How to optimize LLM multi-agent service ecosystems?
Beyond the model: Key differentiators in large language models and multi-agent services
This paper argues that simply having the largest Large Language Model (LLM) is no longer the key to success in generative AI. Instead, the surrounding ecosystem – efficient data management, computational cost reduction, low latency, and robust evaluation frameworks – are the true differentiators. Specifically for multi-agent systems, this emphasizes the importance of optimizing how agents interact with data and each other, minimizing computational overhead, and ensuring effective communication and coordination within the system. Data management strategies like model-to-data movement and synthetic data become crucial for multi-agent learning and adaptation. Additionally, evaluation frameworks for assessing performance and reliability of interacting agents become paramount.