In my last post on Human-in-the-Loop (HITL) for agentic workflows, I talked about something simple but important — AI can execute, but humans own the outcome (intent, constraints, and validation).
Occasionally, I come across many scenarios where this play out very concretely. I wanted to share once of such instance here.
I was building velocity features for a fraud detection use case using AI-assisted coding. The output looked great. Clean structure. Correct-looking pandas logic. Even the rolling window approach was exactly what you’d expect in a “textbook solution”.
And yet — it kept failing.
Not because of syntax issues or logical bug. Just runtime errors and inconsistent behavior that didn’t make sense at first glance.
After digging in, the issue was surprisingly simple: the AI tool had assumed a “perfect world version” of the data and execution environment. Clean timestamps. Perfect formatting. No edge cases. No real-world messiness.
Basically — an ideal solution that works everywhere except reality.
And that’s when my previous blog post on HITL reminded me to share with you this learning.
Because this is exactly the gap we talk about, but rarely experience so sharply:
AI generates what should work. (possibilities)
Humans catch what actually works.
The failure wasn’t in the code logic — it was in the assumptions behind the code. And those assumptions are often invisible unless you deliberately question them.
This is also where AI-assisted coding can quietly mislead you. It moves fast, looks confident, and produces “industry-standard” patterns. But it does not naturally question constraints like data types, ordering, or system quirks unless you explicitly force that thinking.
So the real job of a human-in-the-loop is not just reviewing outputs, it is challenging assumptions the model didn’t even know it made.
That’s the part no model fully owns yet — and probably shouldn’t. Because in real systems, correctness is not about writing code that looks right, it is about surviving everything that is not in the prompt.
(Thoughts are personal but polished by GenAI companions)