AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908845109936128 |
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| author | Baqar, Mohammad Naqvi, Saba Khanda, Rajat |
| author_facet | Baqar, Mohammad Naqvi, Saba Khanda, Rajat |
| contents | Modern software delivery has accelerated from quarterly releases to multiple deployments per day. While CI/CD tooling has matured, human decision points interpreting flaky tests, choosing rollback strategies, tuning feature flags, and deciding when to promote a canary remain major sources of latency and operational toil. We propose AI-Augmented CI/CD Pipelines, where large language models (LLMs) and autonomous agents act as policy-bounded co-pilots and progressively as decision makers. We contribute: (1) a reference architecture for embedding agentic decision points into CI/CD, (2) a decision taxonomy and policy-as-code guardrail pattern, (3) a trust-tier framework for staged autonomy, (4) an evaluation methodology using DevOps Research and Assessment ( DORA) metrics and AI-specific indicators, and (5) a detailed industrial-style case study migrating a React 19 microservice to an AI-augmented pipeline. We discuss ethics, verification, auditability, and threats to validity, and chart a roadmap for verifiable autonomy in production delivery systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions Baqar, Mohammad Naqvi, Saba Khanda, Rajat Software Engineering Artificial Intelligence Modern software delivery has accelerated from quarterly releases to multiple deployments per day. While CI/CD tooling has matured, human decision points interpreting flaky tests, choosing rollback strategies, tuning feature flags, and deciding when to promote a canary remain major sources of latency and operational toil. We propose AI-Augmented CI/CD Pipelines, where large language models (LLMs) and autonomous agents act as policy-bounded co-pilots and progressively as decision makers. We contribute: (1) a reference architecture for embedding agentic decision points into CI/CD, (2) a decision taxonomy and policy-as-code guardrail pattern, (3) a trust-tier framework for staged autonomy, (4) an evaluation methodology using DevOps Research and Assessment ( DORA) metrics and AI-specific indicators, and (5) a detailed industrial-style case study migrating a React 19 microservice to an AI-augmented pipeline. We discuss ethics, verification, auditability, and threats to validity, and chart a roadmap for verifiable autonomy in production delivery systems. |
| title | AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2508.11867 |