Multi-Agent Ethnography

Post-Conventional Anthropological Practice Through Human−AI Collaboration

Datos Bibliográficos

ID8130866
AutoresM Artz (0000-0002-3822-1429, Azimuth (United States), autor de correspondencia)
Año2026
Páginas1-19
Fecha de publicación2026-02-08
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAnthropological Forum (JOURNAL)
Identificadores de la revistaISSN: 0066-4677 • E-ISSN: 1469-2902
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00664677.2026.2614501
OpenAlexW7128402581
IdiomaEN
Citas recibidas7
Referencias citadas59

This paper introduces multi-agent ethnography (MAE), an approach that positions LLM-based AI agents as configurable collaborators within distributed human−AI research networks. MAE extends anthropology's tradition of methodological innovation—from multi-sited to multi-species ethnography—by incorporating AI agents as research partners across the entire research lifecycle. Drawing on empirical evidence demonstrating AI's capacity to function as a ‘cybernetic teammate’ (Dell'Acqua et al. 2025), I argue that purpose-built agents designed with anthropological considerations can extend research capabilities beyond what either humans or AI achieve independently. To demonstrate this approach, I present the AI Anthropology Toolkit, an MCP server implementation that coordinates specialised agents through conversational interaction, enabling researchers to direct complex analytical workflows using natural language. The current implementation comprises three agents for codebook generation, transcript segmentation, and coding with thematic analysis. This conversational approach enables systematic comparative analysis across multiple epistemological perspectives simultaneously, allowing individual researchers to access the analytical breadth typically achieved through collaborative team configurations. MAE's architecture supports agent coordination across research design, fieldwork, analysis, and dissemination. While data privacy, environmental costs, and algorithmic bias remain important considerations, the Toolkit demonstrates that anthropologically informed AI tools are feasible to build, accessible to use, and capable of augmenting ethnographic practice across research phases, expanding what individual researchers can achieve

Coding (social sciences · Empirical research · Ethnography · Function (biology · Thematic analysis · Workflow · Anthropology: Ethics, History, Culture · Architecture · Embodied and Extended Cognition · Language and cultural evolution