Artificial intelligence
AI agents: evaluate quality before automating customer service
An agent that succeeds in a demo may fail on real requests. An evaluation process makes those gaps visible.
Google described an evaluation loop in Agents CLI for running cases, comparing results and detecting agent regressions.
The KIMY perspective
In customer service, a fluent answer can still be wrong or trigger the wrong action. We recommend defining unacceptable errors first: inventing availability, disclosing private information or confirming an operation that never happened. Then build representative scenarios, including ambiguous and multilingual requests. Track quality alongside cost. Important decisions should retain human validation, and difficult cases need a clear handover route. An evaluation score is a decision aid, not permission to remove all supervision from a business process.
What to check
- Build tests from anonymized business situations.
- Retest after changes to the model or instructions.
- Provide a clear escalation path to a person.