proxiblue.com.au · engineering notes
How I run, test, and govern AI coding agents for real client work. Written up so peers can borrow the ideas. Everything here runs live in my fleet.
A vendor security advisory hid two High-severity lines in a later chunk. My agent reported clean anyway — it had read a summarised preview, not the page. Here's what got banned the same day, fleet-wide.
Nobody runs them because they run themselves. Coding standards, static analysis, comment hygiene and duplicate-code detection moved out of code review and into the commit path, so my agents' output gets linted, analysed and de-narrated before a human ever reads it.
Print debugging is fast to type and almost always slower to truth. I took var_dump away from my agents and wired in real breakpoint debugging instead — the print-statement path is blocked at the moment of the edit, not just discouraged in prose.
Code tells an agent what exists. A temporal knowledge graph tells it why things are the way they are: the meetings, emails, tickets and reversed decisions I used to carry in my head. That is what turns generic plans into scoped ones.
Every rule my agents live under exists because something went wrong once. So each rule gets an eval that replays the original incident, headless, and proves the defence still holds. If a rule can't be demonstrated to fire, it is not a rule, it is a hope.