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This research aims to improve the deployment of large language models by transitioning from static prompting to dynamic multi-agent ecosystems.

February 2, 2026Open Access

Building Socially Grounded Multi-Agent LLM Systems Requires the Transition from Static LLM Prompting to Autonomous Multi-Agent Ecosystems

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This research aims to improve the deployment of large language models by transitioning from static prompting to dynamic multi-agent ecosystems.
Developed a Six-Level Continuum of agentic complexity.
Examined architectural features necessary for effective social simulation.
Outlined the collaboration between machine learning and social science for system design.
Identified limitations of static prompting in LLMs as related to social mechanisms.
Provided a clear framework for selecting architectural elements in agentic systems.
Promoted the importance of interdisciplinary collaboration for meaningful advancements.

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This framework highlights how evolving agentic systems enhances social interactions in LLMs, facilitating better outcomes.

Haase et al. (2026) studied this question.

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