Position: Agentic AI Is a Foreseeable Pathway to AGI
Junwei Liao, Shuai Li, Muning Wen, Jun Wang, Weinan Zhang
Abstract
Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is enough to achieve universal super-intelligence. Instead, we identify Agentic AI as the necessary evolution for handling complex, real-world task distributions to achieve AGI in the human world. Through concrete theoretical derivations, we contrast the optimization constraints of monolithic learners against the efficiency of Agentic systems, evolving from simple routing mechanisms to general Directed Acyclic Graphs (DAGs) of Agents. We demonstrate that Agentic AI offers superior generalization and efficiency. Finally, we reinterpret the instability of current multi-agent frameworks and call for more future actions on Agentic AI.
BibTeX
@inproceedings{icml2026_positionagentica,
title = {Position: Agentic AI Is a Foreseeable Pathway to AGI},
author = {Junwei Liao and Shuai Li and Muning Wen and Jun Wang and Weinan Zhang},
booktitle = {ICML 2026},
year = {2026}
}