35 articles in AI Engineering
AI can generate a complete test suite — descriptive names, 94% coverage, green CI — in the same session it writes the implementation. The gap is behavioral: tests written after code verify what the code does, not what it should. Mutation testing makes this visible.
59% of developers ship AI-generated code they don't fully understand. The output looks professional. The reasoning underneath is absent. Here's what broke, why it matters, and how engineering discipline rebuilds the apprenticeship AI bypassed.
A PM and an engineer sit down with four colors of sticky notes. Sixty minutes later they have surfaced every business rule, every edge case, and every unanswered question — and produced acceptance tests an AI agent can build against. No prose. No translation layer. No "the AI guessed wrong" post-mortem.
Most teams deploy agents at stage five speed on stage two or three foundations. AI amplifies whatever it's applied to — strong process gets faster, broken process gets more broken, faster. The question before expanding agent use is not whether. It's where your codebase actually sits.
The demo works. The prototype impresses. Then it hits production and quietly fails — wrong answers at scale, cascading LLM timeouts, no way to know when the prompt regressed. This is not an AI problem. It is an engineering problem with a known solution.
When an AI agent works on your codebase, your test suite is the only artifact of intent it consistently sees. Tickets, Notion pages, and Slack threads do not enter the loop. If your tests describe what the system should do, the agent is constrained. If they only describe what the code already does, the agent ships whatever it generated.
AI agents and offshore teams break in exactly the same way. They are execution accelerators, not thinking substitutes. The work that doesn't compress — specs, architecture, test design, critique — is the work you have to keep in-house. Outsource the typing. Keep the thinking.
AI didn't take your job. It exposed it. The work was never typing characters into an editor — it was translating ambiguous business intent into something that adds value to users. Code was always the byproduct. The translation was always the work.
When AI generates code that breaks production, ownership doesn't transfer to the model — the post-mortem still names the human who clicked merge. Vibe engineering is the craftsmanship answer: you delegate the typing, but never the understanding, the design intent, or the accountability.