RAG Agentic AI as Digital Frontline in Service-Based Organizations: A Systematic Literature Review

Najmi Khairunnisa; Riska Dhenabayu; Syahda Hati Dendra

doi:10.26740/jdbim.v5i1.77596

Authors

Najmi Khairunnisa Universitas Negeri Surabaya
Riska Dhenabayu Universitas Negeri Surabaya
Syahda Hati Dendra Institut Teknologi Bandung

DOI:

https://doi.org/10.26740/jdbim.v5i1.77596

Keywords:

Agentic AI, Retrieval-Augmented Generation, Digital Frontline, Conversational AI, Autonomous Agents

Abstract

This study systematically reviews the development of Agentic AI as digital frontline systems within service-based organizations. The increasing adoption of artificial intelligence in organizational environments has accelerated the implementation of conversational systems capable of supporting consultation workflows, operational communication, and information delivery processes. However, conventional chatbot systems still experience limitations related to contextual understanding, reasoning capability, and multi-step task execution. To address these limitations, recent studies increasingly integrate Agentic AI and Retrieval-Augmented Generation (RAG) to support more adaptive and context-aware conversational systems. This study employed a Systematic Literature Review (SLR) approach following the PRISMA guidelines. Literature selection was conducted using several academic databases, including Scopus, IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar. After the screening and eligibility process, 61 articles published between 2020 and 2026 were selected for final review and analyzed using thematic synthesis. The findings identified three dominant research themes consisting of Agentic AI architectures and autonomous systems, Retrieval-Augmented Generation and conversational intelligence, and deployment of Agentic AI in digital frontline systems. The review demonstrates that Agentic AI integrated with retrieval-enhanced conversational mechanisms has strong potential to support contextual interaction, consultation workflows, and organizational communication processes within service-based environments. Nevertheless, several challenges remain related to hallucination, governance, trustworthiness, evaluation reliability, and real-world deployment complexity.

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References

Ali, M. A., & Dornaika, F. (2025). Agentic AI: A Comprehensive Survey of Architectures, Applications, and Future Directions. https://doi.org/10.1007/s10462-025-11422-4

Asai, A., Wu, Z., Wang, Y., Sil, A., & Hajishirzi, H. (2023). Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection. http://arxiv.org/abs/2310.11511

Chen, W., Su, Y., Zuo, J., Yang, C., Yuan, C., Chan, C.-M., Yu, H., Lu, Y., Hung, Y.-H., Qian, C., Qin, Y., Cong, X., Xie, R., Liu, Z., Sun, M., & Zhou, J. (2023). AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors. http://arxiv.org/abs/2308.10848

Edge, D., Trinh, H., Cheng, N., Bradley, J., Chao, A., Mody, A., Truitt, S., Metropolitansky, D., Ness, R. O., & Larson, J. (2025). From Local to Global: A Graph RAG Approach to Query-Focused Summarization. http://arxiv.org/abs/2404.16130

Harvard Business Review. (2025). Empowering Frontline Work with Digital Transformation and AI. https://share.google/fce0zLp34LoNyWUm0

Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., Wu, C., & Schmidhuber, J. (2024). MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. http://arxiv.org/abs/2308.00352

Hosseini, S., & Seilani, H. (2025). The role of agentic AI in shaping a smart future: A systematic review. In Array (Vol. 26). Elsevier B.V. https://doi.org/10.1016/j.array.2025.100399

Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2025). A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Transactions on Information Systems, 43(2). https://doi.org/10.1145/3703155

Huang, Y., & Huang, J. (2024). A Survey on Retrieval-Augmented Text Generation for Large Language Models. https://doi.org/10.1145/3805774

Jeong, S., Baek, J., Cho, S., Hwang, S. J., & Park, J. C. (2024). Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity. http://arxiv.org/abs/2403.14403

Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., & McHardy, R. (2023). Challenges and Applications of Large Language Models. http://arxiv.org/abs/2307.10169

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2021). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. http://arxiv.org/abs/2005.11401

Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., & Ghanem, B. (2023). CAMEL: Communicative Agents for “Mind” Exploration of Large Language Model Society. http://arxiv.org/abs/2303.17760

Monteiro, M. de S., & Salgado, L. C. de C. (2023). Conversational agents: a survey on culturally informed design practices. Journal on Interactive Systems, 14(1), 33–46. https://doi.org/10.5753/jis.2023.2961

Ngai, E. W. T., Lee, M. C. M., Luo, M., Chan, P. S. L., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50. https://doi.org/10.1016/j.elerap.2021.101098

Nisa, U., Shirazi, M., Saip, M. A., & Pozi, M. S. M. (2025). Agentic AI: The age of reasoning—A review. Journal of Automation and Intelligence. https://doi.org/10.1016/j.jai.2025.08.003

Packer, C., Wooders, S., Lin, K., Fang, V., Patil, S. G., Stoica, I., & Gonzalez, J. E. (2024). MemGPT: Towards LLMs as Operating Systems. http://arxiv.org/abs/2310.08560

Pati, A. K. (2025). Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications. IEEE Access, 13, 151824–151837. https://doi.org/10.1109/ACCESS.2025.3585609

Qin, Y., Liang, S., Ye, Y., Zhu, K., Yan, L., Lu, Y., Lin, Y., Cong, X., Tang, X., Qian, B., Zhao, S., Hong, L., Tian, R., Xie, R., Zhou, J., Gerstein, M., Li, D., Liu, Z., & Sun, M. (2023). ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs. http://arxiv.org/abs/2307.16789

Roy, S. K., Tehrani, A. N., Pandit, A., Apostolidis, C., & Ray, S. (2025). Ai-capable relationship marketing: Shaping the future of customer relationships. Journal of Business Research, 192. https://doi.org/10.1016/j.jbusres.2025.115309

Sapkota, R., Roumeliotis, K. I., & Karkee, M. (2026). AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges. Information Fusion, 126. https://doi.org/10.1016/j.inffus.2025.103599

Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools. http://arxiv.org/abs/2302.04761

Shavit, Y., Agarwal, S., Brundage, M., Adler, S., O’keefe, C., Campbell, R., Lee, T., Mishkin, P., Eloundou, T., Hickey, A., Slama, K., Ahmad, L., Mcmillan, P., Beutel, A., Passos, A., & Robinson, D. G. (2023). Practices for Governing Agentic AI Systems.

https://openai.com/index/practices-for-governing-agentic-ai-systems/

Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2024). Cognitive Architectures for Language Agents. http://arxiv.org/abs/2309.02427

Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., & Wang, C. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. http://arxiv.org/abs/2308.08155

Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., Zheng, R., Fan, X., Wang, X., Xiong, L., Zhou, Y., Wang, W., Jiang, C., Zou, Y., Liu, X., … Gui, T. (2023). The Rise and Potential of Large Language Model Based Agents: A Survey. http://arxiv.org/abs/2309.07864

Yan, S.-Q., Gu, J.-C., Zhu, Y., & Ling, Z.-H. (2024). Corrective Retrieval Augmented Generation. http://arxiv.org/abs/2401.15884