Secure Multi-Agent RAG for Enterprise Automation
Keywords:
Enterprise Automation, Multi Agent, Retrieval Augmented, AI Systems, Agent Architecture, Data Retrieval, Knowledge Systems, Decision Making, Data Governance, Data Provenance, System Security, Risk Mitigation, Policy Enforcement, Scalable Systems, Modular Design, Open Interfaces, Information Access, Query Processing, System Evaluation, Intelligent Systems.Abstract
Automating enterprise operations offers the promise of substantially increased agility and competitive advantage. However, enterprises are still constrained in their ability to leverage such innovations for many operations beyond routine transaction processing. Security and oversight requirements ordinarily preclude the deployment of advanced AI techniques in data-rich enterprise context. Recently proposed retrieval-augmented AI designs provide a means to address some of these limitations, enabling formal institutions to enforce policy and mitigate risk, even when operations require complex reasoning and data synthesis. Yet no formal framework for retrieval-augmented multi-agent systems exists. Such a framework is necessary to support comprehensive validation and evaluation of emerging systems while also guiding practical implementations. Providing such an underpinning costitutes the principal contribution of this work.
The contributions of this study formalize the design of retrieval-augmented multi-agent systems. A foundation architectural model is defined, linking agent modularity, open interfaces, and scalable operation to retrieval-augmented operation. Grounding the architecture for enterprise contexts supports a comprehensive theory of retrieval-augmented multi-agent systems. Security and governed operation are addressed via consideration of (1) agent role definitions; (2) data access and provenance management; (3) strategic behaviour, information source maintenance, and understandable queries. Together, these aspects provide the procedural scaffolding necessary for scientifically-grounded operational and performance evaluations, endorsing the continued development of novel retrieval-augmented multi-agent systems for enterprise automation.
References
1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
2. Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. Proceedings of EMNLP, 6769–6781.
3. Mattaparthi, R. (2023). Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9077-9088.
4. Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. Proceedings of SIGIR, 39–48.
5. Beltagy, I., Peters, M. E., & Cohan, A. (2020). Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150.
6. Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M.-W. (2020). REALM: Retrieval-augmented language model pre-training. Proceedings of ICML, 3929–3938.
7. Nagubandi, A. R. (2023). Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 653-674.
8. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
9. Johnson, J., Douze, M., & Jégou, H. (2020). Billion-scale similarity search with GPUs. IEEE Transactions on Big Data, 7(3), 535–547.
10. Reimers, N., & Gurevych, I. (2020). Making monolingual sentence embeddings multilingual using knowledge distillation. Proceedings of EMNLP, 4512–4525.
11. Vaswani, A., Gomez, A. N., Jones, L., Kaiser, Ł., & Polosukhin, I. (2020). Attention mechanisms in large-scale transformers. Journal of Machine Learning Research, 21(140), 1–32.
12. Kolla, S. K. (2022). Engineering Healthcare Data Infrastructures for Predictive Clinical Analytics and Evidence-Based Decision Making. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5370-5380.
13. Chen, W., Wang, Z., Xiong, C., & Callan, J. (2020). Open-domain question answering with dense retrieval. Information Retrieval Journal, 23(6), 561–585.
14. Izacard, G., & Grave, E. (2021). Leveraging passage retrieval with generative models for open domain question answering. Proceedings of EACL, 874–880.
15. Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., & Sifre, L. (2021). Improving language models by retrieving from trillions of tokens. Proceedings of ICML, 2206–2240.
16. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2021). React-style reasoning for intelligent agents. arXiv preprint arXiv:2110.03629.
17. Kolla, S. H., & Loganathan, R. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research, 603-618.
18. Bommasani, R., Hudson, D., Adeli, E., Altman, R., Arora, S., von Arx, S., & Liang, P. (2021). On the opportunities and risks of foundation models. Stanford CRFM Report.
19. Lin, J., Nogueira, R., & Yates, A. (2021). Pretrained transformers for text ranking. Synthesis Lectures on Human Language Technologies, 14(4), 1–325.
20. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., & Liu, P. (2021). Exploring transfer learning with text-to-text transformers. Journal of Machine Learning Research, 21(140), 1–67.
