@Article{M-10360,
AUTHOR = {Patel, Nikit and Patel, Kaushal},
TITLE = {AI-Powered Meeting Assistant: An LLM-Centric, Agentic AI Approach for Automating Post-Meeting Workflows},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {3},
YEAR = {2025},
NUMBER = {1},
ARTICLE-NUMBER = {M-10360},
URL = {https://isrdo.org/journal/SRJSET/currentissue/ai-powered-meeting-assistant-an-llm-centric-agentic-ai-approach-for-automating-post-meeting-workflows},
ISSN = {2584-0584},
ABSTRACT = {Meetings are critical for collaborative decision-making, yet their outcomes are often underutilized due to inefficiencies in capturing, organizing, and tracking discussions. Traditional approaches to meeting documentation—manual note-taking or transcription-based solutions—fail to provide actionable insights, frequently leading to lost information, missed deadlines, and lack of accountability. With the advent of Large Language Models (LLMs) and agentic artificial intelligence (AI), it is now possible to design systems that not only document but also act upon meeting outcomes. This paper presents an LLM-centric AI-powered meeting assistant architecture that automates the complete post-meeting workflow. The system ingests audio or text transcripts, preprocesses them for clarity, and leverages LLMs to generate structured meeting summaries, extract actionable tasks, and create formal Minutes of Meeting (MoM). These outputs are converted into JSON format for seamless integration with task trackers, notification systems, and centralized dashboards. By adopting an agentic AI approach, the system enables proactive follow-ups, real-time reporting, and task completion monitoring through both tracker APIs and email-based confirmations. We provide a detailed literature review of speech-to-text technologies, LLM-driven meeting automation, and workflow orchestration, followed by a comprehensive description of the system’s architecture and methodology. The benefits, limitations, and challenges—including speaker diarization, overlapping speech, and task misclassification—are critically examined. The study highlights the transformative potential of agentic AI for enterprise productivity, while emphasizing ethical considerations and the importance of human oversight.},
DOI = {}
}