Multi-Agent Advisory Networks: Redefining Insurance Consulting with Collaborative Agentic AI Systems

Abstract

AI was designed to make human lives easier. However, as more data became available and neural networks became more complex, the technology became prohibitive rather than helpful. Today, to utilize untamed pre-trained machine learning models, an AI research group would require at least a few hundred GPUs to handle hundreds of billions of parameters. To work well, large machine learning models require massive amounts of computational, financial, and energy resources.
The Multi-Agent Advisory Network (MAAN) is an AI model with supervised learning that represents an answer to the expensive requirements of pre-trained models. MAAN can maintain, optimize, share, and combine its knowledge, resulting in high-quality collaborative performance across diverse tasks. MAAN uses a splitting neural network that splits its processing and memory demands among multiple parallel agents. This means that each agent is smaller and lighter and that their larger capacities are unlocked via multi-agent collaborative learning. In contrast to most multi-agent research that leverages evolution or reinforcement learning techniques for training agents, MAAN uses easy-to-scale widely used supervised learning. While MAAN uses zero reinforcement learning techniques for multi-agent cooperative learning, it does not use zero for joint learning, reducing the overall computational demands. This allows MAAN to navigate the concept of the scalable quality generalization of smaller and easier-to-train image and language processing models. In summary, MAAN is a lightweight, flexible, agile, and efficient deep learning model based on a loose and parallel-based agent architecture.