Artificial Intelligence Agents: A Structural Review of Frameworks, Core Components, and Open Challenges

Abstract

Artificial intelligence (AI) agents have emerged as a major application paradigm built on large language models (LLMs). These systems can perceive inputs, reason about goals, plan actions, use tools, access memory, and interact with external environments with varying degrees of autonomy. Although AI agent systems are rapidly expanding, the literature still lacks a concise structural review that explains how these systems are organized into recurring framework patterns and which components they commonly share. This paper addresses this gap by presenting a structural review of major AI agent frameworks. We organize existing systems into eight nonexclusive architectural patterns, including single agent, tool-augmented, retrieval-augmented, reflective, multi-agent, hierarchical, blackboard or shared memory, and human-in-the-loop frameworks. Each pattern is analyzed according to autonomy, communication topology, memory design, and oversight strategy. We further identify nine recurring components across AI agent systems, including perception, reasoning, planning, memory, tool use, action execution, reflection, coordination, and human oversight. Based on this analysis, we discuss six open challenges, including reliability and hallucination, safety and alignment, privacy and data governance, evaluation under open ended task settings, interoperability and standardization, and cost-aware orchestration. Rather than advocating a single dominant architecture, this paper provides a common vocabulary and structural synthesis to support researchers in designing new AI agent systems and practitioners in selecting appropriate frameworks for real world applications.