LangGraph for Agentic Workflows

LangGraph is designed for stateful, graph-based agent workflows. Instead of a single linear chain, a workflow is represented as nodes and edges. Each node can perform a task, update state, and decide where the workflow should go next.

Real enterprise workflows are rarely linear. They involve branching, retries, approvals, validation failures, and multiple specialized agents. LangGraph is useful when an AI system needs memory, decision routing, and controlled agent collaboration.

def classifier(state):
    state["document_type"] = "clinical_note"
    return state

def validator(state):
    required = ["patient_id", "diagnosis"]
    state["missing"] = [f for f in required if not state.get(f)]
    return state

def route_after_validation(state):
    return "human_review" if state["missing"] else "policy_review"

state = {"patient_id": "P100", "diagnosis": None}
state = classifier(state)
state = validator(state)
print(route_after_validation(state), state)