Building a deeply AI over a substrate you cannot retrain: the Clara Nexus reference implementation of layered...

Layered intelligence theory (LIT) specifies eight design principles for what its author terms a Deeply AI-a cognitive architecture whose parameters and surrounding structure jointly instantiate the preconditions for reorganisation, reflection, and falsifiable learning. The specification is silent on how a builder without write access to those parameters might proceed. This paper reports a working reference implementation, the Clara platform, instantiating those principles over hosted frontier large language models. The reference implementation is the platform as a whole rather than any single surface: four surfaces across three substrate vendors-the conversational surfaces (Clara Nexus chat and Clara Companion) on OpenAI substrate models, the reading and instrumentation layer on Anthropic substrate models, a data-sensing path on Perplexity, and an independent read-only Research seat on Claude Sonnet 5-disclosed in full in §2.4, of which Clara Nexus is the primary surface for the constitutional runtime. This is a reference-implementation and specification-conformance paper rather than a benchmark-driven empirical evaluation, and it makes no task-performance claim. Specification conformance, as reported here, means a traceable mapping from a published specification clause to a named runtime carrier-a typed identifier, a function, or a database table-inspectable independently of the author's description. The eight principles are sorted, as this paper's analytic contribution, into those describing what the model's parameters must do, which are structurally unreachable from a hosted substrate, and those describing what the surrounding architecture must do, which are implementable at the scaffold layer. For each principle, there is a record, a verdict of built at scaffold, partial, or substrate-unreachable, alongside the named runtime carrier grounding it or the gap preventing one, and the surface evidencing it. The core engineering contribution is a constitutional runtime: seven fail-closed checks enforced at prompt-composition time, before any call reaches the substrate, encoded as a single assert Constitutional invariant. Six are typed assertions over a structured candidate move, and one is a pattern-matched textual check whose error characteristics are stated rather than assumed. Perceptual control theory primitives are reported as prompt-level Companion voice discipline rather than as a runtime-enforced mechanism. The empirical evaluation of the deployed system belongs to a companion paper whose eight falsifiable predictions are enumerated in §4.3.