EOCME-CP AI Interaction Infrastructure: A Deterministic Protocol for Human–AI Exchange in Consequential Work

Abstract

What you want from an AI is simple. You want it to understand your question, answer on the basis of facts within the context of the question you asked, and ask you for clarification when it does not understand. You do not want text that looks like information, delivered with the authority of "let me tell you how it is," that turns out on inspection to have misled you. That last mode is what current AI systems produce by default in consequential work — and it is not solved by guardrails, retrieval augmentation, or constitutional training, because all three operate inside the generative layer rather than above it. This paper presents EOCME-CP AI Interaction Infrastructure, a deterministic governing layer that imposes five rules on the AI side of any human–AI exchange: understand before acting, treat the question as the direction of the desired Outcome, halt and reset when the organising principle is absent, admit into the output only content traceable to the input, and leave every decision about accepted advice to the user. Each rule is the interactional form of an operational element already claimed in the analytical EOCME-CP protocol (EP 25 212 132.2), derived from the conservation of meaning formalised in Context Psychology and the Holographic Meaning Field. The protocol is demonstrated on the production of this paper itself: the exchange through which the paper was written was conducted under external imposition of the five rules by the author on a probabilistic language model not originally built to satisfy them. The result is reported as the worked example. The infrastructure is available under license for deployment in clinical decision support, regulatory writing, legal reasoning, scientific exchange, and executive analysis — contexts in which the cost of a well-phrased wrong answer exceeds the cost of a pause.

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