Automatización de Operaciones Internas

from langgraph.graph import StateGraph
from langchain_anthropic import ChatAnthropic

# Define order processing workflow
workflow = StateGraph()

workflow.add_node("fetch_order", fetch_order_details)
workflow.add_node("check_stock", verify_stock_availability)
workflow.add_node("check_customer", get_customer_history)
workflow.add_node("evaluate_risk", assess_fraud_risk)
workflow.add_node("decide", make_approval_decision)
workflow.add_node("auto_approve", process_and_ship)
workflow.add_node("manual_review", escalate_to_ops)

# Decision logic with multiple criteria
def should_approve(state):
    if state["risk_score"] < 30 and state["stock_ok"]:
        return "auto_approve"
    elif state["risk_score"] > 70:
        return "manual_review"
    else:
        # Ask LLM for nuanced decision
        decision = llm.decide(context=state)
        return decision

workflow.add_conditional_edges(
    "decide",
    should_approve,
    {
        "auto_approve": "auto_approve",
        "manual_review": "manual_review"
    }
)

# Execute
app = workflow.compile()
result = app.invoke({"order_id": order.id})