This paper presents the Limitation and Constraint Duality (LCD), a deterministic information geometric framework that formally separates an AI agent’s intrinsic capabilities from the externally imposed boundaries on its admissible behavior. Grounded in Knowledge Action Duality (KAD) Theory, the framework establishes a rigorous distinction between an agent’s ontological state space and its deontic feasibility region. The Limitation Set L (S) is defined as an ontological reachability manifold ML, derived from the agent’s binary substrate through Binary Logical Orchestration of Context (BLOC) Theory. Modeled as a 17 dimensional Fisher geometric simplex, ML represents the agent’s absolute ”CAN SPACE”. The Constraint Set C (S) is introduced as a deontic stability subregion: C (S) = x ∈ ML | Fs (x, t) ≥ ϵ, capturing the externally imposed ”MAY SPACE” defined by a safety functional Fs. By embedding both sets within a unified information geometric measure space (ML, gF, μ), safety is characterized as a geometry induced risk field, where the stochastic failure rate λ is governed by the geodesic distance to the admissibility frontier ∂C. The model incorporates Mosaic Aging and SIL (t) temporal hazard dynamics, capturing the time varying decay of the admissibility region under operational drift. A deterministic proof of the Indestructible Relation C (S) ⊆ L (S) is established, demonstrating that constraints can restrict but never expand or modify the ontological extent of an agent’s capabilities. Finally, the framework is operationalized through the Data Attribute Importance Standard (DAIS-10), showing how the synthesis of BLOC and KAD yields a deterministic alternative to probabilistic guardrails. This unified formulation provides a robust protocol for the design, orchestration, and functional safety of autonomous AI agents operating in high stakes environments.
Usman Zafar (Sun,) studied this question.