Summary
Agentic AI systems offer substantial benefits and challenges as they strive to autonomously pursue complex goals while addressing inherent safety and accountability concerns. These advanced systems boost efficiency and productivity by allowing organizations to perform diverse tasks autonomously, thus transforming various industries such as healthcare, finance, and manufacturing. By integrating with technologies like quantum computing and utilizing models like GPT-4, agentic AI enhances specific task capabilities through tool integration and reasoning advancements. However, these systems also pose potential risks, including misuse and ethical issues, requiring robust governance and regulation frameworks to mitigate as they adapt to evolving competitive dynamics, labor displacement, and cascading errors. The transition from "Copilot" to "Autopilot" models exemplifies a significant shift in AI deployment, emphasizing the need for responsible adoption strategies. Collaboration across industries remains essential to tackle challenges in privacy, security, ethical considerations, and regulatory gaps. Furthermore, agentic AI models demand durable solutions for scalability, adaptability, and deployment across numerous domains, encouraging dynamic multi-agent collaboration and persistent memory strategies. Substantial advancements in AI require industry-specific case studies and integrations alongside ongoing interdisciplinary efforts to enhance the understanding and potential of agentic AI to ensure it aligns ethically with societal values and supports sustainable human-AI coexistence.
Agentic AI systems can enhance efficiency and achieve complex goals but pose significant risks. Establishing clear accountability and best practices is crucial.
Published By:
Y Shavit, S Agarwal, M Brundage, S Adler… - Research Paper …, 2023 - cdn.openai.com
Agentic AI enhances organizational performance but faces challenges like privacy and ethical concerns. Future studies should explore industry-specific applications and impacts on privacy, security, and labor markets.
Published By:
S Hosseini, H Seilani - Array, 2025 - Elsevier
Distinguishing AI Agents from Agentic AI in the Generative AI era: taxonomies, applications, challenges. AI Agents automate tasks using LLMs and external tools; Agentic AI involves multi-agent collaboration and dynamic task management.
Published By:
R Sapkota, KI Roumeliotis, M Karkee - arXiv preprint arXiv:2505.10468, 2025 - arxiv.org
Agentic AI enhances GenAI with stronger reasoning and autonomy for complex tasks. The survey explores their differences and potential applications.
Published By:
J Schneider - arXiv preprint arXiv:2504.18875, 2025 - arxiv.org
Agentic AI systems, unlike conventional AI, enable autonomous complex task completion. However, they pose challenges such as errors and regulatory gaps requiring transparency and oversight.
Published By:
YK Dwivedi, MYI Helal, IA Elgendy… - Global Business and …, 2025 - Wiley Online Library
Agentic AI surpasses traditional systems with advanced autonomy, adaptability, and goal-driven reasoning. Evaluated using diverse metrics, Agentic AI's applications span multiple fields, enhancing automation and decision-making.
Published By:
A Bandi, B Kongari, R Naguru, S Pasnoor, SV Vilipala - Future Internet, 2025 - mdpi.com
AI development must focus on holistic systems-theoretic perspectives to understand risks and capabilities. A systems-level approach is essential for shaping and understanding agentic AI systems.
Published By:
E Miehling, KN Ramamurthy, KR Varshney… - arXiv preprint arXiv …, 2025 - arxiv.org
AI agents are transforming sales by automating tasks, with market growth projected to surge. However, sales literature lags behind, prompting calls for academic research on these autonomous technologies.
Published By:
GR Gonzalez, J Habel, GK Hunter - Journal of Business Research, 2026 - Elsevier
Agentic AI is revolutionizing scientific research automation and enhancing hypothesis generation and analysis. The survey reviews systems, challenges, and future directions for human-AI collaboration.
Published By:
M Gridach, J Nanavati, KZE Abidine, L Mendes… - arXiv preprint arXiv …, 2025 - arxiv.org
Agentic AI systems in education offer autonomous, adaptive interactions that personalize learning, providing dynamic, real-time feedback and fostering engagement. They face challenges in managing cognitive load and data privacy, necessitating responsible design and human oversight.
Published By:
G Kostopoulos, V Gkamas, M Rigou… - IEEE Access, 2025 - ieeexplore.ieee.org