Position: Agentic AI System Is a Foreseeable Pathway to AGI

Computer Science > Artificial Intelligence [Submitted on 13 May 2026] Position: Agentic AI System Is a Foreseeable Pathway to AGI Junwei Liao, Shuai Li, Muning Wen, Jun Wang, Weinan Zhang Is monolithic scaling the only path to AGI? This paper challenges the dogma that purely scaling a single model is sufficient to achieve Artificial General Intelligence. Instead, we identify Agentic AI as a necessary paradigm for mastering the complex, heterogeneous distribution of real-world tasks. Through rigorous theoretical derivations, we contrast the optimization constraints of monolithic learners against the efficiency of Agentic systems, progressing from simple routing mechanisms to general Directed Acyclic Graph (DAG) topologies. We demonstrate that Agentic AI achieves exponentially superior generalization and sample efficiency. Finally, we discuss the connection to Mixture-of-Experts, reinterpret the instability of current multi-agent frameworks, and call for greater research focus on Agentic AI. Comments: Accepted by ICML'26 Position Track Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2605.12966 [cs.AI]   (or arXiv:2605.12966v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.12966 Focus to learn more Submission history From: Junwei Liao [view email] [v1] Wed, 13 May 2026 04:00:43 UTC (237 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)