{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:32:06Z","timestamp":1775032326933,"version":"3.50.1"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Modern CI\/CD pipelines face a critical challenge. While AI tools accelerate code generation, static pipelines have become the primary bottleneck to delivery velocity. Flaky tests and pipeline noise create a persistent challenge, with reported failure rates ranging from 11 to 27 percent for test flakiness and 5\u201316 percent for noise-induced build failures. This forces teams to spend more time investigating false failures than building features. As systems scale across regions and dependencies, these problems compound and threaten the fundamental promise of continuous delivery. We introduce a framework that transforms CI\/CD pipelines from deterministic scripts into intelligent, adaptive systems. At its core is the Sense-Analyze-Predict-Act-Learn loop, which we call SAPAL. This loop extends classical adaptive models with CI\/CD specific capabilities including flakiness characterization, dependency risk scoring, multi-region awareness, and developer feedback. We operationalize this loop through a five-layer architecture spanning data collection, reliability intelligence, predictive modeling, adaptive execution, and human-AI collaboration. Three novel metrics quantify pipeline intelligence. Pipeline Health Index measures overall reliability. Test Stability Score identifies flaky patterns. Failure Prediction Confidence validates model accuracy. Three scenarios demonstrate application to real CI\/CD challenges. Intelligent retry strategies, grounded in empirical studies of flaky test detection and resolution, project 60 percent reduction in flaky-induced build failures. ML-based test selection techniques from recent literature suggest 50 to 80 percent reduction in feedback time. Stability-aware deployment orchestration adapts rollout strategies to regional reliability patterns. These projections synthesize findings from published component studies rather than measurements from unified framework deployment. By enabling pipelines to learn from executions, predict with calibrated confidence, and adapt to behavior patterns, this framework provides a practical path toward reliable delivery at scale where intelligence is essential, not optional.<\/jats:p>","DOI":"10.3389\/frai.2026.1776546","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:40:29Z","timestamp":1775022029000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-augmented reliability in CI\/CD: a framework for predictive, adaptive, and self-correcting pipelines"],"prefix":"10.3389","volume":"9","author":[{"given":"Rohit","family":"Dhawan","sequence":"first","affiliation":[{"name":"Independent Researcher","place":["Edmonds, WA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohit","family":"Dhawan","sequence":"additional","affiliation":[{"name":"Independent Researcher","place":["Edmonds, WA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3290605.3300233","article-title":"\u201cGuidelines for human-AI interaction,\u201d","volume-title":"Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems","author":"Amershi","year":"2019"},{"key":"B2","volume-title":"DevOps: A Software Architect's Perspective","author":"Bass","year":"2015"},{"key":"B3","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1145\/3180155.3180164","article-title":"\u201cDeflaker: automatically detecting flaky tests,\u201d","volume-title":"Proceedings of the 40th International Conference on Software Engineering","author":"Bell","year":"2018"},{"key":"B4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3377811.3380369","article-title":"\u201cLearning-to-rank vs ranking-to-learn: strategies for regression testing in continuous integration,\u201d","volume-title":"Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering","author":"Bertolino","year":"2020"},{"key":"B5","volume-title":"Site Reliability Engineering: How Google Runs Production Systems","author":"Beyer","year":"2016"},{"key":"B6","unstructured":"State of ci\/cd Report 2024\n          \n          2024"},{"key":"B7","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: a survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. 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