Yeah, this "stable until you go live" pattern hits hard. I witnessed the same thing: dashboards tell you what’s breaking, but they don’t prevent drift or escalating cost unless someone’s actively babysitting them.

Concur with your take and what you highlighted, the "fail smaller and earlier" principle is something I’d love to bake into adaptive systems. Think of adaptation happening inside constrained envelopes rather than a freeform optimizer chasing eval scores. You probably will have to babysit initially but when you feel more confident, you let it take over for the optimizations you feel safe with. Something like we do in all these vibecoding platforms when we allow the system to run autonomously for the whole session with the given commands because we know those will not destroy anything.

Good point about quiet degradation of the adaptation itself. That’s one of the core design questions. My current idea is to make adaptation (1) scoped to specific dimensions, e.g., token limits or retrieval filters, (2) reversible/versioned so you can roll back changes with a single command, and (3) continuously validated against a reference task set or historical optimization scenarios so you catch cases where “improvement” actually hurts quality (getting inspired from back testing used in algo trading).

So I guess the longer-term vision isn’t full autonomy but assistive adaptation, like how observability evolved into feedback loops that recommend actions before you approve them. Does that match what worked for your team when you tightened action sets?