Agentic AI Application Architecture

4.4 Defining the Graph

1 min

How the graph is defined

The graph is built explicitly in build_workflow():

python 
def build_workflow() -> StateGraph:
    graph = StateGraph(PlannerState)
    graph.add_node("coordinator", lambda state: coordinator_node(state))
    graph.add_node("flight_specialist", lambda state: flight_specialist_node(state))
    graph.add_node("hotel_specialist", lambda state: hotel_specialist_node(state))
    graph.add_node("activity_specialist", lambda state: activity_specialist_node(state))
    graph.add_node("plan_synthesizer", lambda state: plan_synthesizer_node(state))
    graph.add_conditional_edges(START, should_continue)
    graph.add_conditional_edges("coordinator", should_continue)
    graph.add_conditional_edges("flight_specialist", should_continue)
    graph.add_conditional_edges("hotel_specialist", should_continue)
    graph.add_conditional_edges("activity_specialist", should_continue)
    graph.add_conditional_edges("plan_synthesizer", should_continue)
    return graph

And the routing logic is here:

python 
def should_continue(state: PlannerState) -> str:
    mapping = {
    "start": "coordinator",
    "flight_specialist": "flight_specialist",
    "hotel_specialist": "hotel_specialist",
    "activity_specialist": "activity_specialist",
    "plan_synthesizer": "plan_synthesizer",
    }
    return mapping.get(state["current_agent"], END)

Even though this uses conditional edges, the workflow is effectively linear:

Knowledge Check

If the workflow is effectively linear, why does the graph still use add_conditional_edges and the should_continue() router?

Click here to see the answer
Because it makes the workflow flexible and extensible. Even though the current flow is linear, the routing function allows the graph to dynamically decide the next node based on the state. This makes it easy to add branching, retries, or different execution paths later without redesigning the graph.
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