AI-Powered ECM Automation with Agentic AI for Adaptive, Policy-Driven Content Processing Pipelines
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I3P118Keywords:
Enterprise Content Management, Agentic Ai, Policy-Driven Automation, Adaptive Pipelines, Large Language Models, Intelligent Document Processing, Ai GovernanceAbstract
Enterprise Content Management (ECM) systems have evolved from static repositories of digital documents into mission-critical platforms that govern the lifecycle of enterprise knowledge. However, traditional ECM architectures remain largely rule-based, brittle, and heavily dependent on manual configuration, making them poorly suited for today’s dynamic regulatory environments, exponential data growth, and heterogeneous content formats. This paper presents an AI-powered ECM automation framework that leverages Agentic Artificial Intelligence (Agentic AI) to design adaptive, policy-driven content processing pipelines. Unlike conventional AI-enhanced ECM solutions that focus narrowly on classification or search, the proposed approach introduces autonomous, goal-driven agents capable of perception, reasoning, planning, and action across the entire content lifecycle. These agents collaboratively interpret organizational policies, regulatory constraints, and contextual signals to dynamically orchestrate ingestion, classification, enrichment, governance, retention, and disposition workflows. The proposed architecture integrates large language models (LLMs), knowledge graphs, reinforcement learning, and policy-as-code paradigms to enable self-adaptive ECM pipelines. Agentic AI components continuously monitor content quality, compliance status, and operational performance, enabling real-time optimization and exception handling. A layered methodology is introduced, consisting of content perception agents, policy reasoning agents, orchestration agents, and auditability agents, each aligned with IEEE-recommended principles for trustworthy and explainable AI. Formal models are presented to describe policy constraint satisfaction, agent coordination, and pipeline optimization. To evaluate the effectiveness of the proposed framework, a comparative analysis is conducted against traditional rule-based ECM and non-agentic AI-enhanced ECM systems. Results demonstrate significant improvements in automation coverage, compliance accuracy, processing latency, and adaptability to policy changes. The findings highlight the transformative potential of Agentic AI in redefining ECM as an intelligent, autonomous, and resilient enterprise capability. This work contributes a comprehensive reference architecture, methodological foundation, and future research directions for next-generation ECM systems aligned with Industry 5.0 and AI governance standards
References
[1] Smith, H. A., & McKeen, J. D. (2003). Developments in practice VIII: Enterprise content management. The Communications of the Association for Information Systems, 11(1), 41.
[2] Paivarinta, T., & Munkvold, B. E. (2005, January). Enterprise content management: an integrated perspective on information management. In Proceedings of the 38th annual hawaii international conference on system sciences (pp. 96-96). IEEE.
[3] Rockley, A., & Kostur, P. (2003). Managing enterprise content: A unified content strategy. New Riders.
[4] Katti, A. R., Reisswig, C., Guder, C., Brarda, S., Bickel, S., Höhne, J., & Faddoul, J. B. (2018). Chargrid: Towards understanding 2d documents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 4459-4469).
[5] Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
[6] Sloman, M., & Lupu, E. (2002). Security and management policy specification. IEEE network, 16(2), 10-19.
[7] Humble, J., & Farley, D. (2010). Continuous delivery: reliable software releases through build, test, and deployment automation. Pearson Education.
[8] Wang, D., Fu, Y., Lu, C.-T., Liu, K., Chen, F., Wang, P., & … Chang-Tien Lu (2021, December). Automated urban planning for reimagining city configuration via adversarial learning: Quantification, generation, and evaluation. arXiv preprint. arXiv:2112.14699
[9] Wooldridge, M. (2009). An introduction to multiagent systems. John wiley & sons.
[10] Park, J. S., O'Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October). Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology (pp. 1-22).
[11] Kravari, K., & Bassiliades, N. (2015). A survey of agent platforms. Journal of Artificial Societies and Social Simulation, 18(1), 11. https://doi.org/10.18564/jasss.2661.
[12] Jennings, N. R. (2000). On agent-based software engineering. Artificial intelligence, 117(2), 277-296.
