Traditional support QA means a supervisor samples a percentage of tickets or calls and scores them against a rubric: was the information accurate, was the tone appropriate, was policy followed. The scores feed back into coaching and process fixes.

For an AI agent, the same discipline applies, but at a different scale: since every interaction is logged, QA can review 100% of conversations against the rubric rather than a small sample, catching a systematic error (a wrong policy being cited repeatedly) far faster than sample-based human QA ever could.

Frequently Asked Questions

What does a QA rubric for an AI agent typically check?

Factual accuracy against source data, adherence to company policy, appropriate escalation (did it correctly recognize when a case needed a human), and tone, whether the response was clear and appropriately empathetic for the situation.

Can QA for an AI agent be automated too?

Partially. Automated scoring can flag likely policy violations or factual mismatches at scale, but human review of a sample remains important for catching subtler quality problems that a rules-based or model-based scorer might miss.

How is this different from evals?

Evals typically run against a defined test set before or during development, to measure whether a system meets a bar. Customer service QA reviews real, live interactions after the fact, catching issues that show up in production regardless of how the system performed on pre-built test cases.

What happens when QA finds a recurring problem with an AI agent's behavior?

The finding should feed back into the agent's operating rules, correcting the specific pattern so it doesn't recur, the same self-learning loop that improves the agent over time rather than a one-off fix applied outside the system.