Cloud-Native Distributed LLM Platforms For Multi-Agent Conversational AI And Enterprise Architecture

Abstract

The rapid adoption of conversational artificial intelligence in enterprise environments has intensified the need for scalable, reliable, and governable large language model (LLM) infrastructures. This study investigates cloud-native distributed LLM platforms designed to support multi-agent conversational AI within enterprise architectures. Using a design science and experimental evaluation approach, the research analyzes how agent specialization, cloud-native orchestration, and governance mechanisms influence system performance, conversational quality, and operational resilience. The results show that multi-agent configurations significantly reduce response latency, increase throughput, and improve task completion accuracy compared to single-agent deployments. Autoscaling and container orchestration enable stable performance under increasing user load, while integrated governance controls enhance policy compliance with acceptable performance trade-offs. The findings demonstrate that cloud-native multi-agent LLM platforms effectively balance scalability, quality, and governance requirements, offering a practical architectural model for enterprise-grade conversational AI deployment.