A Comprehensive Survey on Agentic Artificial Intelligence Architectures: Reasoning, Planning and Real-World Applications

Agentic Artificial Intelligence (AI) marks a fundamental paradigm shift: from passive language models to autonomous systems capable of multi-step reasoning, adaptive planning, dynamic tool use, and sustained goal-directed behaviour. This survey provides a structured review of the theoretical foundations, architectural patterns, reasoning strategies, and real-world deployments of agentic AI. We examine the core components of contemporary agentic frameworks — perception, reasoning engines, memory hierarchies, planning modules, and tool interfaces — drawing on recent literature spanning large language model (LLM)-based agents, multi-agent systems, embodied agents, and hybrid symbolic-neural approaches. Advanced reasoning paradigms including Chain-of-Thought (CoT), Tree-of-Thought (ToT), ReAct, Reflexion, and Monte Carlo Tree Search (MCTS)-augmented planning are analysed alongside the emerging challenges of alignment, safety, and scalability. Real-world applications across software engineering, scientific discovery, healthcare, robotics, enterprise automation, and education are surveyed, and critical open problems are identified. This survey is intended as a reference architecture for researchers and practitioners engaged in the design, evaluation, and deployment of agentic AI systems.