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.
Keywords: Continuous Integration (CI); Continuous Delivery (CD); DevOps; DORA Metrics; Lead Time; Deployment Frequency; Change Failure Rate; Mean Time to Recovery (MTTR); Microservices; Progressive Delivery; React 19; Canary Releases; Feature Flags; Large Language Models (LLMs); Autonomous Agents; Machine Learning (ML); Artificial Intelligence (AI); AIOps; Policy-as-Code; Open Policy Agent (OPA); Rego; Cedar; Kubernetes; GitOps; GitHub Actions; GitLab CI/CD; Service Mesh; Envoy; Telemetry; Observability; Security; Auditability; Trust Tiers; Intervention Accuracy; Human Override Rate; Decision Taxonomy; Guardrails; Test Triage; Flakiness Management; Rollback Automation; Feature Flag Tuning; Postmortem Analysis; Remediation; Pull Requests (PRs); Argo Rollouts; Prometheus; Jaeger; Istio; CrewAI; TensorFlow; PyTorch; XGBoost; LLaMA 3; Confidence Thresholds; Safety Compliance; Ethics; Data Security; Role-Based Access Control (RBAC); Multi-Factor Authentication (MFA); Blockchain Ledger; Explainability; Chaos Experiments; Counterfactual Replay; SOC 2; ISO/IEC 27001; GDPR; Model Drift; External Validity; Measurement Bias; Benchmark Scarcity
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