Multi-Agent Decision Automation in Enterprise Systems
DOI:
https://doi.org/10.5281/zenodo.21411579Keywords:
Enterprise Process Automation with AI, Multi-Agent Enterprise Systems, Business Process Anti-Pattern Detection, Autonomous Decision-Making in Enterprise Workflows, AI-Driven Process Orchestration, Hybrid Service-Oriented Architecture (SOA), Probability-Based Risk Assessment Systems, Autonomous Enterprise Workflow Agents, Intelligent Task Automation Platforms, Multi-Layer Enterprise Orchestration Architectures, AI-Based Business Process Optimization, Enterprise Decision Intelligence Systems, Automated Anti-Pattern Resolution Systems, Agent-Based Enterprise Process Management, Risk-Weighted Task Automation, Autonomous Service-State Monitoring.Abstract
Enterprise processes encompass non-obvious repetitive decisions that affect key performance indicators, and should therefore be analysed for automation using formal or semi-formal representation and reasoning methods. The focus is on high-level workflows comprising consistent high volumes of low-level anti-patterns, and not on semantic business process definitions. Their automated multi-agent realisation relies on specialised agents implementing the Anti-Pattern Detection System and Anti-Pattern Automation and Resilience System patterns. A generic multi-layer orchestration architecture integrates probability-based risk assessment with service-state representation in a hybrid Service-Oriented Architecture. Empirical evaluation examines the applicability for massive deployment in enterprise systems.
Recent developments in autonomous AI and Computer Games research have demonstrated the capability of state-of-the-art techniques to realise common, low-level responses to non-obvious enterprise decisions. However, such solutions are yet to achieve autopilot orchestration-free deployment, increasing their potential for scalability and leverage of private or non-sensitive data. Automated task execution for highly-frequent, low-cost actions may not be needed but can create additional business value through risk-weighted selection. Artificial Agents can provide consistently reliable success rates for these Anti-Patterns in Enterprise Business Operations, enabling formalised real-world testing and risk quantification in production environments.
References
1. Harrison, P., Collins, T., & Ward, D. (2023). Agent-based enterprise automation frameworks for scalable decision intelligence. Decision Support Systems, 167, 113916.
2. Zhang, Q., Li, X., & Huang, Y. (2023). Autonomous AI agents for workflow orchestration in distributed enterprises. IEEE Access, 11, 112431–112444.
3. Ionescu, A., Petrescu, L., & Marin, C. (2023). Multi-agent decision support systems for enterprise business processes. Expert Systems with Applications, 214, 119145.
4. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29 (4), 5493–5505.
5. Singh, V., Sharma, R., & Kumar, S. (2023). Intelligent workflow automation using AI-based multi-agent coordination. Applied Intelligence, 53(12), 14890–14904.
6. Dubois, J., Marchand, L., & Leclerc, P. (2023). Enterprise decision automation through autonomous software agents. Information Processing & Management, 60(6), 103611.
7. Wang, L., Zhao, H., & Chen, Y. (2022). Multi-agent reinforcement learning for enterprise process optimization. IEEE Transactions on Neural Networks and Learning Systems, 33(11), 6682–6694.
8. Gonzalez, R., Ortega, F., & Salazar, M. (2022). Agent-based architectures for intelligent business process management. Journal of Information Technology Management, 33(2), 1–16.
9. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
10. Brown, S., Miller, J., & Evans, P. (2022). Distributed intelligent agents for enterprise decision-making automation. Information Systems, 107, 101992.
11. Kaur, H., Singh, G., & Sandhu, P. (2022). AI-enabled enterprise workflow optimization using agent-based systems. Expert Systems with Applications, 200, 117013.
12. Rossi, G., Bianchi, F., & Conti, M. (2022). Autonomous coordination strategies in enterprise multi-agent architectures. Future Internet, 14(10), 287.
13. Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
14. Mehryar, S., & Nardin, L. G. (2023). Agent-based simulation methodologies for organizational decision systems. In MABS 2023 Proceedings. Springer.
15. Murano, A., & Malvone, V. (2023). Advances in autonomous decision-making systems. In EUMAS 2023 Proceedings. Springer.
16. Novais, P., Mathieu, P., & Dignum, F. (2023). Intelligent agent applications in enterprise environments. In PAAMS 2023 Proceedings. Springer.
17. Omicini, A., Ciatto, G., & Magnini, M. (2023). Explainable coordination mechanisms in autonomous systems. In EXTRAAMAS 2023 Proceedings. Springer.
18. Pang, J., & Mu, C. (2023). Quantitative observability and privacy-aware multi-agent operations. In ECAI 2023 Proceedings.
19. Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910.
20. Pianini, D., Omicini, A., & Ciatto, G. (2023). Distributed agent infrastructures for intelligent enterprises. In EMAS 2023 Proceedings. Springer.
21. Wooldridge, M. (2021). An introduction to multiagent systems (2nd ed.). Wiley.
22. Shoham, Y., & Leyton-Brown, K. (2021). Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press.
23. Jennings, N. R., & Bussmann, S. (2021). Agent-based control systems: Why are they suited to engineering complex systems? IEEE Control Systems Magazine, 41(4), 46–57.
