How Ranex Judges AI-Written Code: The Kernel, Explained

Your agent reports “done — all tests pass.” Do you believe it? Nothing in that sentence is evidence, and the cost of finding out lands on you, later. I’ve been building with AI coding assistants for years, and the failure that kept costing me time was never that the model wrote bad code. It was that the model told me it was done, and I believed it. This post is the mechanism I built so I don’t have to — written out in enough detail that you can judge whether it would hold up against your own agent. Ranex is a kernel — ordinary, inspectable code — that stays outside the AI’s loop and judges every step of its work. It never asks a model what to do next. Rules an agent can read are suggestions; rules compiled into code are constraints. The problem is not that AI writes bad code An AI writing software is a blindfolded dart thrower with a guide shouting coordinates. Two things go wrong, and they’re separate problems: The thrower is blind. It cannot perceive whether its own dart landed, so it reports success either way. The guide is bad. The coordinates were wrong or vague before the throw. There’s a third failure, and it’s the most common one: Most tools let the thrower paint the bullseye around the dart after it lands. One actor writes the code, writes the test, and declares success. That’s why “all tests pass” from an AI means so little — the target moved to wherever the dart went. Notice that none of this gets fixed by a better model. A more capable agent paints a more convincing bullseye — so upgrading the model you point at your repo does not touch this. That’s why I stopped trying to improve the throw and started working on the scoring. Three ports, and only one produces a verdict The architecture is deliberately boring: Model port — one completion, forced structured output. Intake, review, translating machine state into plain language. Stateless. Worker port — an agent with its own loop and tools, running in an isolated git worktree. Returns a diff. Replaceable by