Separating concerns by using a Declare Expression for time-based calculations and a Decision Table for business logic (role, weekend) promotes reusability, clarity, and easier maintenance. A single complex When rule becomes difficult to debug and update, while embedding logic in a Flow Action or excessive Data Transforms is less reusable and organized.

A common pitfall is a mismatch between the process state used for pausing (e.g., a specific stage like .pyStatusWork) and the condition defined in the SLA. If the SLA is set to pause when `.pyStatusWork == “On-Hold”` but the process sets a custom property like `.Status = “Hold”`, the SLA will not recognize the pause state and will continue counting down.

While Pega can technically support many stages, the primary risk is usability. A case life cycle with 20+ stages becomes visually cluttered and cognitively overwhelming for users. It often indicates an over-decomposition of the process. Best practice is to group related steps into broader, meaningful stages to create a clear and intuitive user experience.

For real-time reporting on specific, frequently queried criteria (like Priority and Resolution), a Declare Index rule is the optimal Pega solution. It creates a purpose-built database index that dramatically improves query performance for reports and data pages filtering on those properties, ensuring the dashboard loads quickly without impacting transactional case processing.

The Pega Approval shape allows for dynamic voting schemes. You can configure it to use a custom scheme where a pre-processing activity or a decision rule evaluates the request amount and sets the `pyRequiredVotes` property (e.g., to 1 or 2). This keeps the process simple, contained within a single shape, and easily maintainable.

Using a Correspondence rule triggered by a When rule (e.g., `!IsCanceled`) is the most robust and design-pattern-aligned solution. It separates the notification logic from the process flow, centralizes email templates, and ensures the business condition (case not being canceled) is evaluated at the exact moment the email is generated, not when the case passes through a shape in the flow.

Pega’s Declarative Rules (Data Transforms for data manipulation, Decisions for business logic) are the cornerstone of a modern, maintainable application. They are more reusable, easier to test, and better integrated with the Pega engine (e.g., for refresh logic, dependency tracking) than procedural activities. Exposing results through data pages provides a clean, cached interface for other rules to consume.

If the data page’s key includes dynamic parameters that change between requests (like `param.CaseID` or `param.OperatorID`), Pega treats each unique key combination as a separate cache entry. For `D_CustomerDetails`, the key should be based on the stable customer identifier (e.g., `param.CustomerID`) so that requests for the same customer reuse the cached page, regardless of the case context.

A Data Page (List) is the declarative, low-code method for sourcing list data. Configuring its refresh interval (e.g., in the `editLoadManagement` settings) allows the Pega engine to automatically refresh the data in the background. This updated data page can then be the source for a Table or Grid widget in a Pega portal, providing the requested auto-updating functionality with minimal custom code.

Separating concerns by using a Declare Expression for time-based calculations and a Decision Table for business logic (role, weekend) promotes reusability, clarity, and easier maintenance. A single complex When rule becomes difficult to debug and update, while embedding logic in a Flow Action or excessive Data Transforms is less reusable and organized.0