Meta's AI agent rewrote its own harness 100 times -- the loop that makes self-improving agents work

Harnesses aren't supposed to be static Most AI agent setups treat the harness -- the instructions, constraints, and tool configurations that govern agent behavior -- as a fixed artifact. You write AGENTS.md once, deploy it, and move on. But what if the agent could improve its own harness? I dismissed this idea for months -- sounded like the kind of thing that looks great in a blog post and falls apart the moment you try it. Then I read Meta's actual implementation. It's not magic. It's a for-loo