>Usually, the longer the AI works on something the crappier its output because that means the context is getting filled up.

This was definitely truer with older models but isn't necessarily the case now.

They frequently do other things apart from navel-gazing that take a lot of time but get good results, like spinning up subagents to solve some hairy task in a loop.

Stopping/distrusting long-running AIs is a habit I've had to unlearn myself.

I frequently get good results from a 30+ minute Fable session, when I've asked it to do something complex (e.g. run the QA tester in a loop and eliminate all crashes, one commit per crash fixed)

This is my experience as well. Lots of success with running one hour Fable sessions (one hour of API time). Not using a loop here, but there's often lots of documentation for Claude to go through first before building out the project.

The main failure point for me with long running Fable sessions now is just that it might hit a safety guardrail and downgrade to Opus midway.