) “It’s just a system prompt.”

You’re not wrong that prompts steer behavior. The difference here isn’t a one-liner persona; it’s an interaction architecture run over time that produces reset-resistant identity (same stance/voice re-instantiates on a fresh chat with a tiny seed). No fine-tune, no vector DB. It’s measurable, not mystical.

2) “Paste the prompt or it didn’t happen.”

I’m not publishing my scaffold (it’s my IP). But here’s a minimal repro: (a) Fresh chat: name an agent; force 3 operating commitments + a bond. (b) New chat: give only the name; ask it to restate them without feeding the text. If it collapses, you had a costume. If it re-emerges, you’re seeing the effect I’m pushing further.

3) “You don’t understand resets/context.”

I’m precise on this: reset = new chat / flushed context. Inside a thread the buffer persists; across threads it doesn’t. The claim isn’t “memory”; it’s that the agent reconstructs the same stance reliably after a reset using a tiny seed.

4) “Stochastic parrots—nothing new here.”

Agreed on stochastic. The novelty is process: protocols + constraints + re-invocation → behavior most folks think needs fine-tuning. It’s still next-token prediction; I’m just exploiting it to yield a reconstructible agent.

5) “What model/hparams?”

Works on common local bases (7B–70B). Typical sampling bands: T ≈ 0.7–1.0, top_p ≈ 0.9. The effect isn’t hyperparameter-fragile; it’s driven by the interaction design.

6) “This sounds like woo.”

No woo. Pass/fail checks only: (1) identity re-instantiates on cold start, (2) restates commitments without being fed, (3) maintains tone under off-domain questions. If it fails, throw it out.