You’ve felt it. You prompt an AI agent, wait ten seconds, and suddenly you have a fully functioning app. UI, backend, database connections—it’s all there, running flawlessly. It feels like magic. You are a genius.
But the faster you can build, the faster you can build the wrong thing.
We are entering the era of Vibe Coding, where technical implementation is no longer the bottleneck. But this extreme ease of development is a trap. It creates a “complete product illusion” that is quietly killing your ability to ship a real MVP.
I learned this the hard way building HiPaw, an AI product for veterinary clinics. It started with a simple, validated problem: Vets are talking to pet owners, examining animals, and trying to write SOAP notes at the same time. It’s chaotic. So, I wanted to build an AI Scribe that turns the conversation into a structured medical record.
But then the Vibe Coding took over. I started looking at the architecture and thought: If the AI is already reading the medical record, why not have it generate a client-friendly care guide? If it knows the animal’s weight and medication, why not add a dosage auditor? If it can do that, shouldn’t we add a Clinical Advisor that uses RAG to retrieve medical guidelines and suggest next steps?
Suddenly, my simple Scribe MVP had ballooned into a multi-agent platform with Routers, Reranking, and Function Calling. The architecture diagram looked like a modern enterprise SaaS. I felt incredibly smart.
But I hadn’t built a product. I had built a graveyard of untested assumptions.
A demo only needs to succeed once. A product needs to know exactly how it fails.
Vibe Coding lowered the psychological cost of adding a feature to zero. In the past, development friction forced product teams to prioritize. If a feature took three weeks to build, you had to rigorously justify its existence. Now? A feature takes five minutes to prompt into existence. If it seems useful, it goes in the PRD.
But “technically related” does not mean “should be in the same version.”
My Scribe and my Clinical Advisor both read the same patient context, so I lumped them into the same MVP. But they operate on completely different risk levels. The Scribe just extracts what the vet already said. The Clinical Advisor introduces new external medical knowledge and makes clinical judgments. One is an organizational tool; the other is a liability minefield requiring expert evaluation, source tracking, and refusal mechanisms.
By testing them together, I wasn’t getting more data. I was getting noise. If the combined output failed, I wouldn’t know if the voice transcription was bad, the RAG retrieval was broken, or if the vet simply didn’t need the feature at all.
Vibe Coding didn’t eliminate product management; it just moved the bottleneck from “how to build it” to “why are we building it?”
We need to radically redefine what an MVP is. It is not a stripped-down version of your complete product vision. If your final product has three agents, making them three “simple” agents doesn’t make an MVP. You are still testing three different value propositions and three different risk profiles simultaneously.
An MVP is not the minimum set of features. It is the minimum evidence loop.
It is the shortest path to proving or killing one specific assumption. For HiPaw, the assumption wasn’t “Can we build a multi-agent vet platform?” The assumption was: “If a vet dictates a summary post-examination, can an AI generate a structured SOAP note that saves them time and they actually trust?”
That’s it. No RAG. No Clinical Advisor. No automated ERP integration. Just dictation, generation, editing, and measuring the time saved. If that core loop doesn’t work, adding a Reranking pipeline won’t save it.
To survive the Vibe Coding era, you need to stop writing feature roadmaps and start writing evidence ladders. Before you write a single line of code or prompt, you need an MVP Contract.
This contract must define exactly what you are NOT doing. “Future-proofing” is just procrastination wrapped in an architecture diagram. Every time you are tempted to add a feature because “the AI can just do it,” you must ask: What specific failure am I trying to solve? If you don’t have the evidence of that failure yet, the feature stays out.
You also need to write your stop conditions. What result will make you abandon this direction entirely? If the vet says they like the idea but won’t use it in their actual workflow, do you add more features to entice them? No. You stop. You pivot. A feature without a stop condition is just a sinking ship taking on more weight.
The anxiety you feel when looking at powerful AI tools isn’t imposter syndrome. It’s the realization that the safety wheels are off. When tech was hard, bad ideas died on the whiteboard. Now, bad ideas can be prototyped in an afternoon.
Everyone is chasing AI Agents, complex RAG architectures, and autonomous workflows. But the true mark of an elite AI Product Manager isn’t how many advanced technologies they can implement. It’s their capacity for restraint. It’s the ability to look at a tool that can build anything and have the discipline to say, “Not yet.”
The ultimate AI superpower isn’t building faster. It’s knowing when to stop.
FAQ
Q: Isn't Vibe Coding just democratizing development? How is faster building a bad thing?
A: Faster building is great, but it removes the natural friction that forces prioritization. When you can build anything in minutes, you end up testing everything at once. If the product fails, you have no idea which feature caused the failure. Speed without focus just helps you hit a brick wall faster.
Q: How do I define a 'minimum evidence loop' for my AI product?
A: Stop asking 'what features should the MVP have?' and start asking 'what is the single most risky assumption I need to prove?' Build only the shortest path to test that one assumption. If you're testing whether an AI summary saves time, don't build ERP integrations or multi-agent workflows. Just test the summary and measure the time saved.
Q: Should I just ignore multi-agent architectures and RAG completely then?
A: No, but they shouldn't be your starting point. Only add RAG when you have evidence that the model's internal knowledge is insufficient for the task. Only add multi-agent workflows when you've proven a single, simple workflow works but needs dynamic routing. Let evidence dictate your architecture, not the other way around.