Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions
Mohamad Abou Ali, Fadi Dornaika

TL;DR
This survey introduces a dual-paradigm framework for agentic AI, categorizing systems into symbolic/classical and neural/generative, analyzing their architectures, applications, and ethical challenges across various domains.
Contribution
It presents a novel dual-paradigm framework for agentic AI, systematically reviews 90 studies, and offers a strategic roadmap for integrating symbolic and neural systems.
Findings
Symbolic systems dominate safety-critical domains like healthcare.
Neural systems are prevalent in adaptive, data-rich environments such as finance.
There is a significant gap in governance models for symbolic systems.
Abstract
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbolic models -- a practice known as conceptual retrofitting. This survey cuts through this confusion by introducing a novel dual-paradigm framework that categorizes agentic systems into two distinct lineages: the Symbolic/Classical (relying on algorithmic planning and persistent state) and the Neural/Generative (leveraging stochastic generation and prompt-driven orchestration). Through a systematic PRISMA-based review of 90 studies (2018--2025), we provide a comprehensive analysis structured around this framework across three dimensions: (1) the theoretical foundations and architectural principles defining each paradigm; (2) domain-specific implementations in healthcare, finance, and robotics,…
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