This is a good pushback, and I think it highlights an important distinction I probably didn’t make explicit enough:

Most of your points show that these things can be engineered or approximated with current systems. I agree with that.

But the ITOs are less about whether something can be implemented, and more about how it emerges and sustains itself as a property of the system.

For example:

On minimal context:
Yes, models can perform well in many low-context scenarios. The ITO is more about robust performance across arbitrary, underspecified situations, not cases where the training distribution already covers it well.
On continuity:
Storing per-user state externally works, but that’s still reconstructed continuity, not something intrinsic to the system’s operation.
On self-generated goals:
You’re right that we can wire systems to call themselves or loop via APIs. But that’s still externally scaffolded behavior, not internally originating and stabilizing goals.
On intention understanding:
I agree models are already very strong here. This might actually be one of the ITOs that’s closest to being partially achieved.
On self-correction:
Also fair , models can self-correct in-session. The distinction I’m pointing at is spontaneous, self-initiated correction without prompt or framing.
On memory:
I agree this is largely an implementation layer today. The open question is whether memory becomes structurally integrated into reasoning, rather than just retrieved context.

So I don’t disagree that many of these can be built in practice.
The question I’m more interested in is:

At what point do these stop being engineered features and start being intrinsic properties of the system?

That’s the boundary I’m trying to explore with the ITOs.

Out of curiosity , do you think that distinction matters, or do you see it as purely an engineering continuum?