Published Release Notes

Support for the JSON Web Token Bearer grant type for accessing external APIs

You can now access external APIs by using the new OAuth 2.0 JSON Web Token (JWT) Bearer grant type, in an OAuth 2.0 authentication profile. To use the JWT Bearer grant type as a client assertion, source the JWT from an active SSO session, a token profile, or a property reference. You can use JWTs that you obtain during an OpenID Connect SSO in connectors, to achieve user impersonation flows, such as the On-Behalf-Of (OBO) flow. The OAuth 2.0 type authentication profile now also supports authentication of client applications by using Private Key JWTs.

Instances of the OAuth 2.0 provider are now deprecated. As a best practice, use the new, unified authentication profile configuration instead.

For more information, see Configuring an OAuth 2.0 authentication profile.

Upgrade impact

After an upgrade to Pega Platform 8.4 and later, Authentication Profiles can take advantage of the new JWT based OAuth 2.0 grant type and client authentication features. To take advantage of this and other new security features, you must update any existing Authentication Profiles formats must to use those in Pega Platform 8.4 and later.

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

Since these features are available only for profiles created in Pega Platform 8.4 and later, clients must open and then save existing 'Authentication Profile' instances to ensure that the configuration is compatible with the latest authentication formats.

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.

Prediction Studio notifications

Prediction Studio now displays notifications when adaptive or predictive models encounter problems. For example, if the performance of a model decreases suddenly, a notification appears, which you can use to immediately investigate the cause of the problem.

For more information, see Accessing Prediction Studio notifications and Prediction Studio notification types.

Production data sampling and migration for decision strategy simulations

Pega Platform™ now supports the sampling and migration of data from the production environment to the simulation environment. The data that is migrated for simulations includes customer details, Adaptive Decision Management information, and interaction history. By running a simulation on that sample data in Pega Marketing™ or Pega Customer Decision Hub™, you can verify how the changes to your decision logic impact the results. You can use that information to optimize and adjust your decision algorithms and processes.

For more information, see Deploying sample production data to a simulation environment for testing.

Integrate text analytics with decision strategies through the Interaction API

The Interaction API provides more context for making next-best-action decisions by integrating text analytics with decision management components such as strategies, propositions, and interaction history. By including natural language processing in your decisioning solution through the Interaction API, you ensure that the next-best-action decisions that you make are more informed and accurate.

For more information, see Customizable Interaction API for text analytics.

Support for machine learning as a service models

In Pega Platform™, you can now run advanced machine learning and artificial intelligence models that you develop in third-party tools. By configuring a connection with an external machine learning as a service provider, such as Google AI Platform, you can use the predictive power of your custom models to improve the predictions in your customer strategies.

For more information, see Connecting to a Machine Learning as a Service model and Configuring a machine learning service connection.