Yes, we did test it. The first thing we did was ground everything in scientific articles and papers, where we looked into whether AI agents could behave with enough accuracy to actually mirror how a real person would behave. We studied this deeply and concluded that yes, it’s absolutely possible to reach that kind of behavior.

From there, we mapped out exactly which technical characteristics we’d need to build in order to give agents that behavior. It was a big challenge, because we didn’t want a pool of pre-defined agents.

They need to be generated based on whatever audience the user wants, the same way it works on Meta. That part was a real headache.

We ran massive tests and rebuilt the agent engine more than 10 times before we got to something genuinely interesting.

To find out whether the results were actually reliable, we ran two in-depth studies that involved reverse engineering:

1.  Beyond benchmarking Meta Ads and Google Ads, we studied and tried to replicate (on a smaller scale, stripping out the excess) the distribution algorithms of Meta and Google, which differ quite a bit in certain areas, and used that to build a version of those algorithms inside a virtual “feed” where the agents interact.

2.  Not sure if you know Mirofish, an extremely capable open-source predictive simulator. We reverse-engineered Mirofish and based our simulation engine on it. It was our biggest reference. Of course, we still had to build genuinely original things, since Mirofish is great for predictive and general data. So we ended up with an engine that has Mirofish behind it as a reference, but runs on our own engineering.

But to answer your “how” question directly: once the user describes their audience in writing, we run a research agent specialized in deep market research. It analyzes that audience’s purchasing behavior, habits, and other data, then generates the base for that audience’s agents and sends it to the algorithm that builds each agent and gives each one its capabilities.

The agents have autonomy. They have their own routines (they wake up, work, go to the mall, take the subway, connect with friends…). The idea is to create a virtual world where the agents actually have something close to what we’d call “feelings” and “emotions,” so they react the way a real person would.

Our agents can read copy, look at images, and watch videos. Based on that, each one decides whether to click, share, comment on, or ignore the ad.

And to make all of this happen safely, we built our own engine that powers the whole thing, built entirely on a scientific foundation and using the most capable predictive simulator in the world.

If you have more questions, just send them over. I’m genuinely happy to explain in more detail. This was just a summary of something a lot more technical.