Published Release Notes

Configuration sets to support no-code run time changes

App Studio now provides the option to create configuration sets that define application behavior, which application administrators can adjust at run time, in a no-code way. For example, you can define a configuration that defines the maximum loan amount to offer a Gold account customer to $10,000. If the bank changes the maximum amount, the application administrator can easily change the loan amount at run time, without performing any design-time actions. Configuration sets minimize the need for custom rule overrides, and help you deliver applications that are convenient to maintain and upgrade.

For more information, see:

• Deliver no-code applications with configuration sets (8.6)
• Creating a configuration set

Low-code authoring of case pages

Mobile authoring for Cosmos React applications now supports the configuration of native mobile pages that include case details. Your mobile channel automatically generates these pages for each case type in your application. This enhancement improves the user experience in your app by providing users with native pages that you can adjust to best suit your business process. For example, you can highlight specific views on a page by modifying which tabs appear on the page, or add a floating action button to help your users edit the case.

For more information, see Authoring mobile case pages for Cosmos React applications.

In Pega Platform™ version 8.6, mobile features in Cosmos React applications are available only as a preview, with limited support for various interface elements and features, such as offline mode. The recommended production design system for Pega Mobile in Pega Platform 8.6 is Theme Cosmos.

For more information about the Cosmos React design system, see Cosmos React and UI architecture comparison.

Ability to handle responses to automatic email notifications in email bots

Pega Email Bot™ can now handle email responses to automatic email notifications that a Pega Platform™ application sends for a related case. As a result, customer service representatives (CSRs) can save time because all relevant emails from customers and other stakeholders are available from a single triage case in the system. To use this feature, ensure that you add the same email account to the email bot that you use to send case notifications in your application.

For more information, see Handle customer responses to automatic email notifications.

More specialized data fields

Pega Platform™ now provides more prescriptive and specialized options to reuse data, which replace data relationships. Now, to reuse information across your application, you can create a field that references a case type, a data object, data embedded in a case, or a data page. As a result, you do not need detailed knowledge about how your system stores data to successfully reuse information in your business processes.

For more information, see:

• Creating fields for capturing data
• Embedding data in a case
• Referencing a data page

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