Transdisciplinary Architectural Intelligence (TAI): Governing Meaning Formation in Autonomous AI Architectures (v1.0 Canonical Release)

Transdisciplinary Architectural Intelligence (TAI) is proposed as the cognitive-architectural layer responsible for governing meaning formation in autonomous AI systems. As AI-native architectures evolve toward distributed agentic execution, system failures increasingly arise not from execution errors but from semantic inconsistency, ontological ambiguity, and upstream goal drift. Existing constitutional AI frameworks successfully regulate downstream execution through invariant preservation, schema compilation, constitutional constraints, and cybernetic governance, yet they assume that the semantic foundation itself is already coherent. This work formalizes TAI as the transdisciplinary capability that discovers, synthesizes, stabilizes, and evolves constitutive meaning structures prior to executable architectural design. TAI integrates human conceptual reasoning, cross-domain knowledge synthesis, architectural abstraction, semantic constitution, and recursive governance into a unified framework capable of generating executable constitutional architectures. The paper positions TAI as the prerequisite layer for Constitutional AI, AI-native operating systems, distributed enterprise architectures, and future Artificial General Intelligence (AGI) governance by addressing semantic coherence before computational execution.