Multi-agent AI systems are transforming business operations by distributing complex tasks among specialized AI agents that collaborate toward common goals. The success of these systems depends on effective communication between agents, as inefficient communication can lead to business costs such as delayed responses, wasted computing resources, and poor output quality. To measure communication efficiency, organizations need to understand the foundational communication architectures that define how multiple AI agents interact, including direct and indirect communication patterns, centralized and decentralized architectures, and synchronous versus asynchronous messaging. Effective measurement of communication efficiency encompasses factors from latency and throughput to message complexity and coordination, and tools like Galileo provide industry-specific measurement templates and compliance monitoring to support the development of more efficient multi-agent AI systems that communicate effectively regardless of implementation complexity.