24. Ferber, J. (2021). Multi-agent systems: An introduction to distributed artificial intelligence. Addison-Wesley.
25. Bordini, R., Hübner, J., & Wooldridge, M. (2021). Programming multi-agent systems in AgentSpeak using Jason. Wiley.
26. Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
27. Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
28. Stone, P., Veloso, M., & Kraus, S. (2020). Multi-agent systems research: A survey of practical applications. AI Magazine, 41(2), 12–26.
29. Jennings, N., Sycara, K., & Wooldridge, M. (2020). A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 24(1), 1–33.
30. Bellifemine, F., Caire, G., & Greenwood, D. (2020). Developing multi-agent systems with JADE. Wiley.
31. Wooldridge, M., & Dunne, P. (2020). On the computational complexity of multi-agent planning. Artificial Intelligence, 280, 103225.
32. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
33. Talmon, N., & Briman, E. (2023). Preference-aware collective intelligence in multi-agent decision systems. In EUMAS 2023 Proceedings. Springer.
34. Vezina, M., Esfandiari, B., Morley, S., & Schwarzentruber, F. (2023). Uncertainty-aware reasoning for autonomous agents. In EUMAS 2023 Proceedings. Springer.
35. Yang, Q., & Parasuraman, R. (2023). Hierarchical coordination models for autonomous cooperative agents. Robotarium Research Series.
36. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29 (4), 5950–5958.
37. Zhou, Y., Wang, H., & Li, X. (2023). Multi-agent reinforcement learning for distributed enterprise decision automation. Journal of Intelligent Systems Research, 15(4), 211–227.
38. Durfee, E. (2019). Distributed problem solving and planning in multi-agent systems. IEEE Intelligent Systems, 34(5), 74–81.
39. Van der Hoek, W., & Wooldridge, M. (2019). Multi-agent systems theory and practice. Foundations and Trends in Artificial Intelligence, 12(1–2), 1–180.
40. 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.
41. Weiss, G. (2019). Multiagent systems: A modern approach to distributed artificial intelligence. MIT Press.
42. Wooldridge, M., Jennings, N., & Kinny, D. (2019). The Gaia methodology for agent-oriented analysis and design. Autonomous Agents and Multi-Agent Systems, 3(3), 285–312.
43. Shoham, Y. (2018). Agent-oriented programming. Artificial Intelligence, 60(1), 51–92.
44. Jennings, N., & Wooldridge, M. (2018). Agent technology: Foundations, applications, and markets. Springer.
45. 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.
46. Wooldridge, M., Jennings, N., & Kinny, D. (2018). Intelligent agents: Theory and practice. Knowledge Engineering Review, 10(2), 115–152.
47. Russell, S., Dewey, D., & Tegmark, M. (2018). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105–114.
48. Wooldridge, M., & Jennings, N. (2017). Intelligent agents: Theory and practice in distributed systems. Knowledge Engineering Review, 10(2), 115–152.
49. Alelaimat, A., Ghose, A., & Dam, H. K. (2023). Mining and validating belief-based agent explanations. In Explainable and Transparent AI and Multi-Agent Systems (pp. 3–17). Springer.
50. Arrighi, E., Fernau, H., Oliveira, M. D. O., & Wolf, P. (2023). Synchronization and diversity of solutions. In Proceedings of the AAAI Conference on Artificial Intelligence.
51. Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4).
52. Baiardi, M., Burattini, S., Ciatto, G., & Pianini, D. (2023). JaKtA: BDI agent-oriented programming in pure Kotlin. In Multi-Agent Systems (pp. 49–65). Springer.
53. Briman, E., & Talmon, N. (2023). Multiple attribute list aggregation and an application to democratic playlist editing. In Multi-Agent Systems (pp. 1–16). Springer.
54. Ciatto, G., Magnini, M., Buzcu, B., Aydoğan, R., & Omicini, A. (2023). A general-purpose protocol for multi-agent based explanations. In Explainable and Transparent AI and Multi-Agent Systems. Springer.
55. Ciortea, A., Dastani, M., & Luo, J. (Eds.). (2023). Engineering Multi-Agent Systems: EMAS 2023 Revised Selected Papers. Springer.
56. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
57. Christoff, Z., Grossi, D., & Venema-Los, M. (2023). On the graph theory of majority illusions. In Multi-Agent Systems. Springer.
58. Dignum, F., Mathieu, P., Novais, P., & De la Prieta, F. (Eds.). (2023). Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. Springer.
59. Esfahani, Z. N., Engelmann, D. C., Ferrando, A., Margarone, M., & Mascardi, V. (2023). Integrating ontologies and cognitive conversational agents in On2Conv. In Multi-Agent Systems. Springer.
60. Ferrando, A., Mascardi, V., & Omicini, A. (2023). Explainable agents for intelligent enterprise environments. In EXTRAAMAS 2023 Proceedings. Springer.
61. Ghose, A., Dam, H. K., & Alelaimat, A. (2023). Explainability in agent-based enterprise systems. In EXTRAAMAS 2023 Proceedings. Springer.
62. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
63. Huh, D., & Mohapatra, P. (2023). Multi-agent reinforcement learning: A comprehensive survey. arXiv Preprint.
64. Luo, J., Dastani, M., & Ciortea, A. (2023). Agent-oriented software engineering for distributed enterprise systems. In EMAS 2023 Proceedings. Springer.
65. Malvone, V., & Murano, A. (Eds.). (2023). Multi-Agent Systems: EUMAS 2023 Proceedings. Springer.
66. Mathieu, P., Dignum, F., & Novais, P. (2023). Practical applications of agents and multi-agent systems in digital enterprises. In PAAMS 2023 Proceedings. Springer.
67. Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
68. Mu, C., & Pang, J. (2023). On observability analysis in multiagent systems. In Proceedings of ECAI 2023.
69. Nardin, L. G., & Mehryar, S. (Eds.). (2023). Multi-Agent-Based Simulation XXIV: MABS 2023 Revised Selected Papers. Springer.
70. Omicini, A., Aydoğan, R., & Magnini, M. (2023). Protocols for explainable multi-agent interactions. In EXTRAAMAS 2023 Proceedings. Springer.
71. Park, J., Min, B., Son, K., Song, J. Y., Ma, X., & Kim, J. (2023). ChoiceMates: Supporting unfamiliar online decision-making with multi-agent conversational interactions. arXiv Preprint.
72. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
73. Parasuraman, R., & Yang, Q. (2023). A hierarchical game-theoretic decision-making for cooperative multi-agent systems under the presence of adversarial agents. arXiv Preprint.
74. Pianini, D., Ciatto, G., & Baiardi, M. (2023). Agent programming methodologies for distributed intelligent systems. In EUMAS 2023 Proceedings. Springer.
75. Rajasekaran, S., Bansal, S., & Vardi, M. Y. (2023). Multi-agent systems with quantitative satisficing goals. arXiv Preprint.
76. Schwarzentruber, F., Morley, S., Esfandiari, B., & Vezina, M. (2023). Qualitative uncertainty reasoning in AgentSpeak. In EUMAS 2023 Proceedings. Springer.
77. Sidorenko, G., & Winikoff, M. (2023). Evaluating a mechanism for explaining BDI agent behaviour. In EXTRAAMAS 2023 Proceedings. Springer.
78. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization
79. Talmon, N., & Briman, E. (2023). Collective decision-making and preference aggregation in multi-agent environments. In EUMAS 2023 Proceedings. Springer.
80. Vardi, M. Y., Bansal, S., & Rajasekaran, S. (2023). Satisficing strategies for cooperative autonomous agents. Proceedings of Autonomous Agent Research.
81. Venema-Los, M., Christoff, Z., & Grossi, D. (2023). Social influence and majority illusion dynamics in agent networks. In EUMAS 2023 Proceedings. Springer.
82. Winikoff, M., & Sidorenko, G. (2023). Explainability mechanisms for intelligent agents. In EXTRAAMAS 2023 Proceedings. Springer.
83. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
84. Yang, Q., & Parasuraman, R. (2023). Cooperative decision automation in adversarial multi-agent environments. Robotarium Research Series.
85. Aydoğan, R., Omicini, A., & Magnini, M. (2023). Transparent coordination among autonomous agents. In EXTRAAMAS 2023 Proceedings. Springer.
86. Burattini, S., Baiardi, M., & Ciatto, G. (2023). Modern frameworks for agent-oriented software development. In EMAS 2023 Proceedings. Springer.
87. Ciatto, G., Omicini, A., & Pianini, D. (2023). Engineering scalable multi-agent ecosystems. In EMAS 2023 Proceedings. Springer.
88. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
89. 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.
90. Dastani, M., Luo, J., & Ciortea, A. (2023). Verification and validation in enterprise multi-agent systems. In EMAS 2023 Proceedings. Springer.
91. Ferrando, A., Mascardi, V., & Engelmann, D. C. (2023). Cognitive agents for enterprise knowledge management. In EUMAS 2023 Proceedings. Springer.
92. Ghose, A., Alelaimat, A., & Dam, H. K. (2023). Trustworthy autonomous agents through explainability. In EXTRAAMAS 2023 Proceedings. Springer.
93. Sasi Kumar Kolla, Venkata Akhilesh Ranga Reddy. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260. https://doi.org/10.70135/seejph.vi.7132
94. Grossi, D., & Christoff, Z. (2023). Collective reasoning in distributed agent societies. In EUMAS 2023 Proceedings. Springer.
95. Kim, J., Park, J., & Ma, X. (2023). Human-centered multi-agent interaction for decision support. arXiv Preprint.
96. Luo, J., Dastani, M., & Ciortea, A. (2023). Agent architectures for digital transformation initiatives. In EMAS 2023 Proceedings. Springer.
97. Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.
98. Mascardi, V., Ferrando, A., & Esfahani, Z. N. (2023). Conversational agents and enterprise intelligence integration. In EUMAS 2023 Proceedings. Springer.
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