I handed a live product to an AI system and let it improve itself, here's what actually happened

I built a system that runs a real product with almost no human involvement. It generates the work, quality-gates it against a rubric, opens a pull request for each piece, distributes what gets merged, then reads the resulting analytics and proposes improvements to its own process. Its own code changes arrive the same way. The only thing I do is decide what to merge. The part that surprised me is how little of the difficulty was about the model being smart enough. The writing and the decisions were fine. What took real work was making autonomy trustworthy: making sure a run never silently waits on a human, that every run reports success or failure, and that every risky change has a rollback. The product itself is almost beside the point. The actual output of the project is a reusable machine that observes, decides, acts, and verifies. Full write-up: [How I Built an AI System That Codes, Runs and Improves Itself](https://fatihkoc.net/posts/ai-system-that-improves-itself/) If you've handed a real workflow to an autonomous system, what convinced you it was safe to stop watching it?