[13] Maican, C., & Lixandroiu, R. (2016). System architecture based on open source enterprise content management systems for supporting educational institutions. International Journal of Information Management, 36(2), 207-214.
[14] Sahu, S. (2022). Intelligent document processing with AWS AI/ML: a comprehensive guide to building IDP pipelines with applications across industries. Packt Publishing Ltd.
[15] Tourani, R., Misra, S., Mick, T., & Panwar, G. (2017). Security, privacy, and access control in information-centric networking: A survey. IEEE communications surveys & tutorials, 20(1), 566-600.
[16] Leitão, P. (2009). Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering applications of artificial intelligence, 22(7), 979-991.
[17] Wang, W., Tan, A. H., & Teow, L. N. (2016). Semantic memory modeling and memory interaction in learning agents. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11), 2882-2895.
[18] Kazemifard, M., Ghasem-Aghaee, N., Koenig, B. L., & Ören, T. I. (2014). An emotion understanding framework for intelligent agents based on episodic and semantic memories. Autonomous agents and multi-agent systems, 28(1), 126-153.
[19] Tian, Y., Schuemie, M. J., & Suchard, M. A. (2018). Evaluating large-scale propensity score performance through real-world and synthetic data experiments. International journal of epidemiology, 47(6), 2005-2014.
[20] Liu, R., Wei, L., & Zhang, P. (2021). A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nature machine intelligence, 3(1), 68-75.
[21] Bhat, J. (2022). The Role of Intelligent Data Engineering in Enterprise Digital Transformation. International Journal of AI, BigData, Computational and Management Studies, 3(4), 106–114. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P111
[22] Sundar, D. (2024). Enterprise Data Mesh Architectures for Scalable and Distributed Analytics. American International Journal of Computer Science and Technology, 6(3), 24–35. https://doi.org/10.63282/3117-5481/AIJCST-V6I3P103
[23] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-Auditing Deep Learning Pipelines for Automated Compliance Validation with Explainability, Traceability, and Regulatory Assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133–142. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P114
[24] Bhat, J., & Jayaram, Y. (2023). Predictive Analytics for Student Retention and Success Using AI/ML. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 121–131. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P114
[25] Sundar, D., Jayaram, Y., & Bhat, J. (2022). A Comprehensive Cloud Data Lakehouse Adoption Strategy for Scalable Enterprise Analytics. International Journal of Emerging Research in Engineering and Technology, 3(4), 92–103. https://doi.org/10.63282/3050-922X.IJERET-V3I4P111
[26] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123–135. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P113
[27] Bhat, J. (2024). Designing Enterprise Data Architecture for AI-First Government and Higher Education Institutions. International Journal of Emerging Research in Engineering and Technology, 5(3), 106–117. https://doi.org/10.63282/3050-922X.IJERET-V5I3P111
[28] Sundar, D. (2023). Serverless Cloud Engineering Methodologies for Scalable and Efficient Data Pipeline Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 182–192. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P118
[29] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2023). A Multi-Layered Zero-Trust Security Framework for Cloud-Native and Distributed Enterprise Systems Using AI-Driven Identity and Access Intelligence. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 144–153. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P115
[30] Sundar, D. (2022). Architectural Advancements for AI/ML-Driven TV Audience Analytics and Intelligent Viewership Characterization. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 124–132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P113
[31] Bhat, J., Sundar, D., & Jayaram, Y. (2024). AI Governance in Public Sector Enterprise Systems: Ensuring Trust, Compliance, and Ethics. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 128–137. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P114
[32] Nangi, P. R., & Settipi, S. (2023). A Cloud-Native Serverless Architecture for Event-Driven, Low-Latency, and AI-Enabled Distributed Systems. International Journal of Emerging Research in Engineering and Technology, 4(4), 128–136. https://doi.org/10.63282/3050-922X.IJERET-V4I4P113
