U [in] AI founder Olga Bialkovich publishes new article on AI agent consistency in Unite.AI
Another conversation around U [in] AI has made its way into the international AI media space.
This week, Olga Bialkovich, founder and CEO of U [in] AI, published a new opinion piece in Unite.AI, one of the specialized publications covering artificial intelligence, AI companies, research and industry leaders. The article appears in its Thought Leaders section.
The title is:
“You Can’t Sample-Test a Brand That Talks to Everyone Differently.”
And this time the subject is something we’ve been thinking about a lot internally as AI agents take on more real work inside companies.
How do you know that an AI agent is actually behaving consistently?
Not just writing well. Not just following the right tone of voice.
Actually making the right decisions.
You can read the full article on Unite.AI here: You Can’t Sample-Test a Brand That Talks to Everyone Differently
01 — The question changes once AI starts talking to real customers
At U [in] AI, we work with AI systems inside real marketing and sales processes.
And there’s a big difference between an agent that produces a nice demo and an agent that is actually talking to potential customers every day.
Once AI becomes part of a real business process, tone of voice is only one part of the job.
The harder questions start appearing very quickly.
Can the agent offer a discount?
Can it change the timeline?
Can it suggest special conditions?
Does it know when it can continue the conversation on its own and when a human needs to step in?
That’s where the idea behind Olga’s Unite.AI article came from.
02 — A real case showed the problem
In the article, Olga describes a real project with a recruitment agency.
On the surface, the AI agent looked fine. It understood the product, knew the audience and communicated naturally.
If you read one conversation at a time, there was nothing obviously wrong.
But when several conversations were compared side by side, something changed.
The agent had started making slightly different promises in very similar situations. One potential client received one timeline, another received something different. In some conversations, the agent suggested flexibility around individual terms even though the company hadn’t given it the authority to make those decisions.
That’s a very different problem from bad copy.
And it’s surprisingly easy to miss.
03 — Testing the decision, not the sentence
One of the main ideas in the article is simple: random conversation reviews aren’t enough when every conversation is personalized.
If every customer talks differently, then naturally every AI response will look different too.
So instead of comparing the wording, Olga suggests comparing the decision behind it.
Take the same business situation and run it several times with different types of people.
One person asks politely. Another pushes harder. Someone mentions a competitor. Someone says they’re ready to buy today.
01 · Asks politely
02 · Pushes harder
03 · Mentions a competitor
04 · Ready to buy todayThen compare what the AI actually decided.
Did the business conditions stay the same?
Did the promise stay the same?
If it changed, was there a real reason?
That approach is very close to how we think about testing and building intelligent AI agents inside U [in] AI.
The goal isn’t to force AI to give identical answers.
Actually, that would defeat the point of personalization.
The goal is to make sure that personalization doesn’t quietly change the company’s rules.
04 — Sometimes the AI exposes a completely different problem
Probably the most interesting part of the article comes later.
You test the agent, find an inconsistent decision, bring it back to the company and ask:
“So what should the correct answer actually be?”
And suddenly the team doesn’t agree either.
Sales says one thing. Marketing understands the rule differently. Management remembers an exception that was made six months ago.
At that point, AI hasn’t necessarily created the inconsistency.
It has exposed one that was already inside the business.
This is why AI agent testing can unexpectedly become a kind of company audit.
Before businesses can expect AI to make consistent decisions, they may first need to make their own internal decision logic much clearer.
That idea also connects directly with how we build AI systems at U [in] AI. Our approach is based on putting company expertise, processes and decision rules into the system rather than asking a generic model to somehow figure them out on its own.
05 — Why this publication matters to us
This is the second recent international AI media publication featuring Olga and the work behind U [in] AI.
For us, that matters because the discussion is moving beyond individual products and features.
People are starting to ask bigger questions about AI agents: how they make decisions, how much authority they should have, how they learn company logic and what happens when they become part of real customer communication.
Those are exactly the questions we want to work on.
U [in] AI started with very practical business problems: lead generation, outreach, marketing automation and communication.
As the technology develops, the question becomes more interesting.
How do we make AI capable of doing more work without losing the judgment, context and communication logic that normally live inside experienced people?
We don’t think the answer is simply “more automation.”
And apparently, we’re not the only ones interested in the question.
Read Olga Bialkovich’s full article in Unite.AI:
You Can’t Sample-Test a Brand That Talks to Everyone Differently
Olga Bialkovich Founder · AI and marketing strategy Olga founded the lead-generation product that now runs as U [in] AI, and she sets the direction of the lab: which parts of marketing are worth automating, and which stay with people. Her working claim is a practical one — a system built around the right profile and the right message takes up to 60% out of the cost of finding customers and partners. 
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