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Published Release Notes

Find release notes for the selected Pega Version and Capability

Browse resolved issues for Platform releases.

This documentation is for non-current versions of Pega Platform. For current release notes, go here.

Predictive models can drive predictions

Valid from Pega Version 8.6

You can use predictive models as the basis of your predictions. As a data scientist, you can now use Machine Learning Operations (MLOps) to replace models in your system. You can replace a model in a prediction with a PMML, H2O MOJO, or Pega OXL predictive model, as well as a scorecard or field, and then approve the update for deployment to a production environment. You can respond to a Prediction Studio notification that an active model does not generate enough lift and decide to replace the low-performing model with a high-accuracy model. You can also update a prediction on a regular basis, for example, whenever you develop a new churn model in an external environment.

For more information, see Replace models in predictions and migrate changes to production with MLOps.

Enhancing the performance of your Next-Best-Action strategy with globally optimized strategies

Valid from Pega Version 8.6

Starting in version 8.6, Pega Platform™ combines the versatility of Next-Best-Action Designer with strategy performance enhancements provided by using globally optimized strategies (GOS). Decrease the run time and memory usage of executing the Next-Best-Action strategy in batch or real-time scenarios by using the globally optimized strategies generated by Next Best Action Designer.

GOS is supported by Pega Platform's standard business change management process. GOS rules are automatically included in relevant revision packages.

For more information, see Enhance the performance of your Next-Best-Action strategy with globally optimized strategies (8.6).

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

Valid from Pega Version 8.6

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.

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

Valid from Pega Version 8.6

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

Valid from Pega Version 8.6

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.

Kafka data set enhancements

Valid from Pega Version 8.6

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.

External data flow rules are removed

Valid from Pega Version 8.6

In previous versions of Pega Platform™, you could configure data flows to run in an external Hadoop environment. The external data flows functionality was deprecated and hidden from view in Pega Platform 8.5. The functionality has been now removed and is no longer available in Pega Platform 8.6.

For more information, see External data flow rules are deprecated.

Attachment support for requirements

Valid from Pega Version 7.1.6

Requirements now support attachments. You can add attachments from the Designer Studio > Application > Profile > Requirements landing page, the Add/Edit Requirement modal, or the requirement rule form.

Enhanced document generation

Valid from Pega Version 7.1.6

The following usability improvements have been made to documents generated by the Application Document Wizard:

  • Stages and Processes now appear for case types that use Case Lifecycle Management. This diagram includes hyperlinks to corresponding sections in the document and is scoped to top-level processes only.
  • All flows (excluding pyStartCase) referenced by a case type now appear in documents. This removes previous constraints on non-starting, draft, and checked out flows.
  • Entity Relationship Diagrams (ERDs) are now embedded in documents as Visio objects.
  • Sections without content are omitted from the generated document.
  • Screenshots are labeled.
  • Requirement text (Long Description) is included in the Requirements Matrix section.
  • Linked requirements appear in context beneath their associated specification.
  • Acceptance Criteria appears in context beneath the associated specification, report, or correspondence item.

Flexible inputs in strategies

Valid from Pega Version 7.1.6

Decision parameters allow business users to influence the decision through flexible inputs that do not require changing strategies. System architects use extension points to configure the data model and user interface supporting decision parameters. Decision parameters are used in strategies, and changes to the values of decision parameters become immediately available without requiring any changes to the strategy.

The rule pxRunDecisionParameters supports the use of decision parameters through activities.

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