Communication-Efficient Networking for Distributed Agentic AI: A Systematic Review of Protocols, Coordination, Latency and Energy Consumption

Abstract

Distributed agentic artificial intelligence increasingly relies on networked groups of autonomous agents that exchange observations, intentions, plans and task states to achieve collective goals. This systematic review synthesizes peer-reviewed studies published between 2023 and 2026 on communication-efficient networking for distributed agentic AI, multi-agent reinforcement learning and networked autonomous systems. Following PRISMA 2020, studies were identified from major scholarly databases and analyzed thematically across communication protocols, message compression and selection, semantic communication, coordination mechanisms, latency reduction, energy efficiency and deployment challenges. The evidence shows that selective engagement, graph-based compression, delay-aware communication, implicit consensus and value-of-information scheduling can reduce redundant exchanges while maintaining coordination quality. Semantic and edge–cloud approaches further lower payload size and local computational demand, although their benefits depend on channel conditions, resource availability and task placement. Persistent limitations include scalability, protocol interoperability, security and privacy risks, inconsistent energy reporting, dependence on simulated environments and limited standardization of evaluation metrics. The review concludes that communication efficiency should be treated as a joint optimization problem involving bandwidth, latency, computation, energy and task performance. Future research should prioritize interoperable protocols, adaptive communication topologies, secure message exchange, realistic testbeds and standardized reporting frameworks for dependable, scalable and sustainable distributed agentic systems at scale.