Enhancing Software Delivery Performance through AI-Driven Observability and Intelligent Automation
DOI:
https://doi.org/10.64137/31079458/IJCSEI-V2I1P107Keywords:
AI-Driven Observability, Software Delivery Performance, Devops, AIops, Intelligent Automation, Continuous Delivery, Dora Metrics, Cloud-Native SystemsAbstract
Modern software delivery operates under simultaneous pressure for speed, stability, security, and cost efficiency. Although DevOps and continuous delivery practices have improved release velocity, many organizations still struggle to translate raw operational telemetry into timely decisions that reduce change failure, shorten recovery windows, and improve delivery throughput. This paper develops a Q1-style conceptual research framework that positions AI-driven observability as the missing decision layer between software delivery instrumentation and intelligent automation. Using a structured synthesis of forty references spanning software delivery performance, AIOps, defect prediction secure microservices, cloud-native monitoring, and cross-domain AI automation, the paper derives a reference architecture that integrates telemetry collection, dependency-aware context modeling, anomaly and risk prediction, policy-aware orchestration, and human-centered decision support. The framework links observability signals such as logs, metrics, traces, pipeline events, configuration changes, and service dependencies to delivery outcomes measured through throughput and stability metrics. In contrast to narrow monitoring or isolated machine learning approaches, the proposed model treats software delivery as a closed feedback system in which prediction, explanation, and automated action must remain aligned with governance, security, and service objectives. The paper contributes three outcomes: a reference architecture for AI-driven observability and intelligent automation, a mechanism-level explanation of how that architecture can improve delivery performance, and an evaluation model grounded in software delivery metrics, automation quality measures, and socio-technical controls. The result is a practical research agenda for building delivery systems that are not only observable, but also increasingly adaptive, trustworthy, and operationally efficient.
References
[1] DORA, "Accelerate State of DevOps Report 2021," Google Cloud, 2021. [Online]. Available: https://dora.dev/research/2021/dora-report/
[2] V. K. R. Mittamidi, “An Automated AI-Driven Monitoring and Observability Framework for Cloud-Based Data Pipelines by Software Defect Prediction Research,” International Journal of Multidisciplinary Evolutionary Research, vol. 5, no. 1, pp. 109–112, 2024, doi: https://doi.org/10.54660/ijmer.2024.5.1.109-112.
[3] S. R. Gudi, “Enhancing Reliability in Java Enterprise Systems through Comparative Analysis of Automated Testing Frameworks,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, 2023, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v4i2p115.
[4] A. K. K. V. Alluri, "End-to-end observability for customer AI: Tracing data, features, and predictions across systems," Global Multidisciplinary Perspectives Journal, vol. 1, no. 5, pp. 67-70, 2024. doi: 10.54660/GMPJ.2024.1.5.67-70.
[5] M. De, T. P. Sales, M. R. Machado, M. De, T. P. Sales, and M. R. Machado, “AIOps for log anomaly detection in the era of LLMs: A systematic literature review,” Intelligent Systems with Applications, vol. 28, pp. 200608–200608, Nov. 2025, doi: https://doi.org/10.1016/j.iswa.2025.200608.
[6] S. D. Sivva, R. R. Thalakanti, S. S. G. Bandari, and S. D. R. Yettapu, “AI-Driven Decision Intelligence for Agile Software Lifecycle Governance: An Architecture-Centered Framework Integrating Machine Learning Defect Prediction and Automated Testing,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, pp. 167–172, 2023, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v4i4p118.
[7] S. K. Gunda, “Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison,” 2024 Asian Conference on Intelligent Technologies (ACOIT), pp. 1–5, Sep. 2024, doi: https://doi.org/10.1109/acoit62457.2024.10939610.
[8] D. Port, B. Taber, and Parisa Emkani, “Investigating Effectiveness and Compliance to DevOps Policies and Practices for Managing Productivity and Quality Variability,” Journal of systems and software/The Journal of systems and software, pp. 112030–112030, Mar. 2024, doi: https://doi.org/10.1016/j.jss.2024.112030
[9] N. Mutyam, “Graph-Based Modeling of Service Dependencies for Predicting Failure Propagation in Distributed Systems,” International Journal of Multidisciplinary Evolutionary Research, vol. 5, no. 1, pp. 113–116, 2024, doi: https://doi.org/10.54660/ijmer.2024.5.1.113-116.
[10] S.R. Gudi, “Design and Evaluation of Secure Microservices Architecture for HIPAA-Compliant Prescription Processing on AWS and OpenShift,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 5, no. 2, Jun. 2024, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v5i2p116.
