Define what the system may propose
Write down the assistance you want: classify a request, extract fields or draft a reply. Then list the actions that could follow and their consequences. A useful draft should show the input and relevant sources so the reviewer can check it. Generating text and authorizing its use are different steps.
Keep fixed business rules outside the generated response. Required fields, allowed recipients, account permissions and approval limits should be validated by the workflow. A model’s confidence or persuasive wording is not an adequate basis for access, financial commitments or sensitive decisions.
Make approval an explicit step
Name the reviewer, the information they see and the choices available: approve, edit, reject or request more information. Define how long a pending item can wait and who handles it if the reviewer is unavailable. A review queue should show status clearly so silence is never treated as approval.
Require approval of the actual content and destination that will be applied. If either changes, the previous approval may no longer be valid. Record the decision and distinguish the approved item from later revisions. For a first pilot, keeping the final action manual can make the boundary easier to inspect.
Test rejection, misuse and recovery
Include examples with missing facts, incorrect recipients and text that asks the system to ignore its instructions. Treat such text as untrusted input. Check that a rejected draft cannot reach the action step and that users cannot approve records outside their role. These tests matter as much as a correct draft.
Define a pause control and an owner for investigating unexpected behavior. Keep enough operational information to identify what happened without collecting unnecessary private content. Agree on when human review remains mandatory. This approach helps control a scoped workflow; it does not make AI suitable for medical, legal or financial advice.
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