not want to use the default approach using the Classic, Cobalt, or Clear outbound email template themes, override this data transform rule to set the email correspondence rule name for the Param.ReplyTemplate target in the Source column field.

For more information about how to update the service email rule and the data transform rule to ensure that your system uses the secure threading mechanism, see Upgrading to the threading mechanism available in the 8.6 version.

Improved indexing performance by gradual retrieval of data

Search functionality in Pega Platform™ versions from 8.4.5 to 8.6 now includes the option to improve indexing performance when reading query results from a large table in a database. For example, to load the recommended 50 records at a time, in the Pega-SearchEngine ruleset, create the indexing/distributed/fetchsize dynamic system setting, and set the value to 50.

Creating the fetchsize dynamic system setting ensures that your system does not crash while indexing classes with numerous instances.

For more information, see Creating a dynamic system setting.

Automatic packaging of cases for offline use

Newly created mobile channels now support automatic packaging of cases for use in offline mode. The packaging process includes case data from all native list pages that you create from offline-enabled case types and add to the navigation of your mobile app. Previously, automatic packaging was limited to assignments in the work list (and their related work items), and cases that were listed in the data page that you manually designated for packaging.

This enhancement improves the user experience for mobile app developers by ensuring that the data that is referenced in list pages for offline-enabled case types is automatically available for the users of your offline app.