Causal Inference Framework for Root Cause Analysis in Ci/Cd Pipeline Failure Using Agentic Test Automation

DOI

10.2991/978-94-6239-654-8_69How to use a DOI?

Keywords

Causal inference; Root cause analysis; CI/CD pipeline failures; Agentic test automation; Cybersecurity safeguards; Blockchain auditability; Ethical AI governance

Abstract

The process of Continuous Integration and Continuous Deployment (CI/CD) has become even more complex and risky, and the failure analysis steps have become dependent on the use of correlation-based approaches, which tend to correlate symptoms instead of looking into the root cause of the problem. In this paper, the author has come up with the concept of a causal inference framework for root cause analysis of CI/CD pipeline failures, devised through the use of agentic test automation techniques, finding the actual causal chains that exist beyond considering infrastructure changes, code commits, and testing environment configurations. By incorporating the use of self-driven AI agents within the testing process, the proposed framework strips away the involvement of the confounding variables, establishes the causal dependency, and provides concrete remediation recommendations. Apart from enhancing the efficiency of the process within the CI/CD pipeline, these steps have incorporated the necessary cybersecurity measures the value in the form of the detection of malicious/anomalous interventions, along with the use of blockchainbased audit trails, which enhance the transparency, trust, and accountability within the process of causal evidence. Finally, the proposed process has given special emphasis to the aspect of human-computer interaction in the form of explainable causal reasoning, beneficial visualization, and special emphasis for the developers/operators. The future perspective regarding the process has outlined the extension of the causal inference agents within the cross-organization DevSecOps domain, thus creating the basis of ethical, transparent, and benchmarked AI-driven AI governance within the software delivery process, thereby making the process of causal inference the next paradigm shift within the context of next-generation resilient, automated, and secure CI/CD pipeline process.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yashvardhan Rathi
AU  - Savi Grover
PY  - 2026
DA  - 2026/04/24
TI  - Causal Inference Framework for Root Cause Analysis in Ci/Cd Pipeline Failure Using Agentic Test Automation
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 886
EP  - 900
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_69
DO  - 10.2991/978-94-6239-654-8_69
ID  - Rathi2026
ER  -