AI-DRIVEN ROOT CAUSE ANALYSIS FRAMEWORK FOR DISTRIBUTED MICROSERVICES ARCHITECTURES
Keywords:
Root Cause Analysis (RCA), RCASage, AI-Driven, AI-Augmented, Causal Inference, Distributed Systems, Graph Neural Networks, Graph-based Deep Learning, Anomaly Detection, Software Defect Prediction, Continuous Integration/Continuous Deployment (CI/CD), Microservices Architecture, Explainable AI (XAI), CI/CD Proactive MonitoringAbstract
Multi-layered IT systems are characterized by persistent system downtime, which creates significant operational burdens due to high costs and prolonged troubleshooting processes. Traditional root cause analysis (RCA) techniques are largely ineffective because they are predominantly reactive and manual, making them unsuitable for the scale and complex interdependencies of modern microservices architectures. This study addresses the critical “observability–complexity gap,” a condition in which contemporary monitoring tools generate vast amounts of correlated data but fail to provide true causal resolution. To address this challenge, the study introduces RCASage, a novel AI-augmented inference engine designed for autonomous fault identification. The core innovation of RCASage lies in its hybrid architecture, which comprises a three-stage pipeline: (1) multi-modal telemetry data ingestion and dynamic dependency graph construction; (2) unsupervised anomaly detection using LSTM autoencoders combined with Natural Language Processing (NLP) classifiers; and (3) an Autonomous Inference Engine (AIE). This engine uniquely integrates Graph Neural Networks (GNNs) with the Neural Granger Causal Discovery algorithm to distinguish true root causes from downstream symptomatic effects. By shifting RCA from symptom correlation to causal inference, RCASage surpasses existing approaches such as beta-binomial inference and event-graph-based systems (e.g., GROOT). Furthermore, it bridges the gap between development and operations by incorporating Just-in-Time (JIT) defect prediction into the CI/CD pipeline. Empirical evaluation demonstrates that the proposed AI-augmented framework reduces Mean Time to Resolution (MTTR) by over 90% compared to traditional manual approaches. In addition, RCASage advances the RCA paradigm by embedding Explainable AI (XAI) principles, providing transparent causal explanations alongside visual dependency graphs. This positions RCASage as an intelligent, evidence-based, and autonomous solution for root cause analysis in contemporary digital infrastructures.
References
A. Das, "5 Common Challenges in Large-Scale Microservices," Medium, 12 March 2025. [Online]. Available: https://article.arunangshudas.com/5-common-challenges-in-large-scale-microservices-c199332ad571. [Accessed 24 November 2025].
M. Söylemez, B. Tekinerdogan and A. Kolukısa, "Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review," Applied Sciences, vol. 12, no. 11, p. 5507, 29 May 2022.
A. Sappa, "Adaptive Machine Learning Models for Effort Estimation and Risk Detection in Distributed Software Project Pipelines," Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), vol. 16, no. 2, pp. 54-74, June 2025.
Cognizant 20-20 Insights, "Pitfalls & Challenges Faced During a Microservices Architecture Implementation," Cognizant Digital Systems & Technology, 2020.
Z. Liu, G. Fan, H. Yu and L. Chen, "An Approach to Modeling and Analyzing Reliability for Microservice-Oriented Cloud Applications," Wireless Communications and Mobile Computing, vol. 2021, no. 1, pp. 1-17, August 2021.
H. Wang, P. Nguyen, J. Li, S. Kopru, G. Zhang, S. Katariya and S. Ben-Romdhane, "GRANO: interactive graph-based root cause analysis for cloud-native distributed data platform," Proceedings of the VLDB Endowment, vol. 12, no. 12, pp. 1942-1945, 1 August 2019.
H. Wang, Z. Wu, H. Jiang, Y. Huang, J. Wang, S. Kopru and T. Xie, "Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings," in 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia, 2021.
L. Pham, H. Ha and H. Zhang, "Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?," in ASE '24: 39th IEEE/ACM International Conference on Automated Software Engineering, Sacramento, CA, USA, 2024.
A. D. Pazho, G. A. Noghre, A. A. Purkayastha, J. Vempati, O. Martin and H. Tabkhi, "A Survey of Graph-Based Deep Learning for Anomaly Detection in Distributed Systems," IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 1, pp. 1-20, 2024.
A. Chaudhry, "AI-Driven Root Cause Analysis: Leveraging Causal Inference in SRE," Medium, 7 January 2025. [Online]. Available: https://amitchaudhry.medium.com/ai-driven-root-cause-analysis-leveraging-causal-inference-in-sre-228c236e4313.
S. Jha, A. Rahane, L. Shwartz, M. Palaci-Olgun, F. Bagehorn, J. Rios, D. Stingaciu, R. Kattinakere and D. Banerjee, "Causal AI-based Root Cause Identification: Research to Practice at Scale," p. 2025.
O. Israel, "AI-Augmented Root Cause Analysis for System Failures in Cloud and Edge Data Centers," September 2025.
B. T. Tutuncuoglu, "AI-Augmented Root Cause Analysis: Autonomous Inference Engine for Multi-Layered System Outages," IEEE, pp. 1-18, 2025.
