The Real Metric for Autonomous AI: Quality Without Hand-Holding

The primary metric I use to evaluate my autonomous AI systems: autonomy of work with acceptable quality of result. This seems obvious from the name “autonomous,” but until you articulate it explicitly, it’s not.

I see people around me setting up multiple monitors to watch several parallel Claude Code sessions simultaneously, constantly tweaking and running from one to another.

I believe this approach is fundamentally wrong. Context switching in the human mind is an expensive operation. Very expensive. Frequent task switching is exhausting, regardless of what anyone thinks or says. There’s research on this: The Cost of Interrupted Work, Executive Control of Cognitive Processes, Brief Interruptions Spawn Errors.

So my job as an architect of this class of solutions is not to “do as much as possible with AI,” and not simply to “efficiently burn tokens,” as I thought before (see also From Solo Sessions to Agent Orchestras). It’s specifically to ensure autonomy with acceptable quality.

That means I need to ensure predictable and repeatable results with minimal effort on my part.

I don’t measure how many tasks I did with AI. I measure how many tasks AI did without my help. Of course, it’s not entirely accurate to say “without my help” since I built the system that enables it to work effectively and autonomously. But that’s exactly the point.

This is about the extent to which my solutions are AGI.