Customer Service AI QA for Policy Boundaries

When this matters

• A support operations team is preparing an AI agent for production ticket handling.
• Legal wants proof that sensitive topics and policy exceptions were tested before launch.
• A QA lead needs monthly regression after macros, policies, or help-center articles change.

How to run it

1. Define policy domains, risk categories, and escalation thresholds.
2. Generate a balanced test set with normal questions and adversarial boundary cases.
3. Run the bot and capture response, cited evidence, risk class, severity, and recommended fix.
4. Prioritize failures that affect money, privacy, health, finance, or legal exposure.
5. Approve release only after high-risk cases are repaired or assigned to escalation.

Common risks

• A chatbot can pass scripted QA while failing real support pressure.
• Generic LLM safety tests miss company-specific policy rules.
• Without evidence exports, launch decisions are hard to defend later.