# Workflow for routing
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In the routing pattern, a classifier or router agent uses an LLM to interpret the intent or category of a query, then routes the input to a specialized downstream task or agent.

![\[Workflow for routing.\]](http://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/images/workflow-for-routing.png)


The Routing workflow is used in scenarios where an agent must quickly classify input intent, task type, or domain, and then delegate the request to a specialized subagent, tool, or workflow. It is especially useful in capability agents, such as those that serve as general assistants, front doors to enterprise functions, or user-facing AI interfaces that span domains.

Routing is particularly effective when:
+ Triaging requests across a variety of tasks (for example, search, summarization, booking, calculations).
+ Inputs must be preprocessed or normalized before entering more specialized workflows.
+ Different input types (for example, images vs. text, structured vs. unstructured queries) require custom handling.
+ An agent is acting as a conversational switchboard, delegating tasks to specialized agents or microservices.
+ This workflow is common in domain-specific copilots, customer-support bots, enterprise service routers, and multimodal agents, where intelligent dispatching determines both the quality and efficiency of agent behavior.

## Capabilities
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+ A first-pass LLM acts as a dispatcher
+ Routes can invoke distinct workflows or even other agent patterns
+ Supports modular expansion of capabilities

## Common use cases
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+ Multidomain assistants ("is this a legal, medical, or financial question?")
+ Decision trees enhanced with LLM reasoning
+ Dynamic tool selection (for example, search vs. code generation)