Is it actually possible to build a model-agnostic persistent text layer that keeps AI behavior stable?

Reddit r/artificial / 4/16/2026

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Key Points

  • The post asks whether a persistent, model-agnostic text layer can reliably keep an AI system’s behavior stable over time, going beyond a standard system prompt.
  • It proposes that such a layer would need structured rules for conflict resolution, prioritization, and decision-making under conditions like context drift, contradictory instructions, and prompt injection.
  • The author questions whether current consistency is mostly determined by the underlying model and training, implying there may be a fundamental limitation to external layers.
  • Overall, the discussion frames stability as an architectural and behavioral constraint problem rather than a prompt-only approach.

Is it actually possible to define a persistent, model-agnostic text-based layer (loaded with the model each time) that keeps an AI system behaviorally consistent across time? I don’t mean just a typical system prompt, but something more structured that constrains how the system resolves conflicts, prioritizes things, and makes decisions even under things like context drift, conflicting instructions, or prompt injection.

Right now it feels like most consistency comes from training or the model itself, so I’m wondering if there’s a fundamental reason a separate layer like this wouldn’t hold up in practice.

submitted by /u/Intercellar
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