How do you implement CI/CD pipeline automation with intelligent quality gates?

Intelligent CI/CD quality gates go beyond pass/fail tests to incorporate risk assessment and automated decision-making. Architecture: pipeline stages (build, test, security scan, performance test, deploy) with quality gates between each stage. Gate implementation: aggregate metrics from multiple sources (test coverage, code quality scores, security vulnerability counts, performance regression data), apply configurable thresholds per metric, and compute a composite deployment risk score. ML-enhanced gates: train a classifier on historical deployment outcomes to predict deployment risk based on change characteristics (files modified, test coverage delta, commit frequency, author experience). Implementation: use GitHub Actions, GitLab CI, or Jenkins with custom gate steps that call an evaluation API. The evaluation API scores the build across dimensions: code quality (SonarQube), security (Snyk, Trivy), test coverage (Istanbul, JaCoCo), performance (k6, Gatling baseline comparison), and change risk (ML model). Auto-approve low-risk changes, require manual approval for high-risk, and block critical-risk deployments. Progressive deployment: quality gates also control canary promotion (promote if error rate stays below baseline + 1 standard deviation for 15 minutes).