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This paper proposes A-LEMS, a framework that redefines AI energy accounting from per-inference to Energy per Successful Goal (EpG), and introduces the Orchestration Overhead Index (OOI) to measure energy costs of multi-step orchestration in agentic systems. Empirical results show agentic workflows consume 4.33× higher mean energy per goal than linear baselines, but OOI can invert for tool-augmented tasks, demonstrating goal-level accounting is necessary.
This paper presents an end-to-end energy accounting framework for LLM distillation pipelines, measuring stage-wise energy costs and constructing energy-quality Pareto frontiers to reveal previously ignored teacher-side costs.