AI ethics is everywhere. Execution models are nowhere. So I built one

This is a really interesting direction, and I genuinely like what you’re exploring.

The idea of persistent context, character-based interpretation, and especially self-conditioning over time is meaningful. It clearly tries to address a real limitation of current systems — that responses are often stateless, shallow, or inconsistent. Your approach is trying to make systems “feel” more grounded and internally coherent, which I think is valuable.

I also agree with your first point: JSON itself is not the essence. The container format — whether JSON or prose — is not what fundamentally changes the system. In that sense, we are aligned. What matters is not the syntax, but whether the system has enough structure to reason about actions.

Where I see a difference is in what layer the problem is being addressed.

What I’m proposing is not about making the model better at interpreting or following constraints after the fact. It is about defining, before execution , whether an action should be allowed to happen at all.

In many current systems, constraints exist, but they are embedded as prompts, guidelines, or narrative context. The model interprets them, but ultimately still decides probabilistically. There is no clear distinction between:

• “this is unsafe”
• and “this should not be executed”

The model may describe the risk — but still proceed.

That’s the gap I’m trying to address.

Regarding your second point — self-enforcing or penalty-based constraints — I think that’s an interesting and potentially powerful direction. But I see it as a different layer, not a replacement.

Those mechanisms operate after behavior emerges :

• the agent acts
• then learns, adapts, or is penalized

What I’m focusing on is before that point :

• defining whether the action should even be eligible for execution

In physical systems, this distinction becomes critical.

If an AI system turns on a heater without water, or activates a device in the wrong context, the failure is not just informational — it is already an event. In those cases, “learning from failure” is often too late.

So I see this as complementary rather than competing:

• Your direction explores how systems adapt and enforce constraints over time
• My direction defines a pre-execution validation layer that determines whether an action is allowed in the first place

In other words:

This is not about how to make the model follow rules better. It is about making the system explicitly decide whether an action should run at all.

That’s the layer I believe is currently missing.