Routing design patterns
Routing is a critical configuration element in any automated process. Routing determines the journey that a Task takes from creation to completion. While routing is often perceived as simply assigning a Task to a person, modern routing is significantly more sophisticated. It orchestrates handoffs between individuals, systems, AI agents, and third-party applications, forming the foundation of intelligent and efficient automation.
Case Management and Flow orchestration features in Pega Infinity™ facilitate seamless routing by using Rules, Decision strategies, and AI-driven decisioning to manage complex, high-volume operations.
Selecting the appropriate routing strategy is essential. A poorly designed routing model can result in bottlenecks, rework, and fragile processes that are difficult to maintain. This area is where design patterns are valuable.
A design pattern is a reusable, proven solution to a commonly occurring problem within a specific context. In workflow automation, routing design patterns offer a shared vocabulary and a set of expert-approved blueprints. They help architects and developers build processes that are efficient, scalable, flexible, and easier to understand. By mastering these patterns, you can move beyond simple, linear workflows and design dynamic and intelligent solutions.
Routing design patterns:
- Linear routing
- State-based routing
- Orchestrator routing
- Deferred choice routing
- Load balancer routing
- Recipient list routing
Linear routing
A straightforward, sequential handoff in which a workflow progresses through a predefined series of Steps (A → B → C) without branching. Each Step must be completed in strict sequence before the next begins. This model is suited for predictable, repeatable work with minimal variation. The next user cannot act on the task until the previous Step is completed.
In Pega Platform™, this structure represents the default Case path, implemented as a simple flow with Service-Level Agreements (SLAs) for goals and deadlines, and minimal deviations from the main implementation path.
State-based routing (Content-Based Router or Evaluator routing)
The Case state or a governing attribute, such as a region or Case Lifecycle status, determines how work is routed. The current Stage and the triggering Event determine the next destination. A Case can move forward, backward, or transition to a different Stage based on Decisions or triggering events.
In Pega Platform, teams commonly implement this approach by using a Decision table or Decision tree to evaluate a Case Field (for example, Country or State) and route the Task to a specific User or Work Queue. You can also use the default routing utilities to route Tasks based on attributes such as skill.
Orchestrator routing
A central orchestrator determines the next Step and coordinates work across services, teams, or applications. This pattern works well for complex, cross-system journeys that require end-to-end visibility and governed sequencing. The orchestrator analyzes a complex goal, breaks it into smaller, independent sub-tasks, and routes them to the appropriate workers, including people, bots, or systems. It then waits for these Tasks, often running in parallel, to complete before moving forward.
In Pega Platform, teams commonly implement this pattern by using a split-join Process Flow. This approach runs multiple Tasks in parallel and confirms their completion before the main Process continues.
Deferred choice routing (pull-based routing)
The system presents a set of available Tasks for users to select what to work on next. Users defer the decision about which Task to perform until they pull an item from the pool. This approach empowers users to manage their workload based on priority, expertise, or availability.
In Pega Platform, you can implement deferred choice by routing Tasks to a Work Queue. Users then select Tasks based on their preferences. Typically, the system pulls the Task by using the Get Next Work algorithm, which teams can customize to reflect business priorities and operational logic.
Load balancer routing
This pattern distributes Tasks across multiple resources to balance workload and prevent bottlenecks. It improves performance and avoids resource overuse.
In Pega Platform, routing utilities distribute Tasks by evaluating User skills and current workload before assigning work. This approach promotes efficient resource use and consistent throughput across teams.
Recipient list routing
The recipient list pattern dynamically builds a list of recipients for a Task and routes it to all specified destinations. The list can remain static or be generated at run time based on conditions.
In Pega Platform, teams commonly implement this pattern in email Channels by using natural language processing to triage incoming messages. Based on the email context, the system routes the Task to the appropriate User or Work Queue.
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