In early April, Meta's engineering team published a piece describing how they used a swarm of 50+ specialised AI agents to map tribal knowledge across one of their large data pipelines: four repositories, three languages, 4,100+ files. The post has been making the rounds in platform engineering circles for good reason. It names a problem most polyrepo organisations live with quietly, and shows what an industrial-scale solution looks like. I read it twice. I think there are actually two posts inside it. One is a complex AI orchestration story about agent swarms, critic passes, and self-refreshing context files. That's the one most takes have focused on. The other is a quieter architectural argument that gets one paragraph and is the more useful piece for everyone who isn't Meta. This is about the second one. What Meta actually built Meta's pipeline is config-as-code: Python configurations, C++ services, Hack automation scripts working together across multiple repositories.…