Natural language workflows
Natural language workflows are automated business processes defined and configured using plain language descriptions rather than traditional programming, flowcharts, or visual rule builders. A team member describes the workflow in natural language — "When a customer requests a refund for an order placed more than 60 days ago, check if they have a loyalty status, and if so, offer a partial credit" — and the system translates this into executable logic.
In customer service, natural language workflows bridge the gap between the CX team's domain expertise and the technical implementation of automation. The people who understand customer needs and business policies best (support managers, CX leaders) can directly create and modify the logic that governs AI behavior, without depending on engineering resources.
Key characteristics of natural language workflows:
Human-readable: Anyone can understand what the workflow does by reading its description
Auditable: The logic is expressed in business terms, making it reviewable by compliance, legal, and management teams
Iterative: Workflows can be refined through conversation — "also check if the product is in the excluded category" — rather than rebuilt from scratch
Deterministic where needed: Despite being expressed in natural language, the resulting logic can enforce strict rules for compliance-sensitive operations
Natural language workflows are particularly valuable in regulated industries where the people who understand compliance requirements (legal, compliance officers) need to verify that the AI's behavior matches policy. When the workflow logic is expressed in plain English rather than code, verification becomes a conversation rather than a code review.
The key architectural challenge is ensuring that natural language descriptions translate into reliable, consistent execution. Ambiguity in language must be resolved before the workflow processes real customer interactions.
Related terms: conversational configuration, AI guardrails, 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.