Building Stateful AI Agents with LangGraph: A Support Case Study

While LangChain is excellent for linear chains, real-world agentic workflows are often cyclical and stateful. This case study explores how we built a “Refund & Dispute” agent that requires high reliability, persistence, and human oversight.

The Challenge: The “Infinite Loop” and Persistence

Our previous agent often got stuck in loops or lost context if the user took too long to respond.

• The Workflow: Identify intent -> Check Database -> Calculate Refund -> Wait for Manager Approval -> Execute Refund.
• The Problem: Traditional chains couldn’t “pause” and wait for a human, nor could they easily recover from a database error without starting over.

The Solution: LangGraph State Machines

We modeled the agent as a State Graph.

1. The State Schema

We defined a strict AgentState that tracks the user query, database results, and the approval status.

2. Cyclical Correction

If the “Database Check” node fails, the graph routes back to the “Input Analysis” node with the error message, allowing the agent to refine its query.

3. Human-in-the-loop (Checkpoints)

Using LangGraph’s Checkpointer, we could “interrupt” the graph execution at the approval node. The state is saved to a database, and the execution only resumes when a human UI trigger is received.

Implementation Example (Python)

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

class AgentState(TypedDict):
    query: str
    refund_amount: float
    is_approved: bool

def process_refund(state: AgentState):
    # Logic to calculate refund
    return {"refund_amount": 50.0}

def human_approval(state: AgentState):
    # This node is a placeholder for the UI interrupt
    pass

# Define the Graph
workflow = StateGraph(AgentState)

workflow.add_node("calculate", process_refund)
workflow.add_node("approve", human_approval)

workflow.set_entry_point("calculate")
workflow.add_edge("calculate", "approve")

# Interrupt before approval
app = workflow.compile(interrupt_before=["approve"])

# Kickoff
thread = {"configurable": {"thread_id": "user_123"}}
app.invoke({"query": "I want a refund for order #1"}, thread)

Results & Impact

Conclusion

LangGraph transformed our AI from a “chatbot” into a “reliable workflow engine.” By treating the agent as a state machine, we achieved the level of control and durability required for financial transactions.