Adaptive systems frequently improve through optimization. Machine learning models are trained, organizations refine processes, and biological populations adapt through selection. Such improvements are often interpreted as evidence that continued optimization can indefinitely increase performance. This paper shows that optimization operates within structural limits. Every system functions under a rule class that determines which states are reachable through admissible updates. This rule class therefore defines a boundary for achievable performance. We formalize this boundary as the structural optimization potential: the best attainable performance within the reachable state region induced by a rule class. Optimization processes can approach this boundary but cannot surpass it without modifying the rule class itself. The framework distinguishes two mechanisms of improvement: state optimization within a rule class and structural transition that modifies the rule class. Persistent recurrence pressure near the structural limit induces kognetic load, which motivates structural transition through rule-level operators (Kognems). The result provides a structural explanation for improvement plateaus and clarifies when progress requires rule change rather than continued optimization. Intellectual Property & Licensing The KOGNETIK Research Series is released under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0). All scientific works within the series may be cited, shared, and adapted for non-commercial research purposes with proper attribution. Commercial use—including consulting, advisory services, integration into commercial platforms, monetized training, certification, or system-level deployment—is not permitted under this license and requires a separate written agreement. Full license text:https://creativecommons.org/licenses/by-nc/4.0/ For licensing, partnerships, translations, or applied development inquiries:research@kognetik.dehttps://www.kognetik.de ORCID: https://orcid.org/0009-0000-8544-4847 Kognetik Series Information KOGNETIK — Minimal Operator Definition of Reflexivity (Ψ = ∂S/∂R) Reflexivity as structural rate-of-change:Ψ = ∂S/∂R measures structural drift under recurrence. Process, not state:Reflexivity specifies a transformation rule rather than a content or level. Domain-independent operator:Applicable across biological, cognitive, artificial, social, industrial, and geophysical systems. Non-ascriptive and empirically testable:Ψ enables comparative analysis of systems via observable structure and recurrence. Higher-order phenomena as specifications:Learning, adaptation, consciousness, governance, and identity are structured regimes of Ψ.