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The convergence of fast-tracked quantum hardware manufacturing paradigms and artificial intelligence (AI) brings radical transformations to systemic cyber-defense architectures. The impending arrival of a Cryptanalytically Relevant Quantum Computer (CRQC) invalidates the baseline asymmetric mathematical problems protecting modern enterprise and sovereign state secrets. Algorithms like RSA, Diffie-Hellman, and Elliptic Curve variants face immediate deprecation under Peter Shor's algorithm, exposing historical databases globally to retroactive decryption vectors through "Harvest Now, Decrypt Later" (HNDL) operational doctrines. Simultaneously, highly distributed adversarial advanced persistent threats (APTs) deploy autonomous generative machine learning agents to identify zero-day exposures and circumvent perimeter security topologies. This paper introduces the Adaptive AI-Driven Post-Quantum Cyber Defense Framework (AAI-PQCDF). The design integrates the primary Federal Information Processing Standards (FIPS 203, 204, and 205) for post-quantum cryptography with custom, decoupled deep reinforcement learning (DRL) models and semantic transformer layers. AAI-PQCDF implements a programmatic zero-trust fabric across hybrid networks, hardening complex agent communication interfaces like the Model Context Protocol (MCP) using automated micro-segmentation routines. Our empirical validations chart performance penalties, network latency inflation factors, and packet structure dynamics across extensive high-throughput data streams, detailing an agile deployment matrix suited for large-scale enterprise infrastructures.