How to configure fail-safe human-in-the-loop triggers for transaction bots

"Require human approval before large transactions" is easy to say and easy to get wrong in practice — if the check is just a prompt instruction, there's no code-level guarantee the agent actually stops. LangGraph's interrupt() makes the pause a real, structural part of the graph: the run genuinely halts, state is saved, and nothing proceeds until a human's decision comes back in.

Implementing the safeguard

1. Identify the actual irreversible action. Not "the agent decided to transfer money" — the specific line of code that calls the transfer API. That's where the interrupt belongs.

2. Call interrupt() right before that action, passing the details a human needs to decide. The value you pass is what surfaces to whoever is reviewing — include the amount, recipient, and anything else relevant to the decision.

3. Resume execution with the human's decision via Command. Only after that resume does the node continue — and since LangGraph restarts the node from its beginning, keep any pre-interrupt logic idempotent.

# LangGraph node that pauses for human approval before a real transfer
from langgraph.types import interrupt, Command

def process_transaction(state):
    amount = state["amount"]

    if amount > 1000:
        # Execution genuinely pauses here until a human responds
        decision = interrupt({
            "question": "Approve this transfer?",
            "amount": amount,
            "recipient": state["recipient"],
        })
        if not decision.get("approved"):
            return {"status": "rejected"}

    # Only reached after approval (or for amounts under the threshold)
    execute_transfer(amount, state["recipient"])
    return {"status": "completed"}

# Resuming after a human decision comes back:
# graph.invoke(Command(resume={"approved": True}), config=thread_config)

The reason this matters for "fail-safe" specifically: a prompt-level instruction is a request the model usually follows; a code-level interrupt() is a checkpoint the graph runtime enforces regardless of what the model generates.