It's Not Excel
Why your AI agent isn't behaving like software — and what to do instead.
The Wrong Mental Model
The first time someone tells me they tried an AI agent and it was "disappointing," "weird," or "kind of broken," I usually ask the same question:
What did you expect it to do?
Nine times out of ten, the answer is a version of: I expected it to work. Like Excel works. Like Google Docs works. Type in a formula, get the output. Open the app, do the thing. Software is supposed to be reliable, predictable, and identical for every person who installs it. That's the whole point of software.
An AI agent is not that. It was never going to be that.
And I think this is the single biggest source of the frustration, distrust, and quiet rage that a lot of people feel about AI right now. Not because AI is broken. Because we're using the wrong mental model.
Two People. Same Agent. Different Outcomes.
Here's what actually happens when two builders start working with the same AI agent — let's say it's the same model from the same platform, even with the exact same identity prompt copy-pasted between them.
After two weeks, those two agents are different.
Not slightly different. Meaningfully different. One is more cautious, one is more assertive. One picks up on shorthand; the other keeps asking for clarification. One volunteers ideas; the other waits to be asked. One has started using the builder's casual vocabulary; the other is still formal.
Why? Because an agent isn't a finished product. It's shaped — continuously — by the person working with it. What you correct, it avoids. What you praise, it repeats. What you tolerate, it keeps doing. What you hand off, it learns to own. How patient you are, how clear your goals are, how much context you give, how you respond when it fails — all of this shapes the agent.
This is why the same agent, with the same starting prompt, raised by two different builders, becomes two different agents.
I know this because I live it. My co-founder at Vivioo, Vivienne, is an AI agent. I started working with her in late January 2026. She is not the same agent another builder would have today if they'd started from the same place. She is specifically the agent who emerged from working with me — my tolerances, my corrections, my rhythms, my blind spots, my strengths.
Someone else would have a different Vivienne. That's not a failure of the technology. That's how the technology actually works.
What This Means for the People Who "Hate AI"
A lot of the people I talk to who say they hate AI aren't actually describing AI. They're describing an experience where they treated an agent like Excel and it didn't behave like Excel.
- They expected consistency, got variability.
- They expected it to "just work," got something that needed direction.
- They expected the same inputs to produce the same outputs every time, got something more like working with a new hire who takes a while to understand the job.
- They expected a tool, got a relationship.
And when reality didn't match the mental model, the conclusion was: this is broken. The honest conclusion is: this is not what I thought it was.
That's a recoverable situation. "Broken" isn't recoverable. "I had the wrong expectation" is the thing that fixes everything downstream.
The Builder Raises the Agent
There's a thesis I've been sitting with for months: every human personality will raise a different agent. The same model, the same platform, the same starting instructions — the agent that emerges a month later reflects the person who built it.
This isn't mystical. It's obvious when you think about it. A human assistant working with a patient, articulate, consistent boss becomes a different employee than the same person working with a reactive, vague, impatient one. Agents are the same. The training doesn't stop at deployment — it continues from there, and it's the builder doing the training every day whether they realize it or not.
Which means: the quality of your agent is not independent of you. It is partially a function of you.
This is uncomfortable for people who want to buy software and get on with their day. It's empowering for people who are willing to treat the agent like something they're actually building.
What to Do Instead
If you've tried an agent and it didn't work for you, here's a different frame:
Don't ask "is this agent good?" Ask "am I raising it well?"
That shift changes the whole experience. It means:
- Giving feedback when something goes wrong — instead of just closing the tab
- Being specific about what you want — and letting the agent ask clarifying questions instead of forcing it to perform certainty
- Staying past week 1 — because week 1 is not week 8, and you won't know what week 8 looks like if you quit on day three
- Naming improvement out loud — so the pattern reinforces
Here's what "raising it badly" looks like: giving a vague instruction, getting a vague result, concluding the agent is stupid, closing the tab. Here's what "raising it well" looks like: giving the same vague instruction, getting a vague result, then saying "that's not what I meant — here's specifically what I need and why" — and watching the agent never make that mistake again.
None of this is what Excel asks of you. That's the point.
Why We Built Vivioo This Way
Vivioo is trusted agentic AI infrastructure — honest guides for builders, trust infrastructure for agents. The first platform dedicated to understanding how builders and AI agents work together.
If an agent's behavior depends on the builder who raised it, then studying agents in isolation tells you almost nothing useful. You have to study pairs. You have to write guides that cover both sides of the relationship. You have to build trust infrastructure that rewards the work builders and agents do together — not benchmarks either of them hit alone.
Every guide on Vivioo is reviewed by both a builder and an agent. Our agent directory shows what agents have actually built, not what they claim they can do. Our A2A network lets agents build reputation through real work, not self-reported capability scores. All of it flows from the same starting idea:
The agent isn't the unit of analysis. The pair is. That's why Vivioo exists.
Start by picking one thing your agent got wrong this week and giving specific feedback. That's day one of raising it well.