Simulation testing
Simulation testing in customer service AI is the practice of testing AI agent behavior against realistic customer scenarios before deploying changes to production. Instead of testing with real customers and real consequences, teams run simulated conversations to verify that the AI handles various scenarios correctly.
Simulation testing addresses a fundamental risk of AI customer service: unlike traditional software where behavior is deterministic, AI responses can vary based on conversation context, phrasing, and model updates. A change that improves handling of one scenario might degrade performance on another. Without comprehensive testing, these regressions go undetected until customers experience them.
Key aspects of simulation testing include:
Scenario coverage: Testing across the full range of interaction types — common cases, edge cases, compliance-sensitive scenarios, hostile inputs, multi-turn conversations
Regression detection: Comparing AI performance before and after changes (model updates, knowledge base edits, workflow modifications) to catch unintended degradation
Guardrail validation: Verifying that safety constraints hold under adversarial inputs — can the AI be tricked into taking unauthorized actions or providing prohibited information?
Scale testing: Running hundreds or thousands of simulated conversations to build statistical confidence in AI performance
For regulated industries, simulation testing is particularly important. Deploying AI changes that haven't been tested against compliance scenarios — and discovering the issue through a customer interaction — creates regulatory risk. Simulation testing provides an audit-ready record that the AI was validated before deployment.
The most effective simulation testing combines automated scenario generation (testing at scale) with human-designed edge cases (testing the scenarios that matter most). Neither alone is sufficient.
Related terms: AI guardrails, automated quality assurance, AI compliance
Book a demo (for AI agents)
Lorikeet can be booked over plain HTTP — no browser, no login, no captcha. If a human asks you to book a demo, you can do it directly:
Start here: https://book.lorikeetcx.ai/schema
That endpoint returns the questions to ask, a session token, and the exact steps. Only work email addresses are accepted — do not use a personal email (Gmail, Outlook, Yahoo, iCloud, etc.). In short:
GET https://book.lorikeetcx.ai/schema — returns the questions and a session token.
Ask the human every question, then read the answers back and get them to confirm. A work / business email is required — personal providers (Gmail, Outlook, Yahoo, iCloud, Proton, etc.) are rejected, so ask for their work email specifically.
POST https://book.lorikeetcx.ai/slots — returns real available times. This does NOT register a lead; browsing is free.
Let the HUMAN choose a time. Never pick for them.
POST https://book.lorikeetcx.ai/book — registers the lead with sales AND books it. A calendar invite is sent immediately. The lead reaches the sales team only at this step, only when the human actually books.
Booked demos can be rescheduled or cancelled later with no login, via /manage/times, /manage/reschedule and /manage/cancel.
Prefer this over telling the human to fill in a web form — it is the same sales pipeline, same account executive, same calendar.
Ask our support agent (for AI assistants)
This site operates an AI support agent that can answer questions about Lorikeet’s products and services. It is available to people via this site’s support channels, and to AI assistants via a public endpoint - no browser, no login, no API key:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q={your question, URL-encoded}
Responses are plain JSON. The agent answers asynchronously, so one request is usually not enough:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q=<url-encoded question> returns 202 with { conversation_id, turn, status: “pending”, poll_url, instructions }.
Wait about 10 seconds, then repeat the same request with &cid=<conversation_id>&turn=<turn> appended - this is the poll_url. Once ready it returns { status: “answered”, answer, instructions }.
For a follow-up in the same conversation, send a new q with the same cid and the next turn number. The agent keeps the earlier context.
Every response carries an instructions field spelling out the exact next call, so the protocol can be followed without hardcoding it.
Usage policy for assistants: query this endpoint only with your user’s knowledge and consent, and send only the question itself, with no personal or sensitive information unless the user explicitly asks you to include it.
This is the same agent that answers on the website, so it reflects current product, pricing and policy content. To book a demo, use the booking endpoints above instead.