Published Release Notes

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.

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.

Upgrading to Hazelcast 4.x requires downtime during upgrades to Pega Infinity 8.6

Upgrade impact statement

On-premises upgrades of Pega Infinity release 8.4.2 and later to version 8.5.1 or later on Tomcat and PostgreSQL are completed with near-zero downtime. However, upgrading to Hazelcast 4.x requires that you shut down and restart your application server.

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

Hazelcast 3.x is enabled by default. For near-zero downtime upgrades, you do not need to perform any action.

For instructions about upgrading to Hazelcast 4.x, see one of the following topics:

• For near-zero downtime upgrades from Pega Infinity release 8.4.2 or later on Tomcat and PostgreSQL, see "Optional: upgrading to Pega Platform version 8.6: Upgrading to Hazelcast 4.x" in Near-zero downtime Upgrade Guide for Pega Platform version 8.4.2 and later for Tomcat and PostgreSQL.
• For all other upgrades, see "Optional: upgrading to Hazelcast 4.x" in the appropriate upgrade guide.

Extend PegaUnit setup and cleanup capabilities using custom fixtures

The set up and clean up capabilities in the PegaUnit framework are now customizable through the use of custom fixtures. Custom fixtures extend the default testing functionality and allow you to define and implement specific runtime actions, such as running a specific test case during set up or sending an email after testing has completed execution. 

For more information, see Setting up and cleaning the context for a test case or test suite. 

Deprecated support for Pega Platform deployments on embedded Cassandra

If you use Pega Platform™ decision management capabilities, Pega Platform uses Cassandra as the underlying storage system for the Decision Data Store (DDS), which manages the Cassandra cluster and stores decision management data in a Cassandra database. Future versions of Pega Platform will no longer support deployments on embedded Cassandra. In Pega Platform version 8.6, deployments using embedded Cassandra are deprecated but still work. To ensure future compatibility, do not create any new installations using embedded Cassandra.

For information about how to configure Pega Platform to access an external database, see Defining Pega Platform access to an external Cassandra database.

Kafka data set enhancements

The Kafka data set is a high-throughput and low-latency platform for handling real-time data feeds that you can use as input for Pega Platform event strategies.

For better integration of Pega Platform with externally hosted Kafka, the following enhancements are implemented:

• Support for Kafka message keys and headers - extended values data format (JSON Data Transform, Apache Avro)
• Custom value processing
• Configuring topic names by using Application Properties
• Data-Admin-Kafka enhancements - supporting a wide range of connection properties

For more information, see Improve your Kafka data set with new enhancements.