Understanding Agentic AI: Algorithms and Infrastructure

Abstract

The rapid evolution of large language models (LLMs) towards autonomous Agentic artificial intelligence (AI) necessitates a systemic overhaul across algorithms, infrastructure, and architectures. This paper presents a unified view of the “Agentic AI Infrastructure,” connecting research threads often studied in isolation. First, post-training algorithms are reviewed, contrasting traditional reinforcement learning (RL) with emerging reasoning-centric methods and test-time scaling strategies. Next, the transition of RL training frameworks is analyzed from monolithic, colocated designs to disaggregated, asynchronous architectures tailored for the extreme variance of agentic rollouts. Furthermore, progress in agent construction is synthesized, covering reflection, planning, tool use, and multi-agent collaboration. By integrating these layers, the paper elucidates how agentic AI systems impose unique demands on underlying training systems. Finally, open challenges are outlined by covering capability scaling, efficiency, safety, privacy, and governance for reliable real-world agentic AI deployment.