When do specialist agents outperform generalists?
Predicting Multi-Agent Specialization via Task Parallelizability
This paper explores when specialized agents (each performing a distinct subtask) are more efficient than generalist agents (each capable of performing all subtasks) in multi-agent systems. It introduces the concept of "task parallelizability"—the ability to execute subtasks concurrently—as a key factor influencing specialization. When tasks are highly parallelizable, generalist agents can work independently and efficiently. Conversely, limited parallelizability due to resource or spatial bottlenecks favors specialist agents performing complementary subtasks.
For LLM-based multi-agent systems, this research suggests that environment design, including resource allocation and spatial layout, can significantly impact emergent agent specialization and overall efficiency. Rather than enforcing specific roles through algorithms, developers can influence agent behavior by manipulating the environment. Furthermore, the study indicates that larger state spaces, which increase exploration demands, can lead to specialization even when generalist strategies are theoretically more efficient. This highlights the challenge of achieving optimal generalist policies in complex environments, suggesting a potential for leveraging environmental design to improve policy diversity and efficiency. The "task parallelizability" concept offers a useful framework for optimizing agent behavior and environment design in LLM-based multi-agent apps.