The rapid growth of social media platforms has significantly increased the complexity of managing digital content across multiple channels. Organizations and content creators increasingly require automated systems that can coordinate publishing, interaction handling, and contextual response generation across heterogeneous platforms. Existing content management tools often provide limited workflow integration and insufficient support for intelligent interaction management. This study proposes a workflow-oriented architecture for automated multi-platform content management that integrates large language models (LLMs) with workflow automation mechanisms. The proposed system is built upon a unified conceptual framework termed CLAW (Content–LLM–Automation–Workflow), which coordinates content resources, contextual information, AI-based response generation, and workflow orchestration within a single operational structure. The architecture combines centralized backend services, workflow automation, and LLM-based contextual interaction capabilities to support synchronized publishing, multi-account management, and automated response handling across social media platforms. A functional prototype was implemented to demonstrate the feasibility of the proposed architecture. The results show that integrating LLM-based contextual reasoning with workflow orchestration enables more coherent, scalable, and adaptive content management operations. The proposed system provides a practical foundation for future research and development of AIenabled multi-platform content management systems