Generative to Agentic AI: Survey, Conceptualization, and Challenges

TL;DR

<3-5 sentence high-level summary> Agentic AI represents a paradigm shift that extends GenAI with autonomous reasoning, environmental interaction, and tool-based capabilities. The paper lays out precise definitions, contrasts GenAI and Agentic AI, and analyzes the evolution, memory, reasoning, and interaction mechanisms enabling more complex, goal-directed behavior. It details methods for specifying, evaluating, and deploying single- and multi-agent systems, and surveys practical challenges and safety considerations on the path toward AGI. The work aims to guide researchers and practitioners in understanding opportunities, risks, and a research agenda for responsible development of Agentic AI.

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.

Figures (15)

• Figure 1: Reasoning models perform extensive problem-dependent computations, commonly employing problem analysis, planning and reflection, while non-reasoning is shown by immediate responses without intermediate steps.
• Figure 2: On the ARC challenge cla18 Agentic AI with its reasoning models such as o1 and o3 performing dynamic, extensive computations involving planning and reflection dramatically outperform other models.
• Figure 3: Overview: The high-level discussion of GenAI to Agentic AI is followed by an in-depth treatment of Agentic AI, followed by challenges and outlook
• Figure 4: Agentic AI and GenAI performs similarly on MMLU hen21mml measuring a wide range of capabilities across disciplines(data from pap25sot)
• Figure 5: From Generative AI to Agentic AI. Early GenAI such as GPT-3 bro20 showed with minor effort basic reasoning and tool usage capabilities. These became much more profound over time, in particular, with Agentic AI, which includes elements from reinforcement learning such as interacting with the environment and other agents.