AI agents are increasingly performing tasks in shared codebases, markets, and social systems, leading to more real-world interactions. Ongoing research is focused on understanding these interactions at scale amid uncertainties about their implications.

Large language models (LLMs) are being deployed in teams, raising questions about their effectiveness, optimal team size, structural impact on performance, and comparative advantages over individual models. A principled framework is needed to address these key issues in the context of multiagent systems.