[33] Sundar, D., & Jayaram, Y. (2022). Composable Digital Experience: Unifying ECM, WCM, and DXP through Headless Architecture. International Journal of Emerging Research in Engineering and Technology, 3(1), 127–135. https://doi.org/10.63282/3050-922X.IJERET-V3I1P113
[34] Bhat, J. (2023). Automating Higher Education Administrative Processes with AI-Powered Workflows. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 147–157. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I4P116
[35] Nangi, P. R., & Reddy Nala Obannagari, C. K. (2024). High-Performance Distributed Database Partitioning Using Machine Learning-Driven Workload Forecasting and Query Optimization. American International Journal of Computer Science and Technology, 6(2), 11–21. https://doi.org/10.63282/3117-5481/AIJCST-V6I2P102
[36] Sundar, D. (2024). Streaming Analytics Architectures for Live TV Evaluation and Ad Performance Optimization. American International Journal of Computer Science and Technology, 6(5), 25–36. https://doi.org/10.63282/3117-5481/AIJCST-V6I5P103
[37] Reddy Nangi, P., & Reddy Nala Obannagari, C. K. (2023). Scalable End-to-End Encryption Management Using Quantum-Resistant Cryptographic Protocols for Cloud-Native Microservices Ecosystems. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 142–153. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P116
[38] Bhat, J. (2024). Responsible Machine Learning in Student-Facing Applications: Bias Mitigation & Fairness Frameworks. American International Journal of Computer Science and Technology, 6(1), 38–49. https://doi.org/10.63282/3117-5481/AIJCST-V6I1P104
[39] Sundar, D., & Bhat, J. (2023). AI-Based Fraud Detection Employing Graph Structures and Advanced Anomaly Modeling Techniques. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 103–111. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P112
[40] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127–135. https://doi.org/10.63282/3050-922X.IJERET-V3I3P113
[41] Sundar, D. (2023). Machine Learning Frameworks for Media Consumption Intelligence across OTT and Television Ecosystems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(2), 124–134. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I2P114
[42] Bhat, J., & Sundar, D. (2022). Building a Secure API-Driven Enterprise: A Blueprint for Modern Integrations in Higher Education. International Journal of Emerging Research in Engineering and Technology, 3(2), 123–134. https://doi.org/10.63282/3050-922X.IJERET-V3I2P113
[43] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2024). A Federated Zero-Trust Security Framework for Multi-Cloud Environments Using Predictive Analytics and AI-Driven Access Control Models. International Journal of Emerging Research in Engineering and Technology, 5(2), 95–107. https://doi.org/10.63282/3050-922X.IJERET-V5I2P110
[44] Sundar, D., Jayaram, Y., & Bhat, J. (2024). Generative AI Frameworks for Digital Academic Advising and Intelligent Student Support Systems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 128–138. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P114
[45] Bhat, J. (2023). Strengthening ERP Security with AI-Driven Threat Detection and Zero-Trust Principles. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 154–163. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P116
[46] Nangi, P. R., Reddy Nala Obannagari, C. K., & Settipi, S. (2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104–113. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P111
[47] Sundar, D. (2024). Streaming Analytics Architectures for Live TV Evaluation and Ad Performance Optimization. American International Journal of Computer Science and Technology, 6(5), 25–36. https://doi.org/10.63282/3117-5481/AIJCST-V6I5P103
[48] Reddy Nangi, P., Reddy Nala Obannagari, C. K., & Settipi, S. (2024). Serverless Computing Optimization Strategies Using ML-Based Auto-Scaling and Event-Stream Intelligence for Low-Latency Enterprise Workloads. International Journal of Emerging Trends in Computer Science and Information Technology, 5(3), 131–142. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P113
[49] Nangi, P. R., & Reddy Nala Obannagari, C. K. (2024). A Multi-Layered Zero-Trust–Driven Cybersecurity Framework Integrating Deep Learning and Automated Compliance for Heterogeneous Enterprise Clouds. American International Journal of Computer Science and Technology, 6(4), 14–27. https://doi.org/10.63282/3117-5481/AIJCST-V6I4P102
