Abstract

Autonomous AI systems, termed "agentic AI," demonstrate remarkable capabilities in pursuing complex objectives with minimal human oversight yet introduce critical challenges: output hallucinations, opaque decision making, and governance fragility. This conceptual paper examines Retrieval Augmented Generation (RAG) as a strategic architectural pivot embedding traceability directly into generative processes. The discussion contextualizes how agentic autonomy amplifies both innovation potential and systemic risk within sociotechnical ecosystems, emphasizing the importance of embedding auditability and interpretability as intrinsic design features, than retrospective controls. It illustrates how RAG driven architectures can reconcile operational efficiency with regulatory compliance through adaptive evidence generation and contextual reasoning. By synthesizing academic literature with industry frameworks, this research proposes actionable pathways for organizations transitioning towards accountable AI systems. This analysis, grounded in the Bright Origin Perspective, contributes a unified framework positioning RAG not as a technical enhancement but as an ethical imperative for sustainable digital transformation, culminating in responsible AI.

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