Production QA checklist
Build a QA loop that leads from a bad call to a verified fix.
Use production conversations, consistent evaluation criteria, evidence-led review, and follow-up checks to improve a voice agent without relying on random call sampling.
Before you evaluate
- Define the customer outcome for each supported workflow.
- Choose representative calls, including failures and edge cases.
- Confirm provider permissions, retention, and evaluator data-handling choices.
- Document what pass, fail, and uncertain mean for the scorecard.
While you review
- Use scores to prioritize calls, then inspect transcript and available audio together when timing matters.
- Require evaluator rationales to point to specific conversation evidence.
- Escalate uncertain, sensitive, or consequential outcomes to a human reviewer.
- Separate agent behavior from provider or infrastructure failures.
After an agent change
- Group recurring failures by likely root cause and assign corrective work.
- Retest against relevant historical examples and new production calls.
- Compare quality signals over time, not only operational activity.
- Keep the calls behind a regression visible to the people making the next change.
Build with VaaniEval
Make evaluation coverage part of how your agent ships.
We are working with production Voice AI teams that need a quality system to keep pace with releases and real call behavior.