Six months running an AI reviewer in the path of every production command (got surprised by what it did to the security team)

i work on an open-source access gateway. we've had an LLM-based reviewer sitting in the path of every production command for about six months with customers in production. the surprise was not technical. it was organizational. going in, the assumption was that the LLM would change how developers worked. fewer manual approvals, faster iteration, less friction for low-risk commands. that happened, roughly as expected. what changed for security teams is what we did not see coming. before the AI reviewer, security's relationship to production access was binary. either they reviewed something or they didn't. most things landed in the second bucket. there was no bandwidth to look at every command, so reviews concentrated on the obviously sensitive surfaces and everything else got static policy with periodic audits. once the AI reviewer was in the path, the relationship shifted. the model handles the volume the team cannot. it flags what looks risky, takes a first pass on context, applies the team's prior guidance. the team stops being a bottleneck on every command and starts being the judgment layer on what the model surfaces. what i did not expect: people on the security team started talking about the reviewer the way you talk about a coworker. agent-in-the-loop is the term that gets used now, and the loop has two agents in it. one for the dev team shipping changes, one for the security team reviewing them. security teams stop governing the dev team and start governing the dev team's agents. happy to go deeper on any of this if useful.