Try It Before You Point a Phone Number At It: Pre-Built Agents and a Fake Clinic API
The gap between reading about a feature and seeing it work
Actions — letting the agent look something up instead of only quoting a document — has the widest gap in this product between understanding it and having watched it happen. To try one you need an endpoint of your own, reachable from here, with a request shape you have already decided on. Most people evaluating a platform have none of the three, so the feature gets read about and never used.
Two things close that gap: agents that arrive already written, and a backend to point them at.
Seven agents, with the prompt shown before you choose
New agent is a split button. Blank is the old flow, untouched. Pre-built agents opens a picker: a list on the left, the whole agent on the right — what it is for in a sentence, an editable name, the opening line, a voice from the same catalogue the Voice page offers, and the entire prompt.
That last one is deliberate. Choosing between Patient intake and Clinic receptionist is choosing between two documents, and hiding the document until after the decision asks you to trust us about the only thing that matters.
The seven: patient intake, clinic reception, restaurant bookings, property enquiries, order support, quotes and callbacks for a trade, and course admissions.
The voice you pick is stored on a voice channel that is switched off — no number, no surfaces — so creating a pre-built agent cannot answer a call by accident, and your choice is not thrown away either. Turning voice on later does not ask again.
A fake clinic to point Actions at
A pretend backend lives at /api/demo/clinic. Five operations: hours and address, open times, book a visit, take a message, and an emergency escalation that answers with the words to say rather than an error the agent cannot voice. It is deterministic, needs no key, and stores nothing.
It is wired to the Patient intake agent, whose instructions already name these endpoints. Create that agent, paste the URLs into Actions, and watch a booking come back with a reference number in it.
It is a mock and it says so in every response it sends. A booking succeeds and is forgotten; ask about it afterwards and it will cheerfully fail to find it. Two people demoing at once cannot see each other's data because there is no data. A mock that pretends to remember is one somebody eventually trusts.
There is no state machine in it either, which is the more useful lesson. A caller books a visit, mentions a symptom and asks about parking in whatever order occurs to them, and every endpoint re-reads who it is about from its own arguments rather than trusting something remembered. Real front-desk work is like that too.
Then talk to it
Test in the browser first. The Playground runs a real conversation against the agent's actual configuration — instructions, knowledge, tools — and an in-browser voice call lets you hear it before a phone number is involved at all.
Only then attach a number. Rent an Indian number from us at ₹349 a month, or import a number you already hold with Plivo for nothing. Calls are billed from ₹2 a minute, all-inclusive, and metered in 30-second pulses rather than rounded up to the whole minute — so an evaluation that turns into four short calls costs about what four short calls should.
What we do not have
There is no simulation suite. No auto-generated persona callers, no adversarial red-team scenarios, no AI judge marking runs pass, review or fail. Testing here is you talking to the agent, and then reading what real calls actually did — connect rate, talk time, dispositions, transcripts, recordings.
We would rather say that than sell a fixture. A generated persona is a model's guess at your customers, and passing a suite that a model wrote for itself is not the same thing as being ready.
Get started
Create the Patient intake agent, paste the demo clinic URLs into Actions, and have a browser call with it. That is about five minutes, and it is the fastest way to find out whether Actions is the feature you thought it was.
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