Agentic AI Application Architecture

4.8 Decomposition Pattern

1 min

The synthesizer shows the decomposition pattern

The final node combines the specialist outputs into one answer.

python 
def plan_synthesizer_node(state: PlannerState) -> PlannerState:
    llm = _create_llm(
    "plan_synthesizer", temperature=0.3, session_id=state["session_id"]
    )

    content = json.dumps(
        {
            "flight": state["flight_summary"],
            "hotel": state["hotel_summary"],
            "activities": state["activities_summary"],
        },
        indent=2,
    )

    response = llm.invoke(
        [
            SystemMessage(
                content="You are the travel plan synthesiser. Combine the specialist insights into a concise, structured itinerary."
            ),
            HumanMessage(
                content=(
                    f"Traveller request: {state['user_request']}\n\n"
                    f"Origin: {state['origin']} | Destination: {state['destination']}\n"
                    f"Dates: {state['departure']} to {state['return_date']}\n\n"
                    f"Specialist summaries:\n{content}"
                )
            ),
        ]
    )
    state["final_itinerary"] = response.content
    state["messages"].append(response)
    state["current_agent"] = "completed"
    return state

This is a classic pattern for agentic apps:

That is one of the main architectural ideas you should take away from this overview.

Knowledge Check

Why does the app use a separate plan_synthesizer node instead of letting one agent generate the entire travel plan?

Click here to see the answer

Because the system breaks the problem into specialized tasks first (flights, hotels, activities). Each specialist produces a focused summary, and the plan_synthesizer node then combines those outputs into one coherent itinerary.

This pattern improves modularity, reliability, and observability, since each agent handles a smaller problem and the final node integrates the results.

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