Published Paper Details:

In this paper, we describe the design of an Automated Content Platform which employs multi-agents systems for content collection, processing, and distribution. The described work merges domain knowledge of Large Language Models (LLMs) with practical deployment approaches that are bounded by scalability, modularity, and efficiency. Our approach followed a stepwise procedure starting with a web-scraping-based content acquisition, then proceeding to content summarization with several models including Gemini by Google, followed by distribution through Telegram and email. The components of the architecture are organized into streams, where each is an independent agent of the system, utilizing FastAPI and Streamlit interfaces, MongoDB for data storage, and so forth. Results prove that the platform substaially meets the requirements for efficient content acquisition from multiple sources, automated summarization of a wide range of topics, and prompt distribution through numerous channels. User testing with 25 participants indicated 87% satisfaction with the quality of summaries provided and 92% with usability of the platform. The measured response times were 3.2 seconds to retrieve content and 4.5 seconds to provide a summary of the retrieved content. The work provided in this document adds to the existing body of knowledge by showing that automated content processes can be further enhanced by multi-agent frameworks focusing on system agility and flexibility in operations.