Short answer
AI quality assurance is the practice of verifying that an AI agent's responses meet a defined standard before and after deployment, through testing, confidence thresholds, audit trails, and human review. It is QA applied to a system that generates language, where the failure mode is not a crash but a confident, fluent, wrong answer.Traditional software QA tests deterministic behavior: given an input, the output is fixed and checkable. AI QA is harder because the same question can yield different answers, and a plausible answer can still be incorrect. So AI QA leans on real cases, probabilistic confidence, and traceability rather than pass-fail unit tests alone.
A sound program covers the full lifecycle. Before deploy: test the agent against real historical conversations and gate it on results. After deploy: monitor confidence, keep a complete audit trail, and route low-certainty cases to a person. In Aide, the AI platform for customer service, this is operationalized intent by intent. The Agent Simulator handles the before, a complete record of every automated action handles the after, and automation only deploys where it has been verified for that intent.
The standard is simple: automation that has not passed its test gate does not ship. Verify first, not deploy-now-and-fix-later. Because every review stays visible and shared, the team's understanding of its customers grows through QA rather than atrophying behind an opaque system.