Published Release Notes

Support for picklists with parameterized data pages in App Studio in Cosmos React UI

You can now use data pages with parameters to populate a property of the picklist type with filtered results in App Studio. For example, in a survey case type, you can use a parameterized data page to configure cascading drop-down controls in which the values in a secondary drop-down list are based on the value that the user selects in the primary drop-down list. With dynamically-sourced picklists, you get greater flexibility in configuring picklists, and users see more accurate values.

For more information, see link Dropdown control Properties — General tab.

Support for configuring and saving external data in App Studio

You can now configure a savable data plan to easily replace or update related data directly from App Studio, and persist those changes during the case lifecycle. This enhancement allows you to work directly in App Studio, without being limited to read-only access of data from case types.

Automation performance enhancements

To improve the performance of automation-based REST services and DX APIs, you can now configure JSON inputs and outputs in automation rules. The automation passes the JSON object directly to an automation-based REST service or DX API, eliminating the need to transform data on a page to JSON first. You can also take advantage of this enhancement if you are calling an automation from an activity. Additionally, you can simulate a JSON response in the automation rule and update the JSON sample if needed.

For more information, see <link TBD> and <link TBD>.

Support for creating automations

You can now create automations in Pega Platform. By creating an automation, you clearly define a contract to perform a specific task. For example, you can create an automation that retrieves a list of data objects that are available in the application.  

For more information, see Creating an automation.

Support for filtering requests and responses in activity-based REST services

You can now use a JSON data transform to filter inputs and outputs in activity-based REST services. With this enhancement, you maintain high performance by filtering unnecessary properties from the REST service request and response.

For more information, see Creating a request for POST, PUT, and PATCH methods by using an activity implementation and Creating a response for Service REST methods by using an activity implementation.

Support for configuring data sources by using OpenAPI Specification

The Integration Designer in App Studio now supports OpenAPI Specification. You can provide an OpenAPI Specification endpoint or upload a YAML file to automatically generate data sources according to the specification. With this enhancement, you avoid having to manually create a representative sample JSON request and response, saving you time and increasing productivity.

For more information, see Integrating with a REST API by using OpenAPI Specification.

Improved resiliency and observability of email infrastructure

Pega Platform now has several enhancements to improve the resiliency and observability of its email infrastructure. With the following enhancements, you can improve service quality and reduce maintenance costs:

• Pega Predictive Diagnostic Cloud alerts notify you of connection timeouts and help you troubleshoot more quickly.
• Default values for email settings ensure that your email configuration is optimal and follows best practices.
• Email listeners automatically retry test connectivity upon start-up if the connection fails or authentication fails.
• Improved logging provides meaningful messages and log categorization to support debugging.

For more information, see List of events and notifications in PDC.

Easier customer record management in Customer Profile Designer (early preview)

The new Customer Profile Designer module of Pega Customer Decision Hub™ makes it possible for marketing analysts and strategy designers to define the associated data for each customer context directly in the Pega Customer Decision Hub portal. It is also possible to define more complex associated data structures that use a custom data flow, or define associated data of different types, such as RDBMS and Cassandra, for the same customer context.

Customer Profile Designer is available in Pega Customer Decision Hub 8.6 version as an early preview version. The functionality will be further expanded in future releases.

For more information, see Manage customer records in Customer Profile Designer (8.6).

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.

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.