The increasing complexity and digitalization of modern vehicles have transformed the driving environment into a multimodal information ecosystem. Although recent advancements in embedded infotainment systems and natural-language interfaces have enabled limited conversational interaction, current in-vehicle assistants remain fundamentally reactive and constrained: they operate on shallow input modalities, lack contextual awareness, provide inconsistent reasoning, and cannot adapt to user behavior or dynamic driving conditions. This work proposes a comprehensive framework for designing an intelligent Intelligent Personal Vehicular Agent (IPVA) capable of multimodal perception, contextual reasoning, safety-aware action, and continuous learning. The proposed IPVA integrates vehicle telemetry, driver voice commands, technical documentation, structured tables, and manual-derived imagery through a hybrid Retrieval-Augmented Generation (RAG) pipeline optimized for automotive knowledge domains, and is designed to interface with existing Advanced Driver-Assistance Systems (ADAS) by providing complementary high-level reasoning and learning capabilities. Additionally, a Perception–Reasoning–Action–Learning (PRAL) architecture is introduced to clearly define the computational and behavioral responsibilities of an intelligent vehicular agent. The system incorporates token-level streaming, federated learning for driving-style classification, multimodal retrieval using BM25, dense embeddings, FAISS indexing, and adaptive policies aligned with automotive safety standards. This paper presents the architectural formulation, design motivations, cognitive workflow, learning paradigms, safety constraints, and implementation considerations required to transition from conventional assistants toward a fully adaptive vehicular agent that augments perception-and-control-oriented ADAS stacks.