Agentic Artificial Intelligence: A Systematic Review of Architectures, Capabilities, Applications, and Open Challenges
Abstract
Agentic artificial intelligence (AI) denotes a class of systems, typically built on large language models (LLMs), that autonomously perceive context, reason over multi-step goals, invoke external tools, retain and update memory, and act within digital or physical environments with limited human intervention. The rapid transition from single-turn generative models to goal-directed, tool-using, and often multi-agent systems has produced a large and fast-growing body of research spanning reasoning and planning strategies, memory architectures, multi-agent coordination protocols, evaluation benchmarks, and domain-specific deployments. This paper presents a structured review of the agentic AI literature, synthesizing contributions from foundational reasoning frameworks such as Chain-of-Thought and ReAct, tool-augmented approaches such as Toolformer and ToolLLM, reflective and memory-augmented agents such as Reflexion and MemGPT, and multi-agent orchestration frameworks such as AutoGen and MetaGPT. A consolidated taxonomy of core agentic capabilities is proposed, covering reasoning and planning, tool use, memory management, self-reflection, and multi-agent collaboration.
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