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

• URL: http://arxiv.org/abs/2508.11867v1
• Date: Sat, 16 Aug 2025 01:51:59 GMT
• Title: AI-Augmented CI/CD Pipelines: From Code Commit to Production with Autonomous Decisions
• Authors: Mohammad Baqar, Saba Naqvi, Rajat Khanda,
• Abstract summary: We propose AI-augmented CI/CD Pipelines, where large language models and autonomous agents act as policy-bounded co-pilots.<n>We discuss ethics, verification, auditability, and threats to validity, and chart a roadmap for verifiable autonomy in production delivery systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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