Very interesting perspective, thanks for sharing!
AI Coding: Architecture or Archaeology?
9 Comments
Yes, agreed.
While AI can greatly accelerate the coding process, it can't replace the fundamental work of architecture and design. The model, boundaries, states, failure modes, and tests are the backbone that ensures the system works correctly and can evolve over time. AI should be seen as an enabler, not a replacement, for the core responsibilities of software engineering.
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To a certain extent, this is true. It doesn't have to be, but for most prompt engineers it is.
Did you know you can direct the AI to program according to principles? The same principles you use that lift you above the neophyte in terms of competence. The model contains all of the knowledge you yourself have absorbed and internalized and probably not even named. Design patterns. SOLID. All the good stuff is out there, waiting to be internalized by your agent.
You just have to direct it to go out and get it and synthesize.
"You are an expert programmer" does not do that. That's cosplay.
@[demoran] Right, a practical note of yours. AI does not do what you describe out of the box, one has to instruct it clearly that is normally done with skills/rules (depending on LLM or a coding environment, e.g. Cursor) or just a project guide markdown files. AI would follow (99% of time) exact patterns (beyond SOLID or GoF) you describe in these files up to the giving it templates. In this regard it does really good job.
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Valentine Shi hit the nail on the head. AI coding often leads to 'Archaeology'—digging through accidental assumptions.
This is why I built Penta-V Kernel. It provides the Architectural Anchor that AI lacks. While AI drafts the code, Penta-V enforces the invariants and the boundaries at a sub-nanosecond scale (845ps).
We don't let AI 'smear decisions' across the codebase; we lock them into a sovereign geometric manifold. Architecture is not optional, and Penta-V makes it enforceable.
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The deeper problem isn't that AI writes bad code — it's that it flattens architectural decisions into local code suggestions. It re-applies generic patterns without seeing the boundaries, contracts, or invariants the team has already committed to. For a junior engineer this looks like authority; for the system, it's slow erosion. Disciplines like DDD exist precisely because those decisions can't be inferred from the file you're editing — and that's the layer AI agents currently can't see. The result is what the post calls archaeology: speed of delivery wins, and quality, scalability, permissions, and privacy quietly lose.
@[Hussein Mahdi] Thanks for pointing out this. All those things, including code templates can/should be built into AI's reasoning with the guiding files. They can be re-used across projects, and customized per project. The thing is one has to know what to write in these guidelines and what the desired AI output should look like. The bad code and generic system decisions come as out-of-the-box behavior that can be redefined.
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