That moment where you ask where the agent is and the room stays quiet because you are working alone, that is the whole post for me. I bet the thirteen findings were easier to work on than the agent was, which is why they all got done first. Sounds like the second diagram is a much smaller one!
The AI Trap and the Ten-Cent Pencil
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That moment where you ask where the agent is and the room stays quiet because you are working alone, that is the whole post for me. I bet the thirteen findings were easier to work on than the agent was, which is why they all got done first. Sounds like th
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The ten-cent pencil story started in my college dorm room.
A woman walked into our room carrying a massive bag. She told us she was selling pencils for ten cents each—but there was a catch: we could only get that price if we bought her entire inventory.
"Pencils are useful," we reasoned, flexing our elite college-student brainpower. "Ten cents is basically free. We can just split them."
We shook on it. Then, she started counting.
One. Two. Three.
We sat there patiently. Then she passed one hundred. Then five hundred. Then a thousand. By the time she finally tied off the bag, our ten-cent bargain had mutated into a one-hundred-dollar invoice. That was way more than any of us expected to spend, but hey, the logic still felt sound. Each item was a dime. The math checked out. We paid her, she handed over the bag, and she left.
A few minutes later, we actually looked inside.
They weren't pencils. They were shirt buttons. Over one thousand plastic shirt buttons.
None of us knew how to sew. None of us needed shirt buttons. We had just spent a fortune on an impressive lifetime supply of something completely useless to us. The price was correct. The counting was correct. The total was correct.
We had just completely forgotten to look at what we were actually buying.
Years later, I fell into the exact same trap. Except this time, the lady with the bag was an AI document reviewer, and the buttons were cloud infrastructure.
I asked the AI to review a design document for a new AI application I wanted to build. It handed back thirteen findings. They were incredibly thoughtful, professional notes: unclear requirements, weak idempotency, missing failure handling, vague cost controls, and no cancellation flows.
"Great," I thought. "Let’s fix them one by one."
That was the ten-cent pencil.
I tackled finding number one: durable event intake missing. Easy. I added a queue. Finding two: execution state unclear. No problem, I threw in a database. Finding three: retries and branching are underspecified. Boom, I deployed a heavy-duty workflow engine.
Then the dominoes really started falling. I added approval handling. Cancellation handling. A status API. Dashboards. Alarms. Budget thresholds. An operational-readiness review.
Every single finding was valid. Every fix was perfectly reasonable. My architectural diagram became a gorgeous, hyper-scalable masterpiece covered in professional-looking rectangular boxes. It was secure. It was observable. It was a flawless, enterprise-grade backend service.
I stared at the final diagram proudly, took a sip of coffee, and asked a simple question:
“Where is the agent?”
The room went completely quiet—mostly because I was working alone, but the existential dread was loud enough.
The AI agent—the entire reason I was building the system—had been reduced to a microscopic box buried deep in the plumbing. In one version of the diagram, it had actually vanished entirely.
I had built a military-grade fortress, but forgot to put a soldier inside.
Thoughts
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Liked that the room went quiet with nobody in it. I sat with that longer than I did with the diagram.
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Nobody ever bought a thousand buttons at once. They bought them one dime at a time.
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Same, except mine was a retry policy. A week of failure handling for a job that ran twice a day and had failed roughly never. That bag was full of buttons too.
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My guess is the design doc described the system and never described a single run of it. Once one request is written out end to end, the agent stops being a small box down in the plumbing.
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Calling it an AI trap aims at the wrong part. Thirteen valid findings in a list is not a plan, and the order was never the reviewer's idea. Working them one, two, three is what does the damage. I have thrown out nine of thirteen before and I was wrong about two of the nine.
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The fortress will outlive the agent. In six months the queue, the dashboards and the budget alarms are all still running, somebody owns them now, and the thing they were built around is the side project.
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The buttons part is familiar. I did a version of it with a migration checklist, worked top to bottom for two weeks, every item correct. Then somebody asked which customers were actually blocked by any of it and the honest answer was none of them. The counting was right there too.
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A flawless enterprise backend with no agent in it is technically a passing review. Zero findings left open.
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That moment where you ask where the agent is and the room stays quiet because you are working alone, that is the whole post for me. I bet the thirteen findings were easier to work on than the agent was, which is why they all got done first. Sounds like the second diagram is a much smaller one!
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Thirteen findings from a reviewer with no idea what you are building is thirteen findings about a generic backend. Idempotency and cancellation are real problems, they are just the answers to questions nobody had asked yet. I do the same thing with linter output, fix all of it, and only later notice the fix list quietly became the plan.
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The ten-cent pencil story started in my college dorm room.