An AI-assisted workflow often combines several kinds of work. It reads a request, finds information, drafts an answer and may take an action in another system. Treating all of those steps as one permission decision makes review difficult. The useful question is where an action creates a consequence that a person should understand and authorize. This guide uses a fictional support-to-refund workflow to make that decision concrete.
The example is deliberately small. A customer asks for a refund after reporting a duplicate order. An assistant can summarize the request and gather order details. A support agent reviews the proposed response and any refund action. The exercise focuses on the decision boundary. The team still needs its own refund policy.
Write down the actions before choosing the automation
Start with the actual sequence of work. In this example, the system receives a message, identifies the relevant order, checks the available records and prepares a recommendation. It may then draft a customer response, create a refund request and send a final message. List each action separately. Reading a record, composing text and moving money create different obligations for the team responsible.
For each step, ask what changes outside the model's response. A draft held inside the support workspace can be edited. A message sent to a customer changes what they have been told. A refund changes a financial record. A deleted attachment may be difficult to recover. This inventory helps the team place review at meaningful points instead of putting a single approval button at the end of an unclear process.
Separate permission from confidence
A model may appear confident about the requested outcome while lacking authority to carry it out. The customer could have supplied an incomplete order number, or the policy could require a supervisor for that case. The workflow should determine permission using the organization's rules and verified application state. Do not treat a fluent explanation as evidence that the action is authorized.
Define the permitted actions for each role. A support agent might approve a standard draft but need a supervisor for an unusual adjustment. An administrator might change the workflow configuration without handling individual cases. Those distinctions should be visible in the product and enforced by the application. A role label on a screen is insufficient if the execution endpoint accepts an unauthorized request.
Put the first review before the consequential action
For the fictional refund case, the assistant can gather information and prepare a proposed action. The reviewer should see the customer request, relevant order details, the policy reference used and the exact proposed refund. If the record is ambiguous, the next action may be a clarification request rather than a refund. Give the person enough context to make that choice without reconstructing the entire conversation.
Keep the approval tied to the proposal. If the amount, recipient or order changes after review, require a new decision. Otherwise the system may execute something different from what the person believed they approved. A useful interface makes the approved values easy to compare with the execution result. The fields will depend on the customer's systems and policy.
Use a real pause with a defined resume path
A review step needs a durable state: awaiting approval, rejected, clarification requested or ready to execute. The workflow should not simply continue because a notification was sent. Decide how long requests remain open, who can find them and what happens when the assigned reviewer is absent. An unanswered approval request is an operating condition that needs an owner.
LangGraph's interrupt documentation shows a mechanism for pausing execution and resuming with external input. Such a mechanism can support human review, but it does not decide the organization's approval rules. The team still needs to specify reviewer access, the evidence presented and the effect of a changed proposal. Test those decisions through the actual interface people will use.
Design rejection as a normal outcome
Reviewers should be able to reject a proposal without writing a long explanation every time. A short reason such as incorrect order, policy exception or insufficient evidence can help route the case. Add room for a note where the reason needs detail. The assistant may prepare a revised proposal, but the revision should return to review rather than inheriting the previous approval.
A rejection also offers useful product evidence. If many proposals fail because the order context is missing, improve retrieval or intake before adjusting the wording of the answer. If reviewers repeatedly disagree about policy, the team may need a clearer policy owner. Approval data should help locate the underlying problem rather than becoming a score that pressures people to accept more drafts.
Keep execution results distinct from approvals
An approved action can still fail. A refund service may be unavailable, the record may have changed or a required permission may have expired. Show the actual execution result and preserve the request identity. Avoid a success message based only on the person's click. The reviewer needs to know whether the action occurred and what to do when it did not.
Retries deserve explicit design. Before repeating a consequential action, the application should establish whether the first attempt completed. Use the target system's supported duplicate-prevention mechanisms where available and test the behavior. If the outcome is uncertain, send the case to an operator with enough evidence to investigate. A second attempt made blindly can turn a temporary error into a more serious problem.
Review the customer message separately where needed
A correct internal action does not guarantee a correct explanation to the customer. The final message may name the wrong amount, imply an unavailable timeline or omit a next step. Decide whether the same approval covers the exact message as well as the action. If the message is generated after execution, consider a review rule for discrepancies or unusual outcomes.
The customer should receive a clear description of what happened and what remains to be done. Avoid exposing internal model reasoning or irrelevant system details. Keep the communication grounded in the verified result. This gives support staff an intelligible record to use if the customer replies later and prevents a polished draft from becoming the only account of the event.
Test the boundary with difficult cases
Create examples involving an ambiguous order, conflicting records, an unavailable tool and a proposal changed after approval. Include a reviewer rejecting the action, a reviewer leaving the page and an execution timeout. Check the visible state, the server record and the next assigned person. A top-to-bottom successful demonstration covers only a small part of the workflow.
The NIST AI Risk Management Framework is a broader resource for thinking about responsibilities around AI systems. For this specific exercise, keep a short decision record naming the action, its consequence, the approval owner and the evidence required. Revisit that record when the workflow gains a new tool or begins serving a new audience.
A practical starting exercise
Take one existing workflow and mark every action that sends, spends, deletes or changes access. For each, write who can authorize it, what they must see and how the system will confirm completion. Then test one rejected case and one uncertain execution result. That exercise can reveal more about readiness than adding another demonstration of the happy path. The goal is an approval step that people can understand and an execution record they can trust.