u/voipceo

The latest Behind the Call is up: a hands-on teardown of Wirevox AI, an inbound AI receptionist.

The agent is genuinely strong where the operator controls the words. It discloses it's AI. It refuses to invent a price it doesn't have. It delivers a correct, liability-aware gas-leak safety instruction. That's real craft. There is clearly a system-level Agent Constitution running that knows it's in a voice call and how to act accordingly.

There is a gap between the confident statement of the agent and the completed action and it is the real frontier in voice AI right now. It isn't a prompt-generation problem. It's behavioral engineering: teaching an agent to speak about actions it can verify differently from actions it merely attempted.

Credit where it's due: exposed, version-controlled, editable prompts and a clean, modern UI put Wirevox ahead of much of what I've tested. Close the confirmation-vs-execution gap and it's near the top of my list.

Link to full teardown, with the receipts in comments.

What if the LLM was already trained and configured for voice?

Not like “can talk through a voice interface," but actually built for voice interaction out of the box.
Even more specifically, for telephony.

Today, when you build a Voice AI agent, a lot of the customer-level prompt ends up doing work the base system should probably already understand:
How to handle interruptions.
How to avoid reading long URLs.
How to respond when the caller changes their mind mid-sentence.
etc.

That feels backwards to me.
The business prompt should be about the business:
Who are you?
What do you do?
Where do you operate?
What services do you offer?
What should happen next?
But too often, before we even get there, we have to teach the agent how phone calls work.

So here’s my question:

Would you use an LLM that was specifically trained, tuned, and system-prompted for voice and telephony from the start?

Not a generic chat model with a voice layer on top, but a model whose default behavior understood phone conversations, caller behavior, confirmations, transfers, interruptions, silence, DTMF fallback, escalation, and spoken response design.

Would that be useful?

Or do you think this belongs in the orchestration layer around the model, not the model itself?

How long should a Voice AI prompt be?
Tomorrow I'd providing an in-depth teardown of a voice AI provider. They seem to have a suggested prompt limit of 4,000 characters.

My reaction: that feels small.
But the real question is not:
How long should the prompt be?
It is:
What is the prompt responsible for?

If the prompt is only describing the business, the services, the call objective, and the escalation rules, 4,000 characters might be workable.

But *if* the customer also has to teach the AI how to behave as a voice agent, that space disappears fast.
Things like:
Don’t invent details.
Ask one question at a time.
Tool usage.
etc.

That should not all live in the customer prompt.
The business-level prompt should be about the business.
The platform should already provide the voice-agent layer underneath it.
Otherwise, it is like having to teach Excel what the plus sign means before you can use addition.
That would be exhausting.
Voice AI has the same issue.
A good prompt should probably be shorter than people think.
But a good production agent should not depend on one short prompt box.

Ideally there should be layers:
Platform behavior, e.g. The Agent Constitution.
Industry behavior, e.g. How to be a plumber, etc.
Company instructions, e.g. hours of operation and service area.
Knowledge base.
Tool rules.
Escalation rules.
Testing and observability.

So yes, prompt length matters.
But prompt responsibility matters more.
A 4,000-character limit is fine only if the platform is doing a lot of work underneath it.
If it is not, that limit is not simplicity.
It is a ceiling.

The AI agent doesn't have to live beside the phone system. Instead, it could live inside it.

I've spent the last two weeks testing a voice AI platform and I'm now putting the final touches on that report that I will share on Friday.

Since the key idea is A2E, I thought I'd first spend some time defining it and why it's useful.

I did a full article at AI Contio in comments.