Train your agents on real conversations, not AI-generated data

Most people training an agent right now are feeding it a summary of what they think happened. That's the mistake.

A model doesn't learn from the tidy version. It learns from the mess — the actual transcript, the email that went three rounds before the deal turned, the call where the buyer said one thing and meant another. Summarise that first and you've thrown away the exact signal the model needed. You've taught it your conclusion, not the pattern that led there.

Real beats synthetic, every time

There's a shortcut going around: generate your training data with another model. It's fast, it's clean, and it's hollow. Synthetic data teaches an agent to sound like your best rep. It can't teach it to be one, because it never saw a real one work. Feed a model AI-generated conversations and you get an agent fluent in the average of the internet — confident, plausible, and wrong in the ways that cost you deals.

Feed it the real thing, unsummarised. Real conversations. Real deals, won and lost. Real emails, real objections, real events as they actually unfolded. The model's first job isn't to reason — it's to detect the pattern: this shape of exchange, this kind of hesitation, this sequence of events. Then to identify the scenario and put a probability on it: "this looks like a stall, and it's 70% budget." Only then does reasoning have anything real to stand on.

The human never leaves the room

Here's the part the demos skip: someone has to train and validate every step of that. A human has to say "yes, that's a stall" or "no, you missed it — that was a champion going quiet because they got overruled." The model detects; the human corrects. Every step, for a long time. An agent that isn't validated by someone who has actually closed deals is just a very articulate guess.

Agents are good. They are not human.

They can watch more, remember more, and never get tired. But they are not human, and pretending otherwise is how you end up automating your own bad assumptions at scale. The value isn't the agent replacing the person. It's the agent handling the pattern-detection so the person can spend their judgment where judgment actually matters. That's the line we hold on every AI build — and the same reason we're careful about where AI belongs at all.

Real data in. Human validation throughout. That's the whole thing. Everything else is a demo.