Every few years, our industry rediscovers an old truth and pretends it’s new.
Clean code.
Microservices.
DevOps.
Now: prompt engineering.
Suddenl...
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This is a really sharp and grounded take—I like how clearly you separate the hype from the actual engineering reality. The point about AI amplifying architecture rather than fixing it feels especially true from what I’ve seen in real systems. I agree that prompts often end up masking deeper design issues instead of solving them, and your distributed-systems comparison really lands. Posts like this make me want to think more seriously about how to design AI features the “boring but correct” way.
Appreciate this. The biggest frustration for me is watching prompts become a substitute for thinking. It feels like we’re repeating old mistakes, just with nicer language.
Yeah, that really came through. The “AI amplifies architecture” point hit hard — I’ve seen teams assume the model will smooth over design gaps instead of exposing them.
Exactly. When things break, people blame “hallucinations,” but most of the time the model is just faithfully executing a bad abstraction.
The distributed systems comparison was especially spot-on. Once you frame agents that way, the failure modes suddenly look… very familiar.
That framing helped me too. Retries, side effects, hidden state — none of this is new. We’ve just wrapped it in natural language and pretend it’s different.
And guardrails end up being more prompts on top of prompts. At some point it feels less like engineering and more like negotiation.☺
Right. If you need 2,000 tokens to explain your business rules, the model isn’t the problem — your system is already screaming.😀
Which is funny, because the demos look magical… but production feels fragile the moment real users show up.
That’s the tradeoff. Good architecture makes AI boring. Bad architecture makes it look impressive — briefly.
Honestly, that might be the best unintended benefit of AI so far: it forces us to confront architectural debt we’ve been ignoring for years.
Thanks.
Strong take—and accurate. LLMs don’t introduce intelligence into a system; they faithfully execute whatever abstractions you give them, so weak boundaries and unclear sources of truth simply get amplified, not fixed.
You’re right — prompt engineering doesn’t fix architecture.
It reveals it.
What most teams call “AI failure” is just latent system debt finally speaking in plain language. When an LLM “makes a bad decision,” it’s usually executing faithfully inside a broken abstraction: fragmented domains, no single source of truth, and business rules smeared across time and tooling.
Good architecture makes AI boring.
Bad architecture makes AI look magical — until scale, cost, or reality hits.
If your system needs ever-longer prompts, retries, and human patching to stay sane, you don’t have an AI problem. You have an architecture problem that now talks back.
The uncomfortable part: AI doesn’t replace design.
It removes excuses.
Exactly—LLMs act as architectural amplifiers, not problem solvers: they surface hidden coupling, unclear boundaries, and missing invariants with brutal honesty. When intelligence appears “unreliable,” it’s usually the system revealing that it never knew what it stood for in the first place.
You're right, but the world and the ones with influence are pushing these things very hard, and it becomes hard for opinions like this one but nice one 👏👏
Thanks for your response.
Let's build something amazing together!
Good point of view, keep it up.
Great.
Thanks for your response.
If you need help, please.