Objective-Layer AI: A Structural Definition of Endogenous Evaluation in Artificial Systems

Contemporary artificial intelligence systems are typically characterised in behavioural terms—generation, autonomy, or task performance. This paper argues that such classifications obscure a more fundamental structural distinction. Across machine learning, finance, and governance, systems optimise behaviour relative to objective functions that remain external to their domain of operation (ℒ ∉ 𝒪(S)). As optimisation becomes an increasingly insufficient locus of differentiation—through model convergence, alpha compression, and system integration—the relevant shift is from optimisation to evaluation. The paper introduces Objective-Layer AI as an architectural category in which the objective function becomes an admissible object of system-level operation (ℒ ∈ 𝒪(S)), subject to externally specified, coherence-preserving constraints. This condition is distinguished from meta-learning and adaptive optimisation, which operate over parameter space under fixed objectives. Representation or refinement of evaluative criteria does not constitute operation on ℒ unless such transformations are themselves admissible within the system’s operation. Using financial markets as a structural test case, the paper shows that optimisation under fixed evaluative criteria is subject to binding constraints, such that persistent differentiation cannot arise from optimisation alone. It further argues that a subset of out-of-distribution failures—where local optimisation succeeds while global outcome alignment breaks down—constitutes a signal of evaluative mis-specification rather than purely statistical error. The analysis has governance implications. Where ℒ remains external, control operates through specification and supervision of optimisation; where ℒ ∈ 𝒪(S), governance shifts to the design and oversight of admissibility constraints governing evaluative transformation. A limited claim concerning agency follows: systems operating under fixed evaluative criteria remain derivative with respect to evaluation. The inclusion of ℒ within 𝒪(S) identifies a necessary, though not sufficient, condition under which this dependence may be relaxed. Objective-Layer AI is thus proposed not as a description of current systems nor as a sufficient condition for general intelligence, but as a minimal architectural criterion for distinguishing optimisation-bound systems from those in which evaluation becomes operational. This paper forms part of a broader research program on evaluative structure, epistemic agency, and the governance of AI systems. It develops a restricted architectural application of that program by introducing “Objective-Layer AI” as a category in which the objective function becomes an admissible object of system operation. The paper does not restate the general theory of epistemic generativity, but applies it to concrete questions of system design, finance, and governance. The fuller formal account of epistemic generativity, including the condition under which evaluative standards become objects of system-level operation (ℒ ∈ 𝒪(S)), is developed in Kahl (2026a, §§1–4).