Agentic AI-Based Smart Video Editor: A Multi-Agent Framework for Automated Video Summarization and Editing

  • Unique Paper ID: 186042
  • PageNo: 180-183
  • Abstract:
  • Automated video editing has emerged as a transformative application of artificial intelligence, addressing the growing demand for efficient content creation in the era of digital media. This study presents an agentic AI framework for intelligent video editing that integrates multi-agent architectures, vision-language models, and reinforcement learning techniques. We leverage pre-trained Vision-Language Models (VLMs) to extract semantic features from video content, enabling context-aware editing decisions. The system employs a multi-agent architecture where specialized agents collaborate to analyze video content, generate editing recommendations, and execute automated editing operations. Our framework incorporates reinforcement learning-based editing strategies to optimize sequential decision-making in video composition, moving beyond rule-based approaches to learn adaptive editing patterns from professional demonstrations. The implementation utilizes a Streamlit-based interface with MoviePy for video processing, demonstrating real-time analysis and editing capabilities. This integrated approach addresses both the technical challenges of automated video editing and the creative aspects of producing professional-quality content through intelligent agent collaboration and learned cinematographic principles.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{186042,
        author = {Tanmay Avinash Shigvan and Adarsh Jagadish Poojary and Kunj Shirish Raut and Ayush Ramesh Shetty and Neha Nandurkar},
        title = {Agentic AI-Based Smart Video Editor: A Multi-Agent Framework for Automated Video Summarization and Editing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {180-183},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186042},
        abstract = {Automated video editing has emerged as a transformative application of artificial intelligence, addressing the growing demand for efficient content creation in the era of digital media. This study presents an agentic AI framework for intelligent video editing that integrates multi-agent architectures, vision-language models, and reinforcement learning techniques. We leverage pre-trained Vision-Language Models (VLMs) to extract semantic features from video content, enabling context-aware editing decisions. The system employs a multi-agent architecture where specialized agents collaborate to analyze video content, generate editing recommendations, and execute automated editing operations. Our framework incorporates reinforcement learning-based editing strategies to optimize sequential decision-making in video composition, moving beyond rule-based approaches to learn adaptive editing patterns from professional demonstrations. The implementation utilizes a Streamlit-based interface with MoviePy for video processing, demonstrating real-time analysis and editing capabilities. This integrated approach addresses both the technical challenges of automated video editing and the creative aspects of producing professional-quality content through intelligent agent collaboration and learned cinematographic principles.},
        keywords = {Agentic AI, Video Editing, Vision-Language Models, Multi-Agent Systems, Reinforcement Learning, Automated Content Creation, Deep Learning, Video Processing.},
        month = {October},
        }

Cite This Article

Shigvan, T. A., & Poojary, A. J., & Raut, K. S., & Shetty, A. R., & Nandurkar, N. (2025). Agentic AI-Based Smart Video Editor: A Multi-Agent Framework for Automated Video Summarization and Editing. International Journal of Innovative Research in Technology (IJIRT), 12(6), 180–183.

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