This page shows the most relevant public items for al:agentic-ai-2025, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • May 12, 2025 • arXiv • Ling Chen, David Wu, Samantha Harris, Yong Zhang
Single-agent code generation models frequently fail when confronted with repository-level refactoring and complex architectural planning. We propose SwarmCoder, an Agentic AI framework that deploys...
Paper • May 20, 2025 • arXiv • Lilian W. Zhang, Michael O. Fasanya, Hiroshi Tanaka
The traditional drug discovery pipeline is hindered by siloed computational predictions and slow, manual wet-lab validation. We present a fully autonomous, closed-loop Agentic AI system that seamle...
Paper • Dec 5, 2025 • arXiv • Alessandro V. Rossi, Elena M. K., Yong Lin
The delegation of high-stakes financial and infrastructural tasks to Agentic AI requires robust mechanisms for verifying agent behavior without exposing sensitive underlying data. This paper propos...
Paper • Sep 22, 2025 • arXiv • David Lin, Amir Gholami, Kurt Keutzer
Persistent and scalable memory is the foundational bottleneck preventing Agentic AI from achieving long-term autonomy. This study provides a comprehensive comparative analysis of state-of-the-art m...
Paper • Nov 5, 2025 • arXiv • Chen Wei, Marcus T. Holden, Elara Vance
The rapid escalation of automated cyber attacks has outpaced human response times, necessitating a shift toward autonomous defense mechanisms. This paper introduces an Agentic AI framework for cont...
Paper • Nov 20, 2025 • arXiv • Dr. Yannick L., Sophia M., Wei C.
As Agentic AI swarms scale to thousands of interacting nodes driven by frontier language models, unexpected macro-level dynamics arise. We deploy a multi-agent simulation simulating a digital econo...
Paper • Mar 21, 2025 • arXiv • Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Kuhu Syal
Automated theorem proving requires a blend of creative intuition and rigorous logical deduction, a combination that eludes pure deep learning models. We propose a Neurosymbolic Agentic AI framework...
Paper • Aug 25, 2025 • arXiv • Alexander C. K.
As Agentic AI systems are granted read/write access to production databases, cloud infrastructures, and financial APIs, the risk of catastrophic runaway actions increases exponentially. This paper ...
Paper • Jul 8, 2025 • arXiv • Carlos E. Jimenez, John Yang, Alexander Wettig, Kilian Lieder, Shunyu Yao, Karthik Narasimhan, Ofir Press
As single-agent coding assistants plateau in their ability to handle repository-scale refactoring, multi-agent swarms have emerged as the new standard for autonomous software engineering. We introd...
Paper • Apr 11, 2025 • arXiv • Minh N. Nguyen, Sarah J. Conner, Wei Lin
Standard Retrieval-Augmented Generation (RAG) relies on static, single-pass semantic searches that often fail on complex, multi-hop queries. We introduce Agentic-RAG, an autonomous framework where ...
Paper • Jul 21, 2025 • arXiv • Mohammad Azarijafari, Luisa Mich, Michele Missikoff
Artificial Intelligence agents represent the next major revolution in industrial automation. Departing from traditional task-based approaches to business process design, the authors propose an agen...
Paper • Aug 13, 2025 • arXiv • Hana Derouiche, Zaki Brahmi, Haithem Mazeni
The emergence of Agentic AI has ushered in a transformative paradigm where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination. This paper ...
Paper • Dec 18, 2025 • arXiv • Anonymous Consortium
As agentic AI systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. This paper unifies the rapidly expanding resea...
Paper • Oct 29, 2025 • arXiv • Mohamad Abou Ali, Fadi Dornaika
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbol...
Paper • May 15, 2025 • arXiv • Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review critically distinguishes between traditional 'AI Agents' and the broader paradigm of 'Agentic AI', offering a structured conceptual taxonomy and application mapping. The authors argue t...
Paper • Jun 2, 2025 • arXiv • Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, Pavlo Molchanov
The rise of agentic AI systems has ushered in a mass of applications where language models perform specialized tasks repetitively with little variation. This position paper argues that Small Langua...
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