Agentic AI Application Architecture

4.1 Request Lifecycle

1 min

What the application does

At a high level, the application accepts a request and turns it into a multi-step workflow:

The main flow looks like this:

python 
@app.route("/travel/plan", methods=["POST"])
def plan():
    data = request.get_json()

    origin = data.get("origin", "Seattle")
    destination = data.get("destination", "Paris")
    user_request = data.get(
        "user_request",
        f"Planning a week-long trip from {origin} to {destination}. "
        "Looking for boutique hotel, flights and unique experiences.",
    )
    travellers = int(data.get("travellers", 2))

    result = plan_travel_internal(
        origin=origin,
        destination=destination,
        user_request=user_request,
        travellers=travellers
    )

    return jsonify(result), 200

A helpful way to explain this is:

  1. Flask receives the request
  2. plan_travel_internal() builds the workflow state
  3. LangGraph executes the nodes
  4. each node updates the state
  5. the final itinerary is returned as JSON

Knowledge Check

Where does the LangGraph workflow actually start executing in this API flow?

Click here to see the answer

It starts inside plan_travel_internal(). The Flask route only receives the request and extracts parameters. plan_travel_internal() initializes the workflow state and invokes the LangGraph graph, which then runs the nodes (coordinator, specialists, synthesizer) that update the state until the final itinerary is produced.

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