Agentic AI, or Agentic Artificial Intelligence, refers to AI systems capable of autonomous decision-making, goal-directed behavior, and proactive interactions within dynamic environments. Unlike traditional AI, which often operates in predefined contexts, agentic AI independently sets goals, devises plans, and adapts strategies through self-directed learning and reasoning.

Agentic AI symbolizes a pivotal shift towards fully autonomous, self-directed intelligent systems capable of complex reasoning and decision-making in real-world scenarios.

Generative to Agentic AI: Survey, Conceptualization, and Challenges

"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."

Preprint Generative to Agentic AI: Survey, Conceptualization, and Challenges

Agent Laboratory is an open-source LLM agent framework for accelerating the individual’s ability to perform research in machine learning. Unlike fully automated research pipelines that attempt to conceive their own research directions, Agent Laboratory is designed as a co-pilot, enabling a more human-centric mode of scientific exploration.

Agent Laboratory: Using LLM Agents as Research Assistants

"Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process. This framework accepts a human-provided research idea and progresses through three stages--literature review, experimentation, and report writing to produce comprehensive research outputs, including a code repository and a research report, while enabling users to provide feedback and guidance at each stage. We deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple researchers to assess its quality by participating in a survey, providing human feedback to guide the research process, and then evaluate the final paper. We found that: (1) Agent Laboratory driven by o1-preview generates the best research outcomes; (2) The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods; (3) Human involvement, providing feedback at each stage, significantly improves the overall quality of research; (4) Agent Laboratory significantly reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods. We hope Agent Laboratory enables researchers to allocate more effort toward creative ideation rather than low-level coding and writing, ultimately accelerating scientific discovery."

Preprint Agent Laboratory: Using LLM Agents as Research Assistants

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Agentic AI: Autonomous Intelligence for Complex Goals – A Comprehensive Survey

• January 2025
• IEEE Access PP(99):1-1
• DOI: 10.1109/ACCESS.2025.3532853
• License CC BY 4.0
• By Deepak Acharya, Karthigeyan Kuppan, Divya B Ashwin

[Abstract

Agentic AI, an emerging paradigm in artificial intelligence, refers to autonomous systems designed to pursue complex goals with minimal human intervention. Unlike traditional AI, which depends on structured instructions and close oversight, Agentic AI demonstrates adaptability, advanced decision-making capabilities and self-sufficiency, enabling it to operate dynamically in evolving environments. This survey thoroughly explores the foundational concepts, unique characteristics, and core methodologies driving the development of Agentic AI. We examine its current and potential applications across various fields, including healthcare, finance, and adaptive software systems, emphasizing the advantages of deploying agentic systems in real-world scenarios. The paper also addresses the ethical challenges posed by Agentic AI, proposing solutions for goal alignment, resource constraints, and environmental adaptability.We outline a framework for safely and effectively integrating Agentic AI into society, highlighting the need for further research on ethical considerations to ensure beneficial societal impacts. This survey serves as a comprehensive introduction to Agentic AI, guiding researchers, developers, and policymakers in engaging with its transformative potential responsibly and creatively.]