How to architect a seven-layer LLM multi-agent system?
The Athenian Academy: A Seven-Layer Architecture Model for Multi-Agent Systems
This paper proposes a seven-layer architectural model, inspired by Raphael's "The School of Athens," for building multi-agent systems (MAS) specifically for AI art creation. The layers range from basic multi-agent collaboration to complex synthesis of multiple agents into a unified, highly capable entity.
Key LLM-related points include: Exploiting the strengths of LLMs for inter-agent communication, task decomposition, and role allocation. Addressing the challenges of using different LLMs within a single MAS, such as style inconsistencies and information fragmentation. Exploring how multi-agent systems can leverage various LLMs (e.g., DALL-E 3, Midjourney, DeepArt) for distinct tasks within a collaborative art creation process, dynamically switching between them as needed. Focusing on using shared LLMs for enhanced agent coordination and efficient resource utilization. The paper emphasizes that LLMs are crucial for enabling complex multi-agent interactions and artistic expression, paving the way for more sophisticated AI-driven art.