From Passive Generation to Autonomous Agency: A Comprehensive Survey of Agentic AI Architectures

The evolution of Artificial Intelligence (AI) from passive generative models to autonomous, goal-driven entities marks a paradigm shift toward Agentic AI. This survey synthesizes recent advancements in agentic architectures, offering a comprehensive analysis of frameworks that transcend traditional Large Language Models (LLMs) by incorporating autonomy, contextual reasoning, tool integration, and environmental feedback loops. Drawing on over 50 state-of-the-art implementations, we classify agentic systems into three principal lineages: symbolic, neural, and hybrid. Symbolic agents rely on persistent state and algorithmic planning, while neural agents leverage stochastic generation and prompt-driven orchestration; hybrid models blend these modalities to enhance adaptability and scalability. A prominent trend identified is the emergence of collaborative agent ecosystems, wherein multiple specialized agents coordinate to execute complex, multi-step workflows—ranging from scientific discovery to software engineering. These systems demonstrate increasing competence in long-horizon planning, dynamic tool selection, and inter-agent communication. However, the transition from sandboxed experimentation to enterprise-grade deployment is hindered by unresolved challenges in reliability, safety, and evaluation metrics. We highlight the need for standardized benchmarks, robust interpretability frameworks, and fail-safe mechanisms to ensure trustworthy operation in real-world environments. This review positions Agentic AI as a foundational layer for next-generation intelligent systems, with implications for autonomous research assistants, adaptive robotics, and scalable decision-making platforms. By bridging conceptual gaps between reactive generation and proactive agency, this work contributes to the theoretical and practical scaffolding required for building resilient, collaborative, and context-aware AI agents