Customer support staff review an AI agent's decisions against two things: the source an answer should come from, and the customer it was meant for. That means reading the record, finding the deciding bound, confirming the answer or action against the knowledge base or order system, and deciding with a short reason.
What does a support person review?
Support staff are the first line of review for an AI agent that answers customers. Three kinds of decision cross their desk:
- Held items: refunds, account changes and sensitive replies waiting for a person.
- Handovers: conversations the AI agent passed to a person, which show where it reached its limits.
- A sample of resolved conversations: contacts the AI agent closed on its own, reviewed for accuracy and tone.
Most support teams already score human conversations for quality. Reviewing the AI agent's conversations is the same habit with one advantage: the record shows the sources and the bound behind each decision, so the reviewer can see why, not just what.
What are the steps?
- Open the record. Read the verdict and the deciding bound. Allowed, held, refused, or withheld at the platform floor.
- Read the customer's request in their own words, not the AI agent's summary.
- Check the answer's source. A policy answer should come from the current knowledge base article; an order answer should come from the order system. If the AI agent answered from something the customer said, that is a declared fact and the answer may be wrong.
- Check the action. If it issued a refund or changed something, was that within its limits and correct for this customer?
- Check the tone and completeness. Did it answer everything? Would the customer feel heard?
- Decide. Approve, edit or decline open items; mark resolved ones as correct or not.
- Record a reason and tag any knowledge gap.
What may the AI agent do, and what stays with a person?
| Work | AI agent | Support staff |
|---|---|---|
| Answer from the knowledge base | Depth 3 | Samples |
| Check order and delivery status | Depth 1 | Relies on it |
| Issue standard refunds within limits | Depth 3 | Samples |
| Change contact details for an authenticated customer | Depth 3 | Samples |
| Larger refunds, exceptions, unauthenticated changes | Drafts, held | Decides |
| Complaints and sensitive conversations | Drafts | Decides, often by calling |
An example: a stale knowledge article
Take an online retailer's support team. During the daily sample, a support agent notices the AI agent told several customers that returns are free for 60 days. The record shows it cited a knowledge base article. The support agent checks: the returns policy changed to 30 days for sale items, but the article was only partly updated, and the sale-item exception sat in a different article the AI agent did not retrieve.
The answers were faithful to the source the AI agent used and wrong for the customer. The support agent edits the pending replies, records the reason, and tags the article for the knowledge owner. The team lead checks how many customers received the wrong answer and arranges follow-up. Market detail is in AI agent training for customer support agents in e-commerce in Australia.
Which controls make support review useful?
- Every support person reviews a few resolved conversations each day, not only held items.
- Knowledge gaps are tagged and routed to an owner.
- Team leads sample support staff's approvals.
- Approval rights on held refunds depend on current training.
Team leads review at a different level; see how support team leads review an AI agent's decisions. Training is covered in how to train the support team to review AI agent decisions and, for managers, how to train the support leadership. The role map is in AI agent skills by role.
Where the Academy fits
Pryme Intelligence Academy's Operator track trains the people who review agent work daily, covering human review, teaching the agent and keeping knowledge current. Module tests are set in the learner's own industry and role. Lessons can be read or listened to as audio, which suits support staff fitting learning around a shift pattern.
Questions people ask
How many resolved conversations should each person review?
A small fixed number per shift works better than a large weekly batch. It keeps review a habit and spreads attention across the week.
What should support staff do if the AI agent was right but the customer is unhappy?
Treat it as a customer service problem. Explain the decision, and if the policy itself is the issue, log it for the policy owner.
Can support staff correct the knowledge base themselves?
Only if their role allows it. Usually they tag the gap, and the knowledge owner changes the article through change control.