Why "It Works" Is the Wrong Bar for AI-Generated Code in Agentic Systems
The most dangerous line of code in your agentic pipeline is not the one that crashes. It is the one that runs fine in isolation, gets merged because it passed review, and then silently degrades your system's reliability at scale. AI-generated code is producing a lot of that second kind right now, and the engineering community is starting to name it.
What Actually Happened
Two conversations have been colliding in engineering circles lately. The first is about building reliable agentic AI systems, specifically the hard operational problem of making multi-step LLM workflows actually hold together in production. The second is a more personal one: experienced engineers describing the specific moment they reject AI-generated code even when it is technically correct and the tests pass.
These two conversations sound separate. They are not. They are describing the same failure mode from two different vantage points.
