TL;DR
A practical review guide for testing factual grounding, tone, dialect, escalation, privacy, and live monitoring before an AI agent launches.
1. Grounding and Hallucination Control
- Source policy: Define which sources are approved, record their owner and update date, and test whether answers cite the right passage.
- Response boundary: Require 'I do not have this information; let me connect you to a team member' when evidence is missing instead of guessing.
2. Tone of Voice & Dialect Calibration
- Cultural Fit: Verify that the agent matches the regional dialect of the user (e.g. Gulf/Saudi dialect instead of formal classical Arabic).
- Brand Tone: Keep responses concise, warm, and highly professional without sounding robotic or excessively flowery.
3. Seamless Escalation & Hand-off
- Intent detection: Verify that direct requests such as 'Can I speak to a supervisor?' reliably enter the correct queue.
- Context preservation: Forward only the relevant transcript and summary permitted by your access, minimization, and retention rules.
4. Testing and Live Evaluation
- Smoke Testing: Test with at least 50 varied user prompts containing complex colloquial expressions and typos.
- Continuous monitoring: Review a controlled sample under your privacy and access rules, label failures, update sources, and rerun the same regression set.
5. Record an acceptance threshold before launch
- Factual accuracy and citation accuracy on the approved test set.
- Unsupported-answer, unsafe-action, and unnecessary-handoff rates.
- Successful human handoff with the right queue and permitted context.
- Response latency at the median and slow tail under expected traffic.
- Zero unresolved privacy, authorization, suppression, or data-leakage defects.