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This paper investigates the architectural landscape of LLM-based agents, proposing a unified taxonomy based on Perception, Brain, Planning, Action, Tool Use, and Collaboration components. It analyzes the evolution from linear reasoning to inference-time reasoning and from fixed APIs to open standards like MCP, while also categorizing agent environments and reviewing evaluation practices. The work identifies key challenges like hallucination and prompt injection, suggesting future research directions for more reliable autonomous systems.
Agentic AI is more than just LLMs generating text; it's about autonomous systems that perceive, reason, plan, and act, and this paper offers a unified taxonomy to navigate this rapidly evolving landscape.
Artificial Intelligence is moving from models that only generate text to Agentic AI, where systems behave as autonomous entities that can perceive, reason, plan, and act. Large Language Models (LLMs) are no longer used only as passive knowledge engines but as cognitive controllers that combine memory, tool use, and feedback from their environment to pursue extended goals. This shift already supports the automation of complex workflows in software engineering, scientific discovery, and web navigation, yet the variety of emerging designs, from simple single loop agents to hierarchical multi agent systems, makes the landscape hard to navigate. In this paper, we investigate architectures and propose a unified taxonomy that breaks agents into Perception, Brain, Planning, Action, Tool Use, and Collaboration. We use this lens to describe the move from linear reasoning procedures to native inference time reasoning models, and the transition from fixed API calls to open standards like the Model Context Protocol (MCP) and Native Computer Use. We also group the environments in which these agents operate, including digital operating systems, embodied robotics, and other specialized domains, and we review current evaluation practices. Finally, we highlight open challenges, such as hallucination in action, infinite loops, and prompt injection, and outline future research directions toward more robust and reliable autonomous systems.
