Uncertainty-Aware Resource Orchestration for Agentic AI Systems
arXiv:2611.05392v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems powered by large language models (LLMs) execute as autonomous multi-step pipelines that dynamically invoke tools, integrate observations into memory, and iterate until a task is resolved or a resource budget is exhausted. Unlike classical workflows with fixed graphs, the aggregate compute, bandwidth, and end-to-end latency demands of such pipelines are unknown at dispatch time: they emerge at runtime from intermediate observations and stochastic token sampling, yielding heavy-tailed, multi-modal random
阅读原文 · arXiv 人工智能本文由 SoccerDream AI 自动聚合索引，内容版权归原作者与 arXiv 人工智能 所有。
本站仅作技术动态聚合，不构成任何形式的建议。
本站仅作技术动态聚合，不构成任何形式的建议。