When AI helps with business work, someone often needs to review the result before it moves forward. That review deserves as much design attention as the generated output. A useful approval step tells the reviewer what happened, what information was used and what action will follow. It also provides a straightforward way to correct, reject or pause the work.
Decide where review belongs
Look at the actions in the workflow and consider the effort required to reverse a mistake. Preparing a draft and sending it are different steps. Updating an internal note and changing a customer-facing record may need different checks. Define the review points before connecting the workflow to live tools. Keep the first version focused enough that its permissions and responsibilities are easy to understand.
Show the reviewer useful context
Place the proposed action beside the information needed to evaluate it. This could include the original request, relevant source material and a summary of the changes. Highlight missing information instead of hiding it inside a confident response. Reviewers should be able to inspect supporting material and edit the output without recreating the entire task. Clear context makes the approval decision more deliberate.
Start with a clear purpose. Build something people can actually use.
Define the paths beyond approval
A workflow needs more than accept and continue. Decide what happens if a reviewer rejects a draft, asks for a revision or does not respond. Provide a route for questions that fall outside the system's scope. Make ownership explicit so incomplete work does not disappear between tools. Keep a useful record of decisions and changes, with access appropriate to the information involved.
Test the review experience
Use ordinary cases alongside incomplete, contradictory and unexpected requests. Ask reviewers to explain how they made each decision. If they cannot find the evidence or do not understand the next action, improve the interface and workflow boundary. Review is an ongoing part of operating the system, so include it in the scope and expected effort. At IndusAGI Labs, we help define these handoffs when shaping AI-agent and automation projects.
