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arXiv · 2508.11867

AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions

Mohammad Baqar·Saba Naqvi·Rajat Khanda
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Abstract

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.

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Mohammad Baqar, Saba Naqvi, Rajat Khanda. 2025-08-16. AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions. https://doi.org/10.1109/fllm67465.2025.11391007

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