Pega Platform™ now stores all adaptive model scoring data so that you can identify the source of each decision, such as the exact model version that was used for scoring. With this feature, you can ensure that your application is auditable, transparent, and in compliance with regulatory requirements related to using adaptive models.
Predict customer behavior and business events by creating predictions. To create a prediction, you answer a series of questions about what you want to predict. For example, you can create a prediction to determine the likelihood of customer churn.
You can now authenticate using JSON Web Token (JWT) token profiles to symmetrically and asymmetrically encrypt both signatures and content. All algorithms in the Nimbus JWT library are supported, including nested tokens. Custom key identifier headers (kid) are also supported. Use token profiles to securely propagate identities and transfer data between systems.
You can now initiate automatic feedback to entity models in Pega Email Bot™, during manual mapping of email content to a case property.
To enable automatic feedback, you set the Work-Channel-Triage.pyIsRuntimeFeedback rule to true in Pega Platform™. By default this feature is disabled. Enabling this feature ensures that the email bot is more responsive by automatically copying detected entities, such as names, locations, dates, and ZIP codes, to case type properties of a case type.
The new Anypicker control displays a drop-down list of values that you can group into expandable categories for faster browsing. To save time and improve search accuracy, the Anypicker control filters the available values based on the characters that the user enters.
You can now configure Pega Email Bot™ to perform text analysis, use training data and triage emails in multiple languages, for example, English, French, German, and Spanish. By detecting topics and entities from emails in different languages, the system can suggest the correct business case and provide an email response in the user's own language.
The Referencing Rule tool now runs in Dev Studio without the need for Adobe Flash Player. This enhancement integrates the Referencing Rule tool into your work environment, which improves productivity by reducing context switching.
With an upgrade to Pega Platform 8.4 and later, the underlying PD4ML library in Pega Platform changes from v3.10 to v4.x. Following an upgrade, most standard HTML-CSS conversions to PDF work seamlessly; however, if you use the following custom coding in their application to convert HTML to PDF, you may find that PDF generation works differently than expected or no longer works:
Application layer Java in activities that directly link to the underlying PD4ML library
A Rule Utility Function
PD4ML tags in HTML fragments
For example, the following PD4ML proprietary CSS keywords are no longer supported in v4.x:
What steps are required to update the application to be compatible with this change?
After you upgrade to Pega Platform 8.4 and later and find your PDF generation works differently than expected or no longer works, you should consult the latest documentation available at pdml support site. You may also consider using the Compact CSS instead of the application skin for PDF generation; for details, see Creating PDF files by using a compact style sheet.
All Pega product release notes can be found on their product pages.