Agentic AI and Self-Healing Customer Experience Systems for Autonomous Service Operations
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V7I1P109Keywords:
Agentic AI, Self-Healing Systems, Autonomous Agents, Cognitive Service Routing, Zero-Touch Remediation, Multi-Agent SystemsAbstract
New customer experiences (CX) are more reliant than ever on distributed digital services, real-time personalisation, and constant service availability. However, traditional customer service channels are largely fragmented, its isolated operations, and fails to proactively solve issues like operations interruption, customer friction and workflow defects in real time. In this research, the need for smart and resilient service infrastructures is addressed by proposing an agentic AI based self-healing service experience solution of autonomous service operations. It combines autonomous AI agents, predictive analytics, event-driven orchestration, reinforcement learning, and self-healing operational intelligence to proactively identify any anomalies in the services being delivered, estimate potential customer dissatisfaction, optimize service workflow, and autonomously correct any issues with the system without requiring human actions where possible. It fuses multi-agent coordination, contextual decision intelligence, real-time telemetry processing, and AI-based orchestration layers to form adaptive and continuously-learning customer service ecosystems that can recover from operations seamlessly as needed – and make decisions based on unique perspectives for each customer. The study shows that, the proposed autonomous CX framework enhanced customer satisfaction, operational resilient, successful completion of the workflow and mean time to recovery (MTTR) as compared to traditional rule-based customer support framework. Experimental testing shows significant gains in predictive issue resolution, intelligent customer routing, and autonomous remediation accuracy by applying AI-driven decision orchestration and pipelines for remediation. The study also introduces an explainable AI-governed secure event-driven processing and adaptive customer intelligence architectural framework, which enables scalable autonomous enterprise-scale service operations. The impact and use of this work spans enterprise customer support platforms, cloud-native digital services, financial systems, healthcare operations, and large-scale omnichannel engagement platforms where agile, intelligent and autonomous customer experience management is critical requirement for businesses to survive and thrive.
References
[1] Pemmasani, P. K., & Rock, D. (2023). The Impact of Ransomware on Government Agencies: Lessons Learned and Future Strategies. International Journal of Modern Computing, 6(1), 64-74.
[2] Thalary, S. (2024). From Pipelines to Policy: Embedding AI-Ready Governance into Cloud DevOps at Scale. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 200-210.
[3] Gudepu, B. K., Jaladi, D. S., & Gellago, O. (2023). How Data Catalogs are Transforming Enterprise Data Governance: A Systematic Literature Review. The Metascience, 1(1), 249-264.
[4] Katipelly, A. (2024). Predictive AI Proactive Customer Engagement Platform and Real-Time Friction Reduction Using AI-Based Churn Prediction. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 211-221.
[5] Kuntamukkala, N. K., & Thalary, S. (2024). Intelligent Angular Architecture: Machine Learning-Based Component Recommendation Systems for Enterprise-Scale Development. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 276-284.
[6] Kuntamukkala, N. K. (2023). Optimizing Enterprise SPAs: Angular Standalone Components and Signals. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 189-200.
[7] Thalary, S. (2023). Monitoring Isn’t Observability: Lessons from Running Enterprise Microservices. International Journal of Emerging Research in Engineering and Technology, 4(2), 139-148.
[8] Katipelly, A., & Thalary, S. (2023). Cryptographic Identity Propagation in Asynchronous Event-Driven Architectures: Implementing Zero-Trust Envelopes for High-Velocity Payment Streams. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 212-222.
[9] Pemmasani, P. K. (2023). AI in national security: Leveraging machine learning for threat intelligence and response. The Computertech, 1-10.
[10] Gudepu, B. K., & Eichler, R. (2024). The role of AI in enhancing data governance strategies. International Journal of Acta Informatica, 3(1), 169-186.
[11] Pemmasani, P. K., & Okara, C. (2024). Machine Learning Models for Predicting Ransomware Attacks on Critical Public Health Infrastructure: A Cross-National Study. The Metascience, 2(2), 75-85.
