Using AI Coding Agents to Create CI/CD Workflows with Jenkins, GitLab Runners, and GitHub Actions

Continuous Integration and Continuous Deployment (CI/CD) pipelines are the backbone of modern software development, enabling rapid, reliable delivery of code changes. Creating and maintaining these pipelines, however, can be complex and time-consuming, often requiring intricate scripting and deep knowledge of the CI/CD tooling ecosystem. Enter AI coding agents — advanced artificial intelligence tools designed to assist developers by generating, optimizing, and troubleshooting code.

In this post, we explore how AI coding agents can streamline the creation and maintenance of CI/CD workflows using popular platforms such as Jenkins, GitLab runners, and GitHub Actions. We’ll cover practical applications, best practices, and provide code examples illustrating AI-assisted pipeline generation.

The Role of AI Coding Agents in CI/CD Pipeline Development

AI coding agents leverage large language models (LLMs) trained on vast codebases to generate syntactically correct and contextually relevant code snippets. By integrating these agents into the CI/CD development process, teams can:

• Accelerate pipeline creation: Generate boilerplate pipeline definitions quickly.
• Reduce errors: Minimize syntax and logic mistakes by leveraging AI-generated code.
• Enhance maintainability: Use AI to refactor or optimize existing pipeline scripts.
• Facilitate knowledge transfer: Assist less experienced engineers in adopting CI/CD best practices.

Some popular AI coding agents include GitHub Copilot, OpenAI Codex, and specialized DevOps automation tools powered by AI.

Creating Jenkins Pipelines with AI Assistance

Jenkins Pipeline Basics

Jenkins pipelines are defined using a Groovy-based Domain Specific Language (DSL) in a Jenkinsfile. For example, a simple declarative pipeline might look like:

pipeline {
    agent any
    stages {
        stage('Build') {
            steps {
                sh 'make build'
            }
        }
        stage('Test') {
            steps {
                sh 'make test'
            }
        }
        stage('Deploy') {
            steps {
                sh 'make deploy'
            }
        }
    }
}

AI-Driven Jenkinsfile Generation

Using an AI coding agent, a developer can prompt it with requirements such as:

"Generate a Jenkins declarative pipeline that builds a Docker image, runs unit tests inside the container, and deploys to a Kubernetes cluster."

The AI agent can generate a Jenkinsfile like this:

pipeline {
    agent any
    environment {
        DOCKER_IMAGE = "myapp:${env.BUILD_NUMBER}"
    }
    stages {
        stage('Build Docker Image') {
            steps {
                script {
                    docker.build(DOCKER_IMAGE)
                }
            }
        }
        stage('Unit Test') {
            steps {
                script {
                    docker.image(DOCKER_IMAGE).inside {
                        sh 'pytest tests/'
                    }
                }
            }
        }
        stage('Deploy to Kubernetes') {
            steps {
                sh 'kubectl apply -f k8s/deployment.yaml'
            }
        }
    }
}

This reduces manual scripting effort and helps ensure best practices like containerized testing.

Integrating AI into Jenkins Pipeline Development

• IDE Plugins: Tools like GitHub Copilot can be integrated into IDEs (VS Code, IntelliJ) used for Jenkinsfile editing.
• ChatOps: AI assistants embedded in chat platforms (Slack, Teams) can generate snippets on demand.
• Automated Review: AI can review Jenkinsfiles for common anti-patterns or security issues.

Using AI Agents for GitLab CI/CD Pipelines

GitLab CI Pipeline Syntax

GitLab CI uses a .gitlab-ci.yml file to define jobs and stages. For example:

stages:
  - build
  - test
  - deploy

build_job:
  stage: build
  script:
    - make build

test_job:
  stage: test
  script:
    - make test

deploy_job:
  stage: deploy
  script:
    - make deploy

AI-Powered GitLab CI Generation

A developer can prompt an AI agent:

"Generate a GitLab CI pipeline that builds a Node.js app, caches dependencies, runs Jest tests, and deploys to AWS Elastic Beanstalk."

The AI might output:

stages:
  - build
  - test
  - deploy

cache:
  paths:
    - node_modules/

build:
  stage: build
  image: node:16
  script:
    - npm install
    - npm run build

test:
  stage: test
  image: node:16
  script:
    - npm run test

deploy:
  stage: deploy
  image: python:3.8
  script:
    - pip install awsebcli
    - eb init my-app --region us-east-1
    - eb deploy
  only:
    - main

This example demonstrates AI's ability to incorporate caching, use appropriate Docker images, and handle deployment commands.

Advanced AI Use Cases in GitLab CI

• Optimizing pipeline speed: AI can suggest parallelization strategies, job dependencies, and cache usage.
• Security scanning: AI agents can integrate SAST/DAST tools into pipelines.
• Dynamic pipeline generation: Based on code changes, AI can tailor pipelines to run only relevant tests.

Automating GitHub Actions with AI Assistance

GitHub Actions Workflow Overview

GitHub Actions workflows are YAML files located in .github/workflows/. A simple example:

name: CI

on:
  push:
    branches: [ main ]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Build
        run: make build
      - name: Test
        run: make test

Leveraging AI to Generate GitHub Actions

Prompt example:

"Create a GitHub Actions workflow that checks out code, sets up Python 3.9, installs dependencies, runs tests, and uploads test coverage."

AI-generated workflow:

name: Python CI

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3

      - name: Set up Python 3.9
        uses: actions/setup-python@v4
        with:
          python-version: 3.9

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt

      - name: Run tests
        run: pytest --cov=.

      - name: Upload coverage to Codecov
        uses: codecov/codecov-action@v3

Benefits and Integration Strategies

• Reusable templates: AI can generate modular workflows to reuse across repositories.
• On-demand snippets: Developers can query AI agents directly in GitHub’s web editor.
• Automated workflow updates: AI can suggest upgrades when new GitHub Actions versions are released.

Practical Tips for Using AI Agents in CI/CD Pipeline Development

1. Provide clear prompts: The quality of AI-generated pipelines depends on detailed, specific instructions.
2. Review generated code: Always audit AI output for security, performance, and compliance.
3. Iterate interactively: Use AI in an iterative manner, refining pipeline definitions step-by-step.
4. Combine AI with domain knowledge: AI should augment, not replace, experienced DevOps engineers.
5. Integrate with version control: Store AI-generated pipelines in source control to track changes and enable collaboration.

Conclusion

AI coding agents are transforming how CI/CD pipelines are developed and maintained by automating the generation of complex configuration files and scripts. Whether using Jenkins, GitLab CI, or GitHub Actions, AI tools can accelerate delivery pipelines, reduce human error, and enable teams to focus on higher-value tasks.

By combining the power of AI with DevOps expertise, organizations can build more resilient, efficient, and scalable CI/CD workflows — a critical advantage in today’s fast-paced software landscape.

Actionable Takeaways

• Experiment with AI coding agents like GitHub Copilot or OpenAI Codex to scaffold your next Jenkinsfile, .gitlab-ci.yml, or GitHub Actions workflow.
• Develop detailed prompts describing your CI/CD requirements to improve AI output quality.
• Incorporate AI-generated pipelines into code reviews and testing cycles to ensure quality and security.
• Use AI to optimize pipeline performance by suggesting parallel jobs, caching strategies, and containerization best practices.
• Stay updated on AI advancements and integrate them as part of your DevOps toolchain for continuous improvement.

Harnessing AI for CI/CD pipeline creation is no longer a theoretical concept — it’s a practical, impactful approach to modern software delivery. Start integrating AI coding agents into your DevOps process today to unlock faster, smarter automation.