Multi-Agent Systems for Action Item Extraction from Meeting Transcripts: A Comprehensive Review
1. Haruto Tanaka, Kansai Institute of Advanced Science and Technology, Student, Japan
2. Grant Thompson, Kansai Institute of Advanced Science and Technology, Professor, Japan
The rapid growth of
digital collaboration platforms has led to an unprecedented increase in
recorded meetings and conversational data. These interactions are commonly
preserved as textual transcripts through automated transcription technologies.
While transcripts provide a complete record of discussions, their unstructured
and verbose nature limits their direct usefulness for organizational follow-up
and decision-making. Among the most valuable outcomes of meetings are action
items, which capture tasks, responsibilities, and commitments that must be
executed after the discussion ends. This review paper examines the role of
multi-agent artificial intelligence systems in extracting action items from
meeting transcripts. By synthesizing recent advances in multi-agent frameworks,
large language models, conversational analysis, and meeting intelligence, the
paper highlights how agent-based decomposition improves accuracy,
interpretability, and scalability in action item extraction. The review also
discusses architectural patterns, coordination strategies, and application
contexts, offering a structured understanding of how multi-agent approaches
address the limitations of traditional single-model pipelines.
Multi-agent systems meeting transcripts action item extraction large language models document intelligence conversational AI
Multi-agent systems offer
a robust and conceptually well-aligned approach for extracting action items
from meeting transcripts by distributing the complex task of conversational
understanding across specialized, cooperating agents. Unlike traditional single-model
summarization pipelines, multi-agent frameworks enable deeper reasoning about
intent, commitment, and context, which are essential for accurately identifying
actionable outcomes embedded within unstructured dialogue. By separating
comprehension, intent detection, validation, and consolidation into distinct
yet coordinated processes, these systems reduce ambiguity, improve
interpretability, and enhance the reliability of extracted action items.
This review highlights
that the true strength of multi-agent architectures lies not only in task
decomposition but also in their coordination and reasoning strategies, which
mirror human analytical practices such as iterative review, cross-checking, and
contextual refinement. When applied to meeting transcripts, such capabilities
support clearer accountability, more effective follow-up, and stronger
alignment between conversational decisions and organizational execution. As
conversational data continues to grow in scale and importance across
professional, academic, and high-stakes domains, multi-agent action item
extraction is positioned to become a foundational component of next-generation
meeting intelligence and document understanding systems, bridging the gap
between discussion and decisive action.
1. -
The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.
The authors did not receive any specific grants from funding agencies in the public, commercial, or non-profit sectors for the research, authorship, and/or publication of this article.
Not applicable.
All authors declare the absence of any conflicts of interest.
I am grateful for the expertise and help provided by all who contributed to this study and manuscript, and for the comments from anonymous reviewers.
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