[12] Pemmasani, P. K. (2024). Behavioral Analytics for Detecting Insider Threats in Governmental Organizations: A Human-Centric Approach. International Journal of Acta Informatica, 3(1), 138-148.
[13] Katipelly, A. (2024). Hierarchical Agentic Orchestration for Microservices: A Neuro-Symbolic Framework for Dynamic Workflow Composition in Decentralized Financial Systems. International Journal of Emerging Research in Engineering and Technology, 5(4), 165-174.
[14] Pemmasani, P. K. (2023). National cybersecurity frameworks for critical infrastructure: Lessons from governmental cyber resilience initiatives. International Journal of Acta Informatica, 2(1), 209-218.
[15] Kuntamukkala, N. K. (2024). Self-Healing Angular Architecture: AI-Driven Autonomous Error Recovery and System Resilience. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 219-230.
[16] Katipelly, A., & Thalary, S. (2024). Semantic Automation of Basel III Liquidity Reporting: Utilizing Ontological Knowledge Graphs for Real-Time Regulatory Compliance and Auditability. International Journal of Emerging Research in Engineering and Technology, 5(2), 147-156.
[17] Pemmasani, P. K., & Rock, D. (2023). Cloud Storage Security in Government Agencies: Protecting National Data from Cyber Threats. The Metascience, 1(1), 239-248.
[18] Thalary, S., & Katipelly, A. (2023). Secure-by-Design Cloud Software Delivery: How DevOps and Software Teams Co-Own Security Outcomes. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(1), 131-140.
[19] Kuntamukkala, N. K., & Katipelly, A. (2023). Predictive Angular Rendering: Machine Learning Models for Intelligent Client-Side Optimization with Adaptive Backend Coordination. International Journal of AI, BigData, Computational and Management Studies, 4(2), 144-154.
[20] Pemmasani, P. K. (2024). Cyber Insurance and Risk Transfer Mechanisms for Public Health Entities: Evaluating Post-Attack Financial Recovery. The Computertech, 1-10.
[21] Patel, K. (2024). Agentic AI for Self-Healing Production Lines: Autonomous Root Cause Analysis & Correction. Journal of Information Systems Engineering and Management, 9, 124-135.
[22] Parise, S., Guinan, P. J., & Kafka, R. (2016). Solving the crisis of immediacy: How digital technology can transform the customer experience. Business horizons, 59(4), 411-420.
[23] Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of interactive marketing, 51(1), 57-71.
[24] Chaturvedi, R., & Verma, S. (2023). Opportunities and challenges of AI-driven customer service. Artificial Intelligence in customer service: The next frontier for personalized engagement, 33-71.
[25] Rajput, P. K., & Sikka, G. (2021). Multi-agent architecture for fault recovery in self-healing systems. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2849-2866.
[26] Nie, Q., Tang, D., Liu, C., Wang, L., & Song, J. (2023). A multi-agent and cloud-edge orchestration framework of digital twin for distributed production control. Robotics and Computer-Integrated Manufacturing, 82, 102543.
[27] Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijomah, T. I., & Abdul-Azeez, O. Y. (2024). Utilizing machine learning algorithms to enhance predictive analytics in customer behavior studies. International Journal of Scholarly Research in Engineering and Technology, 4(1), 001-018.
[28] Emily, H., & Oliver, B. (2020). Event-driven architectures in modern systems: designing scalable, resilient, and real-time solutions. International Journal of Trend in Scientific Research and Development, 4(6), 1958-1976.
[29] Araujo, H., Mousavi, M. R., & Varshosaz, M. (2023). Testing, validation, and verification of robotic and autonomous systems: a systematic review. ACM Transactions on Software Engineering and Methodology, 32(2), 1-61.
[30] Tsolakis, N., Bechtsis, D., & Srai, J. S. (2019). Intelligent autonomous vehicles in digital supply chains: From conceptualisation, to simulation modelling, to real-world operations. Business Process Management Journal, 25(3), 414-437.
