The increasing demand for short-form video content on platforms such as YouTube Shorts, Instagram Reels, and TikTok has made content creation a time-consuming and complex process. Traditional workflows require multiple tools and manual effort for tasks such as scriptwriting, media selection, voice generation, and video editing, which limits scalability and consistency. To address these challenges, this paper proposes an end-to-end AI-powered framework for automated short-form video generation. The system is built using a modular, multi-agent architecture that integrates large language models (LLMs), multi-modal media retrieval, voice synthesis, and a schema-driven video editing framework. This approach enables seamless coordination between different stages of content creation within a unified pipeline. The proposed system incorporates a dynamic content engine capable of generating scripts, captions, and media queries, along with an asset retrieval mechanism that collects images and videos from multiple external APIs. An audio processing module ensures proper synchronization and maintains duration constraints suitable for short-form content. In addition, an API tracking and cost analysis component is included to monitor resource usage and improve efficiency. By combining agentic AI principles with multi-modal processing and automated orchestration, the proposed framework provides an efficient and scalable solution for modern content creation.
M et al. (Tue,) studied this question.
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