Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
A new framework called A-LEMS has been introduced for measuring energy consumption in agentic AI systems. This framework shifts the focus from energy per inference to Energy per Successful Goal (EpG), which accounts for the total energy used across all attempts to achieve a goal. The findings indicate that agentic workflows consume significantly more energy than linear baselines, highlighting the importance of orchestration structure in energy costs.
- ▪A-LEMS redefines AI energy accounting from energy per inference to Energy per Successful Goal (EpG).
- ▪Agentic workflows consume 4.33 times higher mean energy per successful goal compared to linear baselines.
- ▪The Orchestration Overhead Index (OOI) measures the energy cost of orchestration relative to linear execution.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.22883 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0kBtfIkGRaZL |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.22883 (cs) [Submitted on 20 May 2026] Title:Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems Authors:Deepak Panigrahy, Aakash Tyagi View a PDF of the paper titled Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems, by Deepak Panigrahy and 1 other authors View PDF HTML (experimental) Abstract:Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads this unit remains coherent.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
