Position: Agentic AI System Is a Foreseeable Pathway to AGI

TL;DR

This paper argues that Agentic AI, involving complex, heterogeneous task management, is a necessary and more efficient alternative to monolithic scaling for achieving Artificial General Intelligence.

Contribution

It introduces the concept of Agentic AI as a crucial paradigm, contrasting it with monolithic models through theoretical analysis and proposing advanced DAG-based architectures.

Findings

Agentic AI achieves exponentially better generalization.

Agentic systems are more sample-efficient than monolithic models.

The paper connects Agentic AI to Mixture-of-Experts and discusses multi-agent framework issues.

Abstract

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.