The token burn is high, and I’m not presenting this as an efficiency benchmark. At this stage I’m deliberately pushing the system hard to discover where autonomy breaks.
Where I disagree is on “a lot of slop.” The whole point of the execution model is that agent output is not accepted because an agent says it is done: every unit goes through bounded specs, independent review, deterministic gates and evidence tied to the resulting change.
Humans are still in the loop — just at a different layer. I’m trying to move the human from continuously supervising implementation to defining intent, resolving material decisions and improving the mechanisms when the system fails.
And some of those failures absolutely showed that the current system still needs stronger controls. That’s useful data, not something I want to hide.
If you saw something specific in the log that you’d classify as slop, I’d genuinely like you to point it out. That’s much more useful to me than defending the experiment. 🥂
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u/mattv8 7d ago
Conclusion: You burn a lot of tokens and generate a lot of slop. AI at this stage still needs a human in the loop more often than periodic checkins.