Break your AI agent before your customers can
Lorikeet is built simulation-first. Generate scenarios from your real tickets, run them in bulk, attack the agent with adversarial prompts and ship changes only when the results hold.
To test an AI support agent before customers see it, run it against realistic conversations with known right answers. In Lorikeet you import real tickets to generate scenarios and test customers automatically, run them in batches against a draft of your concierge with tool responses mocked, and agree a pass-rate threshold before you publish. Guardrails get their own test tab, and adversarial scenarios such as prompt injection run the same way.
Which AI support platforms let you simulate conversations before launch?
Lorikeet turns real tickets into simulations: importing a ticket generates the scenario and a test customer from the actual tool responses in that conversation, and Coach can rerun failed scenarios against your latest draft and compare the results with the previous batch (docs: simulation-driven development). Before choosing Lorikeet, Carmoola ran a proof of concept and “felt like we’d really met our match” (Carmoola story).
Other platforms with simulation testing:
Sierra runs AI-powered simulations against defined outcomes, keeps regression test suites, and has voice simulations for transcription and background noise (Sierra Agent Studio).
Decagon auto-generates tests with Duet, supports scheduled test runs and CI/CD integration, and can alert tools like PagerDuty (Decagon Testing and QA).
See Lorikeet vs Sierra and Lorikeet vs Decagon.
How do Lorikeet, Sierra and Decagon test an AI agent before launch?
Where scenarios come from: Lorikeet: Imported real tickets, synthetic scenarios, or generated by Coach. Sierra: Simulated conversations against defined outcomes. Decagon: Auto-generated by Duet.
Tool and API calls in tests: Lorikeet: Mocked through test customers; knowledge search runs live. Sierra: Not stated on page. Decagon: Checkpoints confirm the right actions, data and tools.
Comparing versions: Lorikeet: Rerun and compare with the previous batch; suites group scenarios. Sierra: Regression test suites before each release. Decagon: Tests every agent version via CI/CD.
Guardrail and adversarial tests: Lorikeet: Dedicated guardrail Test tab plus guardrail test scenarios. Sierra: Not stated on page. Decagon: Not stated on page.
Voice-specific testing: Lorikeet: Conversation testing lab for outbound calls. Sierra: Voice simulations. Decagon: Simulations across channels.
Sources, one per column. Lorikeet: docs: simulations overview, docs: simulation-driven development, docs: guardrails, docs: outbound voice. Sierra: Agent Studio. Decagon: Testing and QA.
Why adversarial tests matter: OWASP lists prompt injection first (LLM01) in its Top 10 for LLM applications, and notes that injections “do not need to be human-visible/readable, as long as the content is parsed by the model” (OWASP LLM01:2025).
How do Lorikeet simulations work?
Simulations
Can I build simulations from our real tickets?
Generate simulations straight from your actual tickets and run them in bulk batches, so every workflow change is tested against the conversations your customers actually have.
Batch diffs
How do I see what a change improved or broke?
Side-by-side batch comparisons show how a workflow edit changed outcomes across hundreds of scenarios, with per-conversation drill-downs when you need to understand a single result.
Guardrail testing
Can I test adversarial and prompt-injection attacks?
Author adversarial scenarios such as false authority claims, mid-conversation goal switches and prompt injection attempts, then run them as guardrail test scenarios so you know how the agent responds before a real user tries the same tricks.
How does Lorikeet test its own defences?
We publish a live prompt-injection challenge at Own Goal, and third-party red teams test Lorikeet’s defenses on top of our internal testing. We think the fastest way to trust an AI agent is to attack it. Testing reduces risk rather than eliminating it, which is why simulation results pair with runtime guardrails and quality scoring in production.
AI agent simulation testing: frequently asked questions
Which AI support platforms let you simulate conversations before launch?
Lorikeet, Sierra and Decagon all offer simulation testing. Lorikeet builds scenarios from your real tickets, generating a test customer from the actual tool responses, runs them in batches against a draft of your concierge, and lets Coach rerun failed scenarios and compare the results with the previous batch.
What is AI agent simulation testing?
Simulation testing runs realistic scenarios against your configured AI agent before customers ever talk to it. Lorikeet generates simulations from historical tickets and synthetic scenarios, runs them in bulk batches, and shows projected resolution quality and knowledge gaps so problems surface in testing rather than production.
Can I test my AI agent with adversarial prompts?
Yes. You can author adversarial scenarios, including gaslighting, false authority claims, mid-conversation goal switches and prompt injection attempts, and run them as simulations and guardrail test scenarios. Adversarial testing reduces risk rather than eliminating it, so results pair with runtime guardrails in production.
What are simulation suites?
Suites are grouped sets of scenarios you keep and re-run after workflow changes. Because the same scenarios run against every version, side-by-side diffs show exactly what a change improved or broke before anything reaches customers.
Does Lorikeet test its own defenses?
Yes: continuous internal adversarial testing, third-party penetration testing, and Own Goal, a public prompt-injection challenge where anyone can try to break a live Lorikeet agent.
How do simulations fit with guardrails and QA?
They cover different moments: simulations test behavior before launch, runtime guardrails check conversations in production, and Coach scores conversation quality afterward. Together they form the layered quality controls that regulated support teams need.
What can’t Lorikeet simulations test today?
Simulations run the whole concierge end to end, so you can’t test a single skill, guardrail or prompt section in isolation; guardrails have their own test tab for that. A mocked tool also returns the same answer whatever inputs it receives, so a branch that depends on different results from repeated calls to one tool can’t be simulated yet.
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.