Agent autonomy is usually pictured as a ladder — a low rung where the agent does a little on its own, a high rung where it runs nearly unattended, and the job is to climb. There’s a sharper way to read the same progression. Each step up isn’t more autonomy in some general sense; it’s one specific decision moving out of human judgment, or out of code, and into the agent. ...
Most production AI agents are static. The prompt you ship is the prompt you keep — until somebody reads enough output to notice a recurring failure, edits by hand, and ships a fix. The cycle takes weeks; most failures slip through it. A self-evolving agent shortens that loop, but it has to solve one structural problem first: a model auditing its own output is bad at it. Same-model audit produces self-deception — the auditor rationalises the same blind spot the triage agent just exhibited. The fix isn’t more prompt engineering; it’s a different model. ...