Conditional Routing Patterns
Explore how to implement conditional routing patterns in AI workflows by separating classification and routing responsibilities. Understand intent labeling, confidence checks, and fallback mechanisms to build scalable and maintainable LangGraph agents that handle multiple branching paths safely and efficiently.
A single conditional branch is easy to manage. The moment a workflow needs four or five branches, the routing logic becomes the most fragile part of the graph.
Consider what tends to happen as routes are added over time. What starts as a clean two-way branch grows into a long chain of if/elif statements inside the routing function. The classifier node starts making decisions it should leave to the router. Node names and intent labels get mixed together in the same place, so renaming a node requires finding and updating every string that references it. There is no obvious place to add a safe default when none of the known routes match.
None of these problems are inevitable. They come from not having a clear pattern for routing from the start. This lesson introduces that pattern: a classifier node that writes a label, a routing function that translates labels to node names using a mapping dictionary, and a fallback path that handles anything the classifier is not confident about.
The two-part routing model
Routing in LangGraph happens in two distinct places, and keeping them separate is the foundation of everything in this lesson.
The classifier node is responsible for deciding what kind of input this is. It reads the message, applies whatever logic is appropriate (keyword matching, model inference, rule checking), and writes a human-readable intent label into state. It does not know or care what node will handle this request.
The routing function is responsible for translating that label into a node name. It reads the intent label from state and returns the exact string name of the next node to execute. It does not do any intent analysis.
The table below shows what each part does and what it must not do.
Part | Writes to state | Returns | Must not do |
Classifier node |
| A partial state update dict | Return a node name |
Routing function | Nothing | A node name string | Write to state |
Keeping these responsibilities separate gives us three things. First, the intent label is stored in state, which means it is available for inspection, logging, and future use by other nodes. Second, if we rename a handler node, we only update the routing function’s mapping dictionary. Third, the classifier can be swapped from keyword-based to model-based without touching the routing function at all.
Route label design
Intent labels are the strings written by the classifier node into the intent field. They should be short, lowercase, and describe what the user needs, not which node handles it. A label like "faq" describes the user’s intent. A label like "handle_faq" describes a function. When labels encode node names, the classifier becomes coupled to the graph structure. If we later split handle_faq into two separate nodes, we would need to update both the classifier and the routing function.
Good labels: "faq", "billing", "escalation", "fallback". These describe what the user is asking for. Routing functions translate these into node names.
What we are building
The workflow in this lesson handles four categories of customer support ...