Enhancing generative AI reliability via agentic AI in 6G-enabled edge computing
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Abstract
Agentic artificial intelligence (AI) workflows, integrated with 6G-enabled edge computing, present a transformative approach to reduce hallucinations in generative AI. Combining real-time tool integration, self-reflection and collaborative multi-agent systems, this framework offers scalable, reliable solutions for diverse applications, advancing the accuracy and adaptability of generative AI in dynamic and data-intensive environments.
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Reflexive mechanisms introduce the capacity for self-evaluation in AI Agents.
“Reflexive mechanisms introduce the capacity for self-evaluation [227,228].”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [228]
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- Authors
- Ale, L., King, S. A., Zhang, N., & Xing, H.
- Affiliations
- Southwest Jiaotong University (CN)
- Texas A&M University – Corpus Christi (US)
- University of Windsor (CA)
- Corresponding author
- Ale, Laha
- Venue
- Nature Reviews Electrical Engineering
- Published
- 2025-04-14
- Volume
- 2
- Issue
- 7
- Pages
- 441-443
- ISSN
- 2948-1201 (ISSN-L)
- OpenAlex
- W4409410640
- Cited by
- 11
- References
- 8
- Citations by year
- 2026: 5 · 2025: 6
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Reviews Electrical Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- agentic AI, hallucination mitigation, 6G, edge computing, multi-agent systems, self-reflection
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