QA Scorecards for Custom Support Agents
QA Scorecards for Custom Support Agents
Section titled “QA Scorecards for Custom Support Agents”Custom support agents do not fail the same way ordinary chatbots fail. They can sound fluent while still violating policy, misrouting edge cases, or quietly increasing human review burden.
That is why support QA needs its own page. Teams often build custom support agents because raw model cost looks cheaper than a support platform. Then they discover the real cost moved into human review, exception handling, and policy drift.
Quick scorecard rule
Section titled “Quick scorecard rule”A useful support-agent QA scorecard must measure more than answer correctness. It must also measure escalation quality, source use, policy adherence, and the amount of human cleanup left behind after the answer.
If the scorecard only measures whether the answer “looked helpful,” the team is not measuring the real operating cost.
What a support QA scorecard should include
Section titled “What a support QA scorecard should include”At minimum:
- Answer correctness
- Source quality and citation quality
- Policy adherence
- Escalation correctness
- Customer-harm risk
- Reviewer effort
Those last two are what keep the scorecard commercially useful. They turn quality into a cost and trust model, not just an annotation exercise.
The categories that usually matter most
Section titled “The categories that usually matter most”Most support teams should score:
- did the answer use approved knowledge;
- did it avoid unsupported promises;
- did it escalate when policy or confidence required escalation;
- did it create recontact or downstream cleanup;
- how long did the reviewer need to verify it.
That gives leadership a more honest view than pure resolution rate.
How much human review is enough
Section titled “How much human review is enough”The answer depends on queue risk.
- Low-risk repetitive queues can move toward sample-based review.
- Medium-risk policy queues usually need heavier targeted review.
- High-risk billing, account, or regulated queues often need persistent review until failure patterns are well understood.
The goal is not zero review. The goal is review proportional to consequence.
Why this matters commercially
Section titled “Why this matters commercially”This is where build-versus-buy economics usually flips.
A custom support agent can look inexpensive on model cost and still be expensive if:
- reviewers are overloaded,
- escalations are poor,
- customers recontact frequently,
- or QA cannot spot drift early.
That is why QA scorecards are not just a quality tool. They are an economics control.
A practical launch sequence
Section titled “A practical launch sequence”- Define score categories before launch.
- Review heavily on one queue for two to four weeks.
- Track reviewer minutes and recontact alongside pass rates.
- Reduce review only where failure modes are stable and low-consequence.
If reviewer burden stays high, the custom stack may still be less efficient than a commercial support platform.