[11] S. Saleh, N. Madhavji, and J. Steinbacher, “A Systematic Literature Review on Continuous Integration and Deployment (CI/CD) for Secure Cloud Computing,” Proceedings of the 20th International Conference on Web Information Systems and Technologies, pp. 331–341, 2024, doi: https://doi.org/10.5220/0013018500003825.
[12] V. K. R. Mittamidi, “Leveraging AI and ML for Predictive Monitoring and Error Mitigation in Change Data Capture Pipelines,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 6, pp. 104–111, 2025, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v6i3p116.
[13] M. Balerao, “A Converged Artificial Intelligence Architecture for Innovation, Software Lifecycle Optimization, and Cybersecurity Risk Mitigation,” International Journal of Multidisciplinary Futuristic Development, vol. 4, no. 1, pp. 117–120, 2023, doi: https://doi.org/10.54660/ijmfd.2023.4.1.117-120.
[14] R. R. Thalakanti, “Optimizing Neural Network Architecture for Binary Classification Using Evolutionary Algorithms,” 2025 International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT), pp. 1–6, Sep. 2025, doi: https://doi.org/10.1109/icec2nt65402.2025.11380048.
[15] R. Amaro, R. Pereira, and M. M. da Silva, “Mapping DevOps capabilities to the software life cycle: A systematic literature review,” Information and Software Technology, vol. 177, p. 107583, Jan. 2025, doi: https://doi.org/10.1016/j.infsof.2024.107583.
[16] Sai Krishna Gunda, “An exploration of adaptive ensemble approaches in software fault detection: Balancing accuracy and robustness,” THE FIRST INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ARTIFICIAL INTELLIGENCE, CYBER SECURITY, AND EMBEDDED SYSTEMS: ICRTACES2024, vol. 3345, no. 1, 7 January 2026, Doi: https://doi.org/10.1063/5.0298093
[17] S. D. R. Yettapu, “A Unified Artificial Intelligence Governance and Reliability Engineering Framework for Secure and Autonomous Software-Intensive and Cyber-Physical Systems,” Journal of Frontiers in Multidisciplinary Research, vol. 4, no. 1, pp. 605–608, 2023, doi: https://doi.org/10.54660/.jfmr.2023.4.1.605-608.
[18] S. K. Gunda, “A Risk-Aware AI Framework for Automated Testing and Quality Assurance in Core Banking Systems,” International Journal of Multidisciplinary Evolutionary Research, vol. 5, no. 1, pp. 117–120, 2024, doi: https://doi.org/10.54660/ijmer.2024.5.1.117-120.
[19] S. R. Gudi, “Ensuring Secure and Compliant Fax Communication: Anomaly Detection and Encryption Strategies for Data in Transit,” 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 786–791, Sep. 2025, doi: https://doi.org/10.1109/icimia67127.2025.11200537.
[20] DORA, "Accelerate State of DevOps Report 2023," Google Cloud, 2023. [Online]. Available: https://dora.dev/research/2023/dora-report/
[21] S. K. Gunda, “Automatic Software Vulnerabilty Detection Using Code Metrics and Feature Extraction,” 2025 2nd International Conference On Multidisciplinary Research and Innovations in Engineering (MRIE), pp. 115–120, Jul. 2025, doi: https://doi.org/10.1109/mrie66930.2025.11156601.
[22] GV Krishna, BD Reddy, and T. Vrindaa, “EmoVision: An Intelligent Deep Learning Framework for Emotion Understanding and Mental Wellness Assistance in Human Computer Interaction,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 6, 2025, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v6i4p103.
[23] A.K.K. Varma Alluri, “Using Salesforce CRM and Deep Learning (CNN) Techniques to Improve Patient Journey Mapping and Engagement in Small and Medium Healthcare Organizations,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 6, 2025, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v6i4p115.
[24] R. R. Thalakanti, “Enhancing Convergence in Fully Connected Neural Networks via Optimized Backpropagation,” 2025 2nd International Conference on Computing and Data Science (ICCDS), pp. 1–6, Jul. 2025, doi: https://doi.org/10.1109/iccds64403.2025.11209625.
[25] P. Chowdhury, “A Cloud-Native Decision Intelligence Architecture for Sustainable CPG Supply Chain Networks,” Journal of Engineering Research and Sciences, vol. 5, no. 1, p. 35, Jan. 2026, doi: https://doi.org/10.55708/js0501004.
[26] Shrutika Prakash Mokashi, Prahlad Chowdhury, and Guru Lakshmi Priyanka Bodagala, “Smart Manufacturing and the Operator’s Digital Double: Modeling Cognitive Load Through a Psychosocial Digital Twin,” International Journal of Sustainability and Innovation in Engineering, vol. 4, no. 1, Mar. 2026, doi: https://doi.org/10.56830/ijsie202602.
