Agentic AI: A Survey of Autonomous Agents, Architectures, and Emerging Applications

Abstract

Artificial intelligence has progressed from taskspecific systems toward more autonomous and adaptive agents capable of complex decision-making. Agentic AI, characterized by goal-driven autonomy, reasoning, and collaboration, represents a paradigm shift in how intelligent systems are designed and deployed. Unlike conventional AI, which relies on predefined models or narrow optimizations, Agentic AI integrates perception, planning, and action with adaptive learning and social coordination, enabling agents to operate in dynamic, uncertain, and multi-agent environments. This paper provides a comprehensive survey of Agentic AI, spanning classical architectures such as belief-desire-intention (BDI) models, reinforcement learning agents, and emerging frameworks powered by large language models (LLMs) and hybrid approaches. Applications across healthcare, finance, cybersecurity, robotics, smart cities, and knowledge management are systematically reviewed, with a focus on their strengths, limitations, and scalability. A comparative taxonomy is presented to classify agents by autonomy, learning mechanisms, and collaboration strategies. Key challenges—including reliability, explainability, coordination overhead, and ethical governance are analyzed, alongside the risks of emergent behaviors and adversarial exploitation. Finally, future research directions are outlined, emphasizing the need for trustworthy, scalable, and socially aligned Agentic AI. This survey offers researchers and practitioners a structured foundation for advancing next-generation intelligent agent systems.

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