Where Should Agents Live? Energy-Memory Characterization of Agentic AI for the Edge-Cloud Continuum

As agentic AI workflows—where teams of LLMs coordinate to perform multi-step reasoning and tool invocation—become embedded in 5G and 6G telecommunication networks, operators face a critical placement dilemma. Existing metrics focus on single-model inference, leaving a gap in understanding the energy costs of distributed multi-agent execution. This paper asks: does distributing agents across the edge-cloud continuum incur meaningful energy costs, and how should network operators orchestrate these teams to maintain sustainability?

The authors introduce agentic-eCAL, a generalized energy metric for multi-agent workflows that accounts for LLM inference, tool execution, vector retrieval, and 7-layer OSI data transport. They develop a two-rate closed-form energy model that decomposes inference into compute-bound prefill and memory-bandwidth-bound decode phases. The framework is validated using hundreds of GPU benchmarks on NVIDIA A100 and H100 accelerators, 16 open-weight models, and 8 distinct orchestration topologies.

The study reveals that inter-agent text transport is negligible, accounting for less than 0.25% of total workflow energy across various network links. Instead, the primary energy driver is the super-linear growth of prompt prefill caused by accumulating conversational history across sequential agent steps. Furthermore, the authors find that while distributing agents is energetically cheap, the memory capacity of edge devices—specifically the key-value (KV) cache footprint—is the binding constraint on serving density. On an ETSI ZSM-aligned telco edge benchmark, multi-agent topologies increased energy consumption by up to 23.9x without consistently improving incident remediation success, suggesting that larger agent teams do not inherently provide better utility.

This research provides a foundational model for network operators to optimize the placement of agentic AI. By demonstrating that communication is essentially free compared to the compute cost of context processing, the paper shifts the focus of sustainable AI orchestration from minimizing data transmission to optimizing context management and agent selection. It warns that blindly scaling agent teams can lead to significant energy inefficiency without delivering proportional gains in task performance.