[27] S. K. Gunda, “Comparative Analysis of Machine Learning Models for Software Defect Prediction,” pp. 1–6, Oct. 2024, doi: https://doi.org/10.1109/icpects62210.2024.10780167
[28] S. R. Gudi, “Leveraging Predictive Analytics and Redis-Backed Caching to Optimize Specialty Medication Fulfillment and Pharmacy Inventory Management,” International Journal of AI, BigData, Computational and Management Studies, vol. 5, no. 3, Oct. 2024, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v5i3p116.
[29] S. D. Sivva, “An End-to-End AI-Based Systems Engineering Paradigm for Lifecycle Governance, Predictive Quality Assurance, Automation Economics, and Cybersecurity Intelligence,” Journal of Frontiers in Multidisciplinary Research, vol. 4, no. 1, pp. 600–604, 2023, doi: https://doi.org/10.54660/.jfmr.2023.4.1.600-604.
[30] Albuquerque and F. Correia, “Logging design patterns for cloud-native applications,” pp. 1–11, Jul. 2024, doi: https://doi.org/10.1145/3698322.3698351.
[31] A.K.K. Varma Alluri, “Salesforce CRM Framework for Real Time DeFi Portfolio Intelligence and Customer Engagement Forecasting in Web3 Based Decentralized Finance Ecosystems Using ML Techniques,” International Journal of AI, BigData, Computational and Management Studies, vol. 6, 2025, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v6i4p111.
[32] R. R. Thalakanti, “Convergence Analysis and Implementation of Linear Multistep Methods for Solving Ordinary Differential Equations,” 2025 2nd Asian Conference on Intelligent Technologies (ACOIT), pp. 1–18, Oct. 2025, doi: https://doi.org/10.1109/acoit66109.2025.11436783.
[33] Prahlad Chowdhury, “BLOCKCHAIN FOR MANUFACTURING TRACEABILITY: SECURING MANUFACTURING DATA IN MULTI-TIER SUPPLY CHAINS,” International Journal of Applied Mathematics, vol. 38, no. 11s, pp. 336–357, Nov. 2025, doi: https://doi.org/10.12732/ijam.v38i11s.1169
[34] Sai Krishna Gunda, “Advancing software fault detection: A comparative study of neural network architectures,” THE FIRST INTERNATIONAL CONFERENCE ON RECENT TRENDS IN ARTIFICIAL INTELLIGENCE, CYBER SECURITY, AND EMBEDDED SYSTEMS: ICRTACES2024, vol. 3345, no. 1, 7 January 2026, doi: https://doi.org/10.1063/5.0298095
[35] P. Chowdhury, “Global MES Rollout Strategies: Overcoming Localization Challenges in Multi-Country Deployments,” The American Journal of Applied Sciences, vol. 7, no. 07, pp. 30–28, Jul. 2025, doi: https://doi.org/10.37547/tajas/volume07issue07-04
[36] S. R. Gudi, “AI-Driven Fax-to-Digital Prescription Automation: A Cloud-Native Framework Using OCR, Machine Learning, and Microservices for Pharmacy Operations,” International Journal of Emerging Research in Engineering and Technology, vol. 5, no. 1, Mar. 2024, doi: https://doi.org/10.63282/3050-922x.ijeret-v5i1p113
[37] P. Chowdhury, “Sustainable Manufacturing 4.0: Tracking Carbon Footprint In SAP Digital Manufacturing With IOT Sensor Networks,” Frontiers in Emerging Computer Science and Information Technology, vol. 2, no. 9, pp. 12–19, Sep. 2025, doi: https://doi.org/10.37547/fecsit/volume02issue09-02
[38] S. R. Gudi, “Enhancing Optical Character Recognition (OCR) Accuracy in Healthcare Prescription Processing using Artificial Neural Networks,” European Journal of Artificial Intelligence and Machine Learning, vol. 4, no. 6, pp. 1–6, Nov. 2025, doi: https://doi.org/10.24018/ejai.2025.4.6.79
[39] P. Chowdhury, “Human-Robot Collaboration (HRC) in Automotive: SAP DM Orchestration of Cobot Work-Cells,” American Journal of Technology, vol. 4, no. 4, pp. 87–100, Dec. 2025, doi: https://doi.org/10.58425/ajt.v4i4.466
[40] S. R. Gudi, “Monitoring and Deployment Optimization in Cloud-Native Systems: A Comparative Study Using OpenShift and Helm,” 2025 4th International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 792–797, Sep. 2025, doi: https://doi.org/10.1109/icimia67127.2025.11200594.
