How to A/B Test in Retell AI

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Most businesses deploy a voice AI agent and just hope it works. The smart ones test two versions against each other.

Retell AI has a built-in AB testing feature that lets you run two versions of the same agent simultaneously, splitting incoming calls between them.

Here is how it works in practice.

You create two versions of your agent: let's call them V2 and V3.

V3 might have a refined opening script, a different objection-handling approach, or a new way of qualifying leads.

You set each version to receive 50% of incoming calls, hit Deploy, and let real conversations do the judging.

No guesswork. Just data from actual callers.

One important caveat first.

AB testing only works if you have the call volume to support it. You need roughly 100 or more calls per week across both variants combined for the results to be statistically significant.

Below that threshold, the differences you see could easily be down to chance rather than the actual performance of each agent version.

If your business is already at that volume, this feature becomes genuinely powerful.

Even a small improvement in how the agent handles the first 30 seconds can have a measurable impact on conversion rates.

AB testing gives you a controlled way to find those improvements without taking the entire system offline or gambling on an untested rewrite.

You can also run more than two variants. The interface lets you split traffic across multiple agent versions, so a three-way test is possible if your volume supports it.

The practical use case.

Say your roofing company's agent is closing appointments at a certain rate.

You rewrite the qualification questions based on feedback from your sales team. Instead of replacing the old agent outright, you deploy both versions side by side.

After a week of real calls (assuming the volume is there), you check which version booked more jobs and retire the weaker one.

That is the kind of iteration that turns a decent voice AI agent into a high-performing one.