The four tools referenced below are open source at github.com/officialwhitebird.
When you run AI coding agents one prompt at a time, the model is the thing you watch. When you run them at volume — many small jobs, handed to agents, coming back as diffs and files and result records — the model stops being the interesting risk. The interesting risk moves to the edges of the run: what went in, what came back, what the agent was allowed to touch, and what got written into public-facing copy on the way out.
That is the same place a physical factory puts its attention. Not on any single machine, but on the boundaries between stations: incoming inspection, work authorization, the contract each station has to meet, and final inspection before anything ships. A run that produces a plausible-looking output but fails one of those boundary checks is not a success. It is a defect that happens to look finished.
This is a description of how I structure that discipline for AI coding runs, and the small set of local, offline command-line checkers I use to make each boundary an explicit, deterministic step instead of a feeling.
The four boundaries of an AI run
