Aug 2026· International Journal of Information Technologies and Systems Approach· 0 citations
TL;DR
An artificial intelligence-enhanced middleware pattern that augments existing integration stacks with telemetry, stream processing, and a lightweight learning loop to predict failures, automatically tune policies, and direct traffic in real time is presented.
Abstract
Enterprise systems still struggle to move data reliably across cloud and legacy platforms while keeping costs, latency, and risk in check. The author presents an artificial intelligence-enhanced middleware pattern that augments existing integration stacks with telemetry, stream processing, and a lightweight learning loop to predict failures, automatically tune policies, and direct traffic in real time. The architecture couples an integration core comprising application programming interfaces (APIs), messaging, and event flows with a model-driven policy layer and feedback control. The approach is validated through implementations involving retail order orchestration, logistics tracking, and financial services, demonstrating reductions in mean time to resolution of 35% to 55%, message loss of 0.01%, and cloud egress costs of 8% to 12% under production-like loads. The author outlines governance and observability practices that make the pattern portable across TIBCO Software Inc. integration platforms, Apache Kafka, MuleSoft, and cloud-native services without vendor lock-in. The result provides a pragmatic route to resilient, compliant, and scalable integration that organizations can adopt incrementally at enterprise scale without rewriting critical systems.
Real-time automation of financial activities, including approvals, controls, routing, and monitoring, can enable fast response to opportunities and threats, eliminating the rotation of capital through liquidity provider balances and streamlining interactions with sources of capital. Generative and agentic AI technologies, supported by real-time data streams of events, market feeds, and key risk indicators, can automate and govern these transactions provided that high standards of latency, throughput, security, and regulatory compliance are achieved. A cloud-native DevOps ecosystem equipped with serverless infrastructure-as-code patterns ensures scalable, cost-effective operations with minimum user interference, allowing a comprehensive evaluation of operational performance, change management, security controls, and regulatory oversight. Scenarios involving AI agents as the driving or supervisory part of automation workflows illustrate the architectural paradigm. The analysis identifies the expected challenges in operational responsibility and response reliability, gathering additional evidence from existing Cloud-Native/Serverless solutions of similar scope. Mitigation strategies address the main issues encountered in every-day AI adoption and propose supporting operations management and security controls. The examination of the full end-to-end process flow, including off-line and real-time phases, ensures cover for Governance, Risk, and Compliance (GRC) objectives by design and the definition of a robust plan for evaluation in on-line use.
E. Campbell· American International Journ...· 0 citations
The findings advocate for the integration of AI-powered pipelines within ERP systems as a transformative approach to enable scalable, intelligent, and high-fidelity data processing, essential for next- generation enterprise software resilience and performance.
Yuvaraj Kavala· International Journal of Com...· 0 citations
An Autonomous Data Fabric architecture that combines AI/ML, metadata-driven automation, knowledge graphs, intelligent orchestration, and policy-based governance to enable seamless, self-managing enterprise data ecosystems is proposed.
Narendra Karmarkar· International Journal of Dat...· 0 citations
Legacy enterprise systems continue to support critical business operations, but many of these systems are monolithic, tightly coupled, difficult to scale, and vulnerable to security risks. These limitations reduce their ability to adapt to modern digital environments that require flexibility, reliability, faster deployment, and continuous security. This paper presents a structured framework for cloud-native modernization of legacy enterprise systems by integrating Artificial Intelligence and DevSecOps practices. The proposed framework supports gradual transformation through microservice decomposition, containerization, API-based interoperability, and hybrid integration, allowing organizations to modernize existing systems without major disruption to business processes. Artificial Intelligence is used to support intelligent code analysis, dependency mapping, anomaly detection, workload optimization, and migration planning. These AI-driven capabilities help identify risks, reduce manual effort, and improve decision-making during modernization. DevSecOps practices are integrated into the software development lifecycle through automated CI/CD pipelines, vulnerability scanning, compliance validation, and continuous monitoring. This ensures that security is not treated as a final-stage activity but is continuously applied from development to deployment and post-migration operations. The framework also addresses important migration concerns such as data integrity, system resilience, interoperability, and governance in hybrid cloud environments. Data consistency is maintained through controlled synchronization, validation procedures, and API-led integration between legacy and modernized components. The proposed model improves operational efficiency, deployment agility, security readiness, and system reliability by combining cloud-native architecture with intelligent automation and continuous security enforcement. This study provides a systematic modernization approach that connects legacy enterprise systems with scalable, secure, and future-ready cloud-native architectures.
Shandilya Avadhanam Venkat Krishna Sastry, Pasuluri Bindu Swetha, S. Kishore et al.· International Conference on...· 0 citations
Enterprise adoption of machine learning has fragmented operations into specialised disciplines—DataOps, MLOps, AIOps—creating silos that impede unified governance. We propose XOps, a five-layer reference architecture integrating PlatformOps, DataOps, MLOps and AIOps beneath an Agentic Orchestration layer with Policy-as-Code governance, together with a continuous-time Markov chain model quantifying the availability effect of agent-driven remediation. Two case studies evaluate the architecture under controlled, synthetic conditions. For a self-healing payment gateway, 250 live executions of the reasoning graph against a hosted language model and a live policy engine yield 85.6% plan-level action consistency and 99.6% fault classification accuracy, with 12.4% of plans rejected by the policy gate and escalated to a human and no policy-violating action authorised for execution; the pipeline’s 3.3-minute recovery time is a simulated latency budget rather than a cluster measurement. For a predictive-maintenance application on NASA C-MAPSS data, autonomous drift detection and retraining sustain
$$R^2 = 0.74$$
against 0.29 for an equivalent static model, measured on engines reserved entirely from retraining. An indicative cost analysis suggests an approximately 70% reduction in expected monthly operational cost. Within this scope the results support the feasibility of agent-driven operations rather than establishing production-scale performance.
Mete Köse, E. Küçüksille· Scientific Reports· 0 citations
This paper explores the transformative potential of Artificial Intelligence (AI)-driven cloud solutions in modernizing enterprise architecture, with a focus on integrating DevOps and DataOps methodologies to achieve scalability.
Fatou Diop· International Journal of Art...· 0 citations
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