A Survey of Agentic AI: Architectures, Reasoning, Planning, Memory, Collaboration, and Real-World Applications

Agentic Artificial Intelligence (AI) is a new paradigm that allows intelligent systems to behave independently, pursue specific objectives, and adapt to changing circumstances. Agentic AI combines reasoning, planning, memory, learning, and tool utilization to do complicated tasks with minimal human involvement, in contrast to traditional AI systems that mainly react to user inputs. In this article, takes a look at Agentic AI from every angle: its theoretical underpinnings, technological supports, architectural blueprints, and real-world uses. Many models and frameworks are covered, including cognitive and modular designs, systems based on reinforcement learning, hybrid models, and multi-agent systems. Symbolic, probabilistic, and self-reflective reasoning are some of the reasoning methodologies covered. Classical, hierarchical, and adaptive planning strategies are also covered. Agents' ability to reliably and efficiently execute tasks is investigated by looking at memory management, communication protocols, coordination mechanisms, and collaborative decision-making. A wide range of business, medical, financial, academic, and personal aid apps are evaluated in the poll. Privacy, security, computational costs, scalability, and unexpected behavior are the main issues that are highlighted in the end. The study emphasizes the need for more investigation into how to create Agentic AI systems that are reliable, flexible, and effective.