arXiv (All)
open access
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 norma
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- When Agents Spend Watts: Measuring Performance-Energy Trade-offs in Agentic AI Systems with AgenticBench#322175
- ACEM: A Cost Estimation Model for Agentic Software Engineering#424031
- How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks#134612
- Environmental Impact of Generative and Agentic AI: An in-Depth Analysis and Green Solutions#1117672
- ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution#1083670
Record · ID 1341420
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive