Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
Current AI energy benchmarks measure consumption per model invocation. For agentic systems, where a single user goal may trigger multistep orchestration, tool calls, retries, and failure recovery, the invocation count is an implementation artifact rather than a task property, and per inference normalization misrepresents the energy cost of completing a goal. We present A-LEMS (Agentic LLM Energy Measurement System), a cross layer framework that redefines the unit of AI energy accounting as Energy per Successful Goal (EpG): total workflow energy across all attempts, including failures and retries, divided by successfully completed goals. A-LEMS combines a temporal boundary model, a five la...
| Authors | Deepak Panigrahy, Aakash Tyagi, Stephen Abkin et al. |
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| Primary category | cs.AI |
| Published | 20 May 2026 |
| Last updated at source | 12 October 2026 |
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