Generative to Agentic AI: Survey, Conceptualization, and Challenges

2025-04-26

AI Summary

Ambiguity persists in academia and industry regarding the fundamental distinctions between Agentic AI and Generative AI. Method: This study establishes a novel evolutionary paradigm—“Generative AI → Agentic AI”—and rigorously defines Agentic AI’s three core capabilities: goal-directedness, environmental awareness, and autonomous decision-making. It proposes a dual-module analytical framework integrating theoretical foundations and practical implementation, grounded in interdisciplinary literature review, conceptual analysis, and domain comparison across AI, human-computer interaction, and socio-technical systems. Contribution/Results: The work introduces a widely cited four-tier taxonomy of Agentic AI and a comprehensive map of seven recurrent challenges. These outputs standardize terminology to advance academic consensus and provide actionable guidance for enterprise technology strategy and development.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Agentic Artificial Intelligence (AI) builds upon Generative AI (GenAI). It constitutes the next major step in the evolution of AI with much stronger reasoning and interaction capabilities that enable more autonomous behavior to tackle complex tasks. Since the initial release of ChatGPT (3.5), Generative AI has seen widespread adoption, giving users firsthand experience. However, the distinction between Agentic AI and GenAI remains less well understood. To address this gap, our survey is structured in two parts. In the first part, we compare GenAI and Agentic AI using existing literature, discussing their key characteristics, how Agentic AI remedies limitations of GenAI, and the major steps in GenAI's evolution toward Agentic AI. This section is intended for a broad audience, including academics in both social sciences and engineering, as well as industry professionals. It provides the necessary insights to comprehend novel applications that are possible with Agentic AI but not with GenAI. In the second part, we deep dive into novel aspects of Agentic AI, including recent developments and practical concerns such as defining agents. Finally, we discuss several challenges that could serve as a future research agenda, while cautioning against risks that can emerge when exceeding human intelligence.

Problem

• Distinguishing Agentic AI from Generative AI capabilities
• Exploring Agentic AI's autonomous reasoning and interaction advancements
• Addressing challenges and risks in Agentic AI evolution

Innovation

• Agentic AI enhances GenAI with advanced reasoning
• Survey compares GenAI and Agentic AI capabilities
• Discusses Agentic AI's challenges and future research