Summary
Agentic AI refers to AI systems that operate with a degree of autonomy, capable of making decisions and setting goals independent of human intervention. These significantly differ from traditional AI systems that require specific instructions and oversight. The ongoing development of Agentic AI is marked by a transition from assistant roles to more autonomous frameworks capable of handling complex tasks and decision-making. This shift offers substantial benefits, such as improving organizational performance through increased efficiency and customer focus, and broader applications across industries including healthcare, education, and scientific discovery. However, Agentic AI also presents unique challenges. These include potential job displacement, ethical concerns such as privacy and security, and the risk of these systems making decisions that could surpass human intelligence and control. Safety practices are crucial, including evaluating the suitability of AI deployment, constraining the actions these systems can take, and ensuring they exhibit behaviors akin to default settings, making them predictable and interruptible. Moreover, there is a need for effective governance and transparency to ensure these AI systems can be held accountable. Industry collaborations and interdisciplinary research are needed to address these challenges, including the ethical ramifications and ensuring responsible usage, integrating insights from multiple fields to enhance understanding and safe deployment of Agentic AI.
Agentic AI systems require governance to ensure safe integration into society. Best practices for safety and accountability are needed.
Published By:
Y Shavit, S Agarwal, M Brundage, S Adler… - Research Paper …, 2023 - cdn.openai.com
Agentic AI enhances organizational efficiency and autonomy but poses challenges in ethics and security. Future research should focus on industry-specific case studies and emerging technologies.
Published By:
S Hosseini, H Seilani - Array, 2025 - Elsevier
AI Agents automate tasks via modular interfaces, while Agentic AI supports complex multi-agent coordination. AI Agents excel in specific tasks, and Agentic AI enhances intelligence across diverse domains.
Published By:
R Sapkota, KI Roumeliotis, M Karkee - arXiv preprint arXiv:2505.10468, 2025 - arxiv.org
Agentic AI enhances GenAI with stronger reasoning for autonomous complex task handling. The survey contrasts both AIs and explores Agentic AI's novel developments and challenges.
Published By:
J Schneider - arXiv preprint arXiv:2504.18875, 2025 - arxiv.org
Agentic AI systems differ from conventional AI by executing complex tasks with minimal oversight. Despite transformative applications, they pose challenges like goal misalignment and regulatory gaps.
Published By:
YK Dwivedi, MYI Helal, IA Elgendy… - Global Business and …, 2025 - Wiley Online Library
Study reviews "agentic AI" advancing autonomous systems; highlights architecture, challenges, and domains. Systems show promise in healthcare, military, software, maintaining dynamic interactions.
Published By:
A Bandi, B Kongari, R Naguru, S Pasnoor, SV Vilipala - Future Internet, 2025 - mdpi.com
AI systems need holistic, systems-theoretic views to understand and manage capabilities and risks. Current development underestimates agentic AI's true potential by focusing too much on individual models.
Published By:
E Miehling, KN Ramamurthy, KR Varshney… - arXiv preprint arXiv …, 2025 - arxiv.org
Autonomous AI agents are rapidly transforming sales, with market projections soaring to $139 billion by 2033. The academic sales literature is lagging, necessitating future research on their impact.
Published By:
GR Gonzalez, J Habel, GK Hunter - Journal of Business Research, 2026 - Elsevier
Agentic AI transforms scientific research with autonomous systems in literature review and experiments. It faces challenges in ethics, system reliability, and human-AI collaboration.
Published By:
M Gridach, J Nanavati, KZE Abidine, L Mendes… - arXiv preprint arXiv …, 2025 - arxiv.org
Agentic AI systems offer personalized, context-sensitive learning experiences, enabling educational differentiation and foster active learning, but pose challenges in ethics, privacy, and over-dependence.
Published By:
G Kostopoulos, V Gkamas, M Rigou… - IEEE Access, 2025 - ieeexplore.ieee.org