21. Wang, X., Yue, Y., & Huang, T. (2021). Secure knowledge retrieval in enterprise AI systems. IEEE Access, 9, 117234–117249.
22. Zhang, Y., Chen, Q., & Li, H. (2021). Knowledge graph enhanced retrieval systems for enterprise search. Expert Systems with Applications, 181, 115193.
23. Sutton, R., & Barto, A. (2021). Reinforcement learning: An introduction (2nd ed.). MIT Press.
24. Amistapuram, K. Energy-Efficient System Design for High-Volume Insurance Applications in Cloud-Native Environments. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
25. Wooldridge, M. (2021). An introduction to multiagent systems (2nd ed.). Wiley.
26. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Xia, F., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837.
27. Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., & Dean, J. (2022). PaLM: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.
28. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., & Petroni, F. (2022). Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2205.12255.
29. Kolla, T., & Kolla, S. K. (2023). FHIR-Based Real-Time Healthcare Analytics using Unsupervised Learning. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11751.
30. Mialon, G., Dessì, R., Lomeli, M., Ebrahimi, M., Alon, U., & Petroni, F. (2022). Augmented language models. Transactions of Machine Learning Research.
31. OpenAI. (2022). GPT-3 and enterprise automation workflows. Technical Report.
32. Wang, P., Li, Y., & Sun, X. (2022). Enterprise document intelligence using transformer retrieval models. Knowledge-Based Systems, 246, 108676.
33. Kumar, A., Singh, R., & Sharma, P. (2022). AI-driven enterprise automation with intelligent agents. Future Generation Computer Systems, 130, 250–264.
34. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
35. Li, M., Zhao, J., & Xu, W. (2022). Trust-aware retrieval architectures for secure AI. IEEE Transactions on Knowledge and Data Engineering, 34(11), 5208–5221.
36. Dignum, V. (2022). Responsible artificial intelligence. Springer.
37. Russell, S., & Norvig, P. (2022). Artificial intelligence: A modern approach (4th ed.). Pearson.
38. Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., & Yang, Y. (2023). Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems.
39. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
40. Yao, S., Yu, B., Zhao, J., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations.
41. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., & Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
42. Jiang, Z., Xu, F., Gao, J., & Li, C. (2023). Secure retrieval pipelines for enterprise RAG systems. IEEE Access, 11, 92310–92325.
43. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
44. Peng, B., Galley, M., He, P., Brockett, C., Liden, L., & Dolan, B. (2023). Check your facts and try again. ACL Proceedings.
45. Sun, Z., Schwenk, H., & Li, H. (2023). Hybrid retrieval strategies for large-scale enterprise QA. Information Processing & Management, 60(4), 103354.
46. Pan, S., Li, J., & Zheng, Z. (2023). Multi-agent collaboration in AI automation platforms. Future Internet, 15(8), 251.
47. Nagabhyru, K. C., & Engineer, S. D. (2023). Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment.
48. Xi, Z., Chen, W., Guo, X., & Wang, W. (2023). Large language models and agent systems: A survey. arXiv preprint arXiv:2308.11432.
49. Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. Microsoft Research Technical Report.
50. Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., & Wang, Z. (2023). MetaGPT: Meta programming for multi-agent systems. arXiv preprint arXiv:2308.00352.
51. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
52. Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. NeurIPS Workshop.
53. Qin, Y., Liang, S., Ye, Y., Zhu, K., & Xu, R. (2023). Tool learning in LLM agents. arXiv preprint arXiv:2305.17126.
54. Li, Y., Wang, Z., & Hu, J. (2023). Enterprise automation through multi-agent orchestration. IEEE Transactions on Services Computing.
55. Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.
56. Chen, R., Xu, J., & Lin, H. (2023). Access-controlled retrieval augmentation for secure enterprise AI. Expert Systems with Applications, 224, 119945.
57. Gao, L., Ma, X., Chen, W., & Liu, Q. (2023). Hallucination mitigation in retrieval-augmented generation systems. Knowledge-Based Systems, 276, 110775.
58. Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
59. Park, J., O’Brien, J., Cai, C., Morris, M., Liang, P., & Bernstein, M. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of UIST, 1–22.
60. Xiong, W., Sun, Y., & Tang, J. (2023). Knowledge-grounded generation in enterprise LLM systems. ACM Computing Surveys.
61. Zhang, K., Liu, H., & Zhao, P. (2023). Secure vector databases for AI knowledge retrieval. IEEE Access, 11, 101221–101239.
62. Shen, T., Huang, R., & Wang, X. (2023). Multi-agent planning for intelligent enterprise workflows. Applied Intelligence, 53(14), 16211–16229.
63. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1-14.
64. Li, C., Zhao, H., & Wen, M. (2023). AI governance frameworks for enterprise generative systems. Journal of Information Security, 14(3), 201–217.
65. Minsky, M. (2023). Society of mind inspired agent architectures in modern AI. AI Magazine, 44(2), 55–66.
66. Yang, J., Kim, S., & Choi, H. (2023). Trustworthy orchestration for autonomous enterprise agents. Computers & Security, 129, 103214.
67. Zhao, Y., & Wang, L. (2023). Policy-aware LLM agents for secure automation. Journal of Systems Architecture, 141, 102931.
68. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
69. He, X., Chen, Z., & Li, Y. (2023). Federated retrieval for secure enterprise knowledge sharing. IEEE Internet of Things Journal, 10(18), 16114–16129.
70. Singh, N., Gupta, V., & Roy, S. (2023). RAG pipelines for document intelligence automation. Procedia Computer Science, 218, 188–197.
71. Kumar, V., Patel, D., & Shah, A. (2023). Context-aware agent orchestration using LLMs. Software: Practice and Experience, 53(11), 2271–2288.
72. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
73. Lee, J., Kim, D., & Park, S. (2023). Secure prompt engineering for enterprise LLM deployment. IEEE Software, 40(6), 72–79.
74. Brown, M., Smith, T., & Wilson, R. (2023). AI workflow orchestration for digital enterprises. MIS Quarterly Executive, 22(4), 231–247.
75. Chen, X., Liu, Y., & Zhao, Q. (2023). Intelligent enterprise copilots with retrieval augmentation. Computers in Industry, 151, 103978.
76. Roy, A., Banerjee, S., & Dutta, P. (2023). Event-driven AI agents for enterprise systems. Journal of Systems and Software, 204, 111807.
77. Kolla, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248-260.
78. Patel, H., Shah, R., & Joshi, K. (2023). Autonomous enterprise decision agents using RAG. Applied Soft Computing, 143, 110449.
79. Gomez, R., & Fernandez, P. (2023). Zero-trust AI architectures for enterprise automation. Computer Networks, 235, 109982.
80. Ibrahim, A., Rahman, S., & Noor, M. (2023). Secure memory management in multi-agent AI systems. Concurrency and Computation, 35(21), e7832.
81. Martin, E., Lewis, D., & Harper, J. (2023). Explainable AI agents in enterprise knowledge systems. Expert Systems, 40(6), e13402.
82. Bandi, V. D. V. K. Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics.
83. Verma, P., Singh, R., & Gupta, A. (2023). Role-based access control for generative AI pipelines. Information Systems Frontiers, 25(6), 2487–2502.
84. Das, S., Mukherjee, A., & Roy, P. (2023). Multi-agent reinforcement learning for workflow automation. Engineering Applications of Artificial Intelligence, 124, 106525.
85. Morales, J., Chen, F., & Li, T. (2023). Privacy-preserving enterprise LLM architectures. Computers & Security, 132, 103401.
86. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
87. Wilson, P., Green, J., & Carter, L. (2023). Secure enterprise copilots with retrieval intelligence. AI & Society, 38(4), 1821–1835.
88. Harrison, D., Clark, M., & Young, S. (2023). Scalable vector search for enterprise generative AI. Journal of Big Data, 10(1), 87.
89. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
90. Ahmed, S., Khan, M., & Ali, R. (2023). Secure multi-agent architectures for enterprise AI automation. Future Generation Computer Systems, 149, 164–179.
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