Published Release Notes

Specify the scope for rolling back rules and data to a restore point

Create restore points to save the state of all rules and data in your system at a significant point in time, for example, before you import an application. Roll back to that restore point to return the system to that state. Now, you can filter which rule and data instances are returned to their previous state:

• System: Roll back every rule and data instance that has a history record.
• User: Roll back rule and data instances modified by a specific user. If any rule was changed by more than one user, you will see an error message and must use the system rollback.
• Application: Roll back rule and data instances in a specific application.

For more information, see Using restore points to enable error recovery.

Support for data page aggregation

You can now aggregate data from multiple sources for faster and easier implementations. With this feature, you can load data pages more easily without using an activity or data transform. 

For more information, see Creating a data page. 

Connect to Amazon SageMaker models in Prediction Studio

Make the most of your custom Amazon SageMaker models in Pega Platform™ by connecting to the models in Prediction Studio. You can then run the Amazon SageMaker models as part of your decision strategies.

For more information, see Enrich your decisioning strategies with H2O and Amazon SageMaker predictive models (8.4).

Import H2O models to Prediction Studio

Make the most of your custom H2O models in Pega Platform™ by importing them to Prediction Studio. You can then include the H2O models in your decision strategies.

For more information, see Enrich your decision strategies with H2O and Amazon SageMaker predictive models (8.4).

Support for auditing adaptive model decisions

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.

For more information, see Configuring the Adaptive Decision Manager service.

Create predictions in Prediction Studio

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.

For more information, see Create predictions in just a few clicks (8.4).

Sign and encrypt signatures and content with additional algorithms

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.

For more information, see Creating a processing JSON Web token profile.

For more information, see Creating a generation JSON Web token profile.

Improvements for test cases and assertions

The process of modifying test cases and assertions has been improved. Adjusting test cases to application changes is now much easier.

You now can:

• Select a page on which to run a tested rule.
• Change the class and rule of unit test cases.
• Create assertions that validate specific error messages on pages, properties, and activities.
• Automatically update decision result assertions with property changes made to a rule.
• Modify a rule's properties directly from decision result assertions.

For more information, see:

• Updating scenario tests
• Setting up your test environment
• Configuring page assertions
• Configuring property assertions
• Configuring decision result assertions

Anypicker control is now available

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.

For more information, see Adding an Anypicker control.

Email bots now support multiple languages

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.

For more information, see Selecting languages for an Email channel and Enabling automatic language detection for text analysis.