Realistic conversations, not scripted demos
Test with the messy, real phrasing an actual visitor would use — typos, vague first messages, mixed-language input if relevant — not the clean, cooperative test conversation that always makes a demo look good.
Edge cases and out-of-scope questions
Deliberately ask things outside the agent's knowledge boundary to confirm it says so honestly rather than guessing — see How to Prepare a Knowledge Base for an AI Agent for how that boundary gets defined in the first place.
Handoff triggers
Confirm the specific conditions that hand a conversation to a human actually fire correctly — see When Should AI Hand a Lead to a Human.
Failure paths
Test what happens if a downstream system (CRM, Telegram, email) is unreachable during a live conversation — the visitor should see an honest message, not a silent failure. See What Happens When an AI Automation Fails.
End-to-end, not component by component
The full path — website, agent response, CRM log, follow-up trigger — is tested as one connected flow before launch, matching how Revenue System describes launch and handover.
Questions
Do you test with real customer data?+
No — test conversations use synthetic scenarios, not real visitor data, during pre-launch testing.
What if an issue is found after launch?+
See What Happens When an AI Automation Fails for how post-launch issues are monitored and handled.
How long does testing take?+
It's part of the build timeline described in Revenue System — not a separate, open-ended phase.