Agentic Artificial Intelligence: A Comprehensive Review of Foundational Architectures, Cognitive Mechanisms, and Governance Imperatives

The goal of this paper is to synthesize the situation of Agentic AI by balancing the current strengths of Low-level large language models (LLMs) with ancient classifications in the history of agent theory, i.e. the Belief-Desire-Intention (BDI) model, and Bounded Rationality. We aim to present a roadmap on the development of such autonomous systems. A thorough study of the new cognitive architectures, hierarchical memory management systems, and Multi-Agent System (MAS) orchestration models is done. The paper reviews deliberative System 2 cycles, including ReAct, Reflexion and Language Agent Tree Search. We discover that, although these complex cognitive loops and memory architectures permit the ability to perform complex computations across large data sets, these solutions come at trade-offs in terms of latency and reliability. We define a new taxonomy of AI components that make the distinction between modular AI components and AI systems. Lastly, we claim that to scale highly autonomous systems, there is a need to adopt an embedded, real-time governance. We suggest adopting muti-agent system, which applies to special Worker, Critic and Guardrail agents to reduce potential risks of misalignment and fraudulent conduct to achieve the confidence and reliability of the production-ready Agentic AI.