The role of agentic AI in shaping a smart future: A systematic review
Open accessAbstract
Artificial intelligence (AI), particularly Agentic AI, is increasingly critical for addressing the demand for speed, efficiency, and customer focus in modern organizations. However, the rapid evolution of Agentic AI, including Generative AI (GenAI) agents, has outpaced a cohesive understanding of its applications, challenges, and strategic implications. This narrative review explores the role of Agentic AI in shaping an intelligent future, focusing on its key attributes—autonomy, reactivity, proactivity, and learning ability—and its potential to transform organizational performance. We identify a research gap in synthesizing the diverse capabilities of Agentic AI (e.g., multimodal processing, hierarchical architectures, and machine learning outsourcing) and providing actionable strategies for adoption. The paper examines how Agentic AI enables autonomous decision-making, automates processes, and enhances efficiency through tools like LangChain, CrewAI, AutoGen, and AutoGPT. It highlights the transition from assisted ("Copilot") to autonomous ("Autopilot") models and the importance of hierarchical agent structures for system coordination. Key contributions include a framework for organizations to formulate GenAI strategies, addressing business needs, tool selection, human resource training, and risk management. Findings reveal that Agentic AI significantly improves productivity, reduces costs, and drives innovation, though challenges such as privacy, security, and ethical concerns remain. Future research should focus on industry-specific case studies to deepen understanding, explore the ethical and social impacts (e.g., privacy, data security, labor market effects), and investigate the integration of Agentic AI with emerging technologies like quantum computing. This review provides a foundation for researchers and practitioners to leverage Agentic AI effectively while addressing its limitations and opportunities.
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Agentic AI operates autonomously with minimal supervision.
“Low intervention; operates autonomously with minimal supervision”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [66]
Multi-domain agents enhance personal assistants, cybersecurity, and autonomous knowledge management, while realizing reduced computation cost and smarter cross-domain problem solving.
“Agents also enhance personal assistants, cybersecurity, and autonomous knowledge management, while realizing reduced computation cost and smarter cross-domain problem solving [32,46,66,134,137,138].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [66]
Based on how the system adapted, feedback is generated autonomously in real time.
“Based on how the system adapted, feedback is generated autonomously in real time [66].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [66]
Platforms like IBM Watson Health and tools like E-rater and IntelliMetric maintain high scoring accuracy in predicting and evaluating.
“Platforms like IBM Watson Health [66] and tools like E-rater [57] and IntelliMetric [57] maintain high scoring accuracy in predicting and evaluating.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [66]
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An Explainable AI Multi-Agent Recommender System for Financial Document Access Control
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Performance-Energy Characterization of ML Inference on Heterogeneous Edge AI Platforms
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“Therefore, we believe that CAIM yields a promising and extendable approach toward holistic memory modeling for LLMs that enables personalized context-aware interactions.”
Image Enhancement Combined with LLM Collaboration for Low-Contrast Image Character Recognition
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Agentic AI in 6G Software Businesses: A Layered Maturity Model
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Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI
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The State of World Research Toward AI Agents and Future Perspective: A Bibliometric Study
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- Authors
- Hosseini, S., & Seilani, H.
- Affiliations
- Shahid Bahonar University of Kerman (IR)
- Corresponding author
- Hosseini, Soodeh
- Venue
- Array
- Publisher
- Elsevier BV
- Published
- 2025-07
- Volume
- 26
- Pages
- 100399
- ISSN
- 2590-0056 (ISSN-L)
- OpenAlex
- W4410197190
- Cited by
- 280
- References
- 45
- Citations by year
- 2026: 217 · 2025: 61 · 2024: 2
- FWCI
- 26.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Array (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- agentic AI, generative AI agents, organizational performance, autonomous decision-making, hierarchical agent architecture, LangChain, CrewAI, AutoGen, AI adoption strategy, privacy and security risks, AI ethics, labor market outcomes
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