Hierarchical Planning & Task Decomposition

While basic ReAct loops excel at short, reactive workflows (1 to 5 turns), they degrade rapidly on long-horizon objectives because error compounding and attention drift cause the agent to wander or get stuck in repetitive dead ends. Enter the Plan-and-Solve architecture.

from typing import TypedDict, List
from langgraph.graph import StateGraph, END

class AgentPlanState(TypedDict):
    objective: str
    plan: List[str]
    current_step: int
    results: List[str]

def planner_node(state: AgentPlanState):
    # Generates structured checklist from user objective
    subtasks = [
        "Clone repository & verify branch",
        "Execute automated test suite",
        "Extract stacktrace from failing unit test",
        "Generate patch and verify fix"
    ]
    return {"plan": subtasks, "current_step": 0}

def executor_node(state: AgentPlanState):
    idx = state["current_step"]
    step_desc = state["plan"][idx]
    # Execute tool associated with step_desc...
    return {"results": state["results"] + [f"Completed: {step_desc}"], "current_step": idx + 1}

def router_condition(state: AgentPlanState):
    if state["current_step"] >= len(state["plan"]):
        return END
    return "executor"