T. K. Adenekan, "Explainable AI Techniques for Root Cause Analysis in Complex Systems," pp. 1-14, 2024.
Á. Brandón, M. Solé, A. Huélamo, D. Solans, M. S. Pérez and V. Muntés-Mulero, "Graph-based root cause analysis for service-oriented and microservice architectures," Journal of Systems and Software, vol. 159, p. 110432, 2020.
A. Ikram, S. Chakraborty, S. Mitra, S. Saini, S. Bagchi and M. Kocaoglu, "Root Cause Analysis of Failures in Microservices through Causal Discovery," Advances in Neural Information Processing Systems, vol. 35, pp. 31158-31170, 2022.
C.-M. Lin, C. Chang, W.-Y. Wang, K.-D. Wang and W.-C. Peng, "Root Cause Analysis in Microservice Using Neural Granger Causal Discovery," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 1, pp. 206-213, 25 March 2024.
Y. Wang, Z. Zhu, Q. Fu, Y. Ma and P. He, "MRCA: Metric-level Root Cause Analysis for Microservices via Multi-Modal Data," in ASE '24: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, 2024.
I. Erakovic and C. Pahl, "Root Cause Analysis for Microservices based on Architecture Profiling and Anomaly Detection," in Cloud Computing and Services Science, Porto, Portugal, Springer-Verlag, 2025, pp. 1-29.
M. Baqar, S. Naqvi and R. Khanda, "AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions," pp. 1-13, 16 August 2025.
F. Williams, A. A. Adeniran, E. Edmund and B. Idowu, "Automated Anomaly Detection in CI/CD Pipelines Using Machine Learning Approaches," ResearchGate, pp. 1-15, 1 September 2025.
R. C. Thota, "CI/CD Pipeline Optimization: Enhancing Deployment Speed and Reliability with AI and Github Actions," International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences (IJIRMPS), vol. 8, no. 2, pp. 1-11, March 2020.
R. Farihane, I. Chlioui and M. Radgui, "CI/CD Pipeline Optimization Using AI: A Systematic Mapping Study," Engineering Proceedings, vol. 112, no. 1, 9-11 July 2025.
E. N. Akimova, A. Y. Bersenev, A. A. Deikov, K. S. Kobylkin, A. V. Konygin, I. P. Mezentsev and V. E. Misilov, "A Survey on Software Defect Prediction Using Deep Learning," Mathematics, vol. 8, no. 1180, pp. 1-14, 2021.
Q. Yu, S. Jiang, J. Qian, L. Bo, L. Jiang and G. Zhang, "Process metrics for software defect prediction in object‐oriented programs," IET Software, vol. 14, no. 3, pp. 283-292, 1 June 2020.
Durga and D. A. Sinha, "Enhancing Software Reliability through Intelligent Fault Prediction Using Machine Learning," International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), vol. 11, no. 3, pp. 945-956, 11 June 2025.
A. B. Tavakkoli, "Framework for Bug Inducing Commit Prediction Using Quality Metrics," Electronic Thesis and Dissertation Repository, Ontario, Canada, 2024.
J. Li, Y. Zhang, T. Wang and Y. Wu, "GHA-BFP: Framework for Automated Build Failure Prediction in GitHub Actions," in 2024 31st Asia-Pacific Software Engineering Conference (APSEC), Chongqing, China, 2024.
M. J. Haruna and T. C. Darius, "Software Defect Prediction Using Machine Learning and Deep Learning Techniques," Kasu Journal of Computer Science, vol. 1, no. 3, pp. 527-543, September 2024.
C. Pornprasit and C. K. Tantithamthavorn, "JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction," in 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR), Madrid, Spain, 2021.
Y. Jiang, B. Shen and X. Gu, "Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning," The Journal of Systems & Software, vol. 219, no. C, pp. 1-14, 1 January 2025.
A. Zaim, J. Ahmad, N. H. Zakaria, G. E. Su and H. Amnur, "Software Defect Prediction Framework Using Hybrid Software Metric," International Journal on Informatics Visualization, vol. 6, no. 4, pp. 921-930, December 2022.
L. Toka, G. Dobreff, D. Haja and M. Szalay, "Predicting cloud-native application failures based on monitoring data of cloud infrastructure," in 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), Bordeaux, France, 2021.
A. Houerbi, C. Siala, A. Tucker, D. E. Rzig and F. Hassan, "Empirical Analysis on CI/CD Pipeline Evolution in Machine Learning Projects," in Association for Computing Machinery (ACM) Conference (Conference’17), New York, NY, USA, 2025.
R. S. Constantinescu, "Exploring Descriptive Metrics of Build Performance: A Study of GitHub Actions in Continuous Integration Projects," 2023.
W. H. Seow, C. Y. Lim and S. L. Ang, "Random Forest Model for Software Build Time Prediction on CI/CD Pipeline," Pertanika Journal of Science & Technology, vol. 33, no. 2, pp. 1031-1048, 21 February 2025.
T. Rausch, W. Hummer, P. Leitnery and S. Schulte, "An Empirical Analysis of Build Failures in the Continuous Integration Workflows of Java-Based Open-Source Software," in 14th International Conference on Mining Software Repositories (MSR 2017), Buenos Aires, Argentina, 2017.