Multi-Agent AI Architecture for Autonomous Retail Operations: Coordinating Intelligent Agents Across Cloud, DevOps, and Enterprise Platform

Abstract

A multi-agent architecture supporting autonomous retail operations must accommodate dedicated functions for cloud, DevOps, and enterprise platforms. The deployment principle for these Cloud-native, DevOps-enabled, Enterprise Integrated Systems follows a federated approach to enable scalability, fault tolerance, and data retention while flexibly supporting latency-sensitive functions. Within each domain, intelligent agents, orchestrators, negotiators, and enterprise services interact to drive seamless operations. Coordinating agents at different levels and across domains address monitoring, lifecycle management, deployment, adaptation, incident response, and other cross-layer functions. Clear data governance policies covering provenance, quality, control, privacy, and retention capture the full data lifecycle, enabling compliance with industry- and scenario-specific regimes. Multi-agent systems support Cloud-native, DevOps-enabled, Enterprise Integrated Systems with dedicated functions for cloud, DevOps, and enterprise platforms. The federated deployment principle accommodates scalability, fault tolerance, and data retention while flexibly supporting latency-sensitive functions. Intelligent agents, orchestrators, negotiators, and enterprise services drive seamless operations within each domain. Coordination across layers and domains tackles monitoring, lifecycle management, deployment, adaptation, incident response, and other cross-layer functions. Data governance policies covering provenance, quality, control, privacy, and retention capture the full data lifecycle, enabling compliance with industry- and scenario-specific regimes.