Abstract
Agentic artificial intelligence marks a shift from static prediction toward systems that perceive, reason, act, verify, and adapt through closed-loop interaction with external environments. Although large multimodal models provide the representational substrate for this shift, practical agency emerges only when model inference is embedded within memory, planning, tool use, environment interfaces, verification, and oversight mechanisms. We formalize the notion of an agentic AI system, distinguish it from single-turn assistants, scripted workflows, retrieval-augmented generation, and classical embodied controllers, and propose a taxonomy covering model substrates, observation spaces, memory, planning, action interfaces, multi-agent coordination, evaluation, and autonomy constraints. We then synthesize learning and optimization paradigms for agents, including supervised trajectory tuning, tool-use adaptation, preference optimization, reinforcement learning, process rewards, verifier-guided search, test-time scaling, memory-driven adaptation, and distillation. Across domains such as web search, software engineering, multimodal reasoning, embodied control, scientific discovery, and enterprise automation, we analyze the factors shaping agent performance and reliability. We further examine benchmarks, evaluation protocols, safety considerations, governance challenges, and open research problems in long-horizon reasoning, memory management, multimodal grounding, and trustworthy deployment. A companion repository tracking developments in agentic AI is available at https://github.com/Tajamul21/awesome-agentic-ai.