Position: Agentic AI System Is a Foreseeable Pathway to AGI
For AI researchers, this work challenges the prevailing scaling dogma and provides a theoretical foundation for agentic systems as a path to AGI.
This paper argues that monolithic scaling alone is insufficient for AGI and proposes Agentic AI as a necessary paradigm, demonstrating through theoretical derivations that it achieves exponentially superior generalization and sample efficiency compared to monolithic models.
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.