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This paper presents a multi-agent organizational framework in which human agents, LLMs, and specialized agents (narrow AIs) collaborate via dynamic, topic-based group formation. Topic-driven interactions enable agents to coalesce around evolving interests, supported by threshold-based protocols for temporal adaptation, topic emergence, and participation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Within our framework, human agents guide the overall system objectives, while consultant agents (LLMs) provide semantic analysis and mediation, and specialized agents perform focused domain tasks. By leveraging automated topic modeling, the approach eschews rigid ontologies and instead supports adaptive and interpretable content management. Mathematical properties ensure system coherence\u2014across roles, tasks, and timescales\u2014while allowing the natural evolution of interests and groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We illustrate the framework\u2019s versatility with example scenarios in emergency response, healthcare research, and financial decision-making, emphasizing how human decision-makers, LLM-based consultants, and specialized worker agents jointly fulfill complex goals through transparent topic alignment and threshold-driven coordination. This formalization advances human-computer interaction as a multi-agent phenomenon that integrates human insight with the strengths of next-generation AI models in a cohesive, evolving system.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10791-025-09667-2","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T10:47:15Z","timestamp":1751539635000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An organizational theory for multi-agent interactions integrating human agents, LLMs, and specialized AI"],"prefix":"10.1007","volume":"28","author":[{"given":"Uwe M.","family":"Borghoff","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Bottoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Remo","family":"Pareschi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"9667_CR1","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3390\/sci6040071","volume":"6","author":"R Pareschi","year":"2024","unstructured":"Pareschi R. 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