How I Used AI Agents to Automate My Entire CI/CD Pipeline

1. Developer pushes to main
2. Someone notices (maybe)
3. Manually trigger CI in Jenkins
4. Wait for tests (pray they pass)
5. Manually approve staging deploy
6. Run smoke tests (manually, of course)
7. Ping Slack: "staging looks good?"
8. Wait for someone to say 👍
9. Manually trigger production deploy
10. Monitor dashboards for 20 minutes
11. If something breaks → rollback (manually)
12. Write incident report
13. Question life choices
14. Repeat tomorrow

# test_agent.py
class TestAgent:
    """Analyzes code changes and generates/updates tests automatically."""

    def on_push(self, commit):
        diff = self.get_diff(commit)
        changed_files = self.analyze_changes(diff)

        # AI analyzes what changed and why
        analysis = self.llm.analyze(
            prompt=f"""
            Analyze this code change:
            {diff}

            What could break? What edge cases should be tested?
            Generate targeted test cases.
            """,
            context=self.get_codebase_context()
        )

        # Generate tests for uncovered paths
        new_tests = self.generate_tests(analysis)
        self.run_and_validate(new_tests)

        # Fix any flaky tests it detects
        flaky_tests = self.detect_flaky_tests()
        for test in flaky_tests:
            self.fix_flaky_test(test)

# build_agent.py
class BuildAgent:
    """Optimizes build process based on what actually changed."""

    def on_tests_pass(self, commit):
        changes = self.analyze_changes(commit)

        # Smart Dockerfile optimization
        if changes.has_dependency_changes():
            self.rebuild_base_layer()
        elif changes.only_app_code():
            self.use_cached_layers()  # Saves 8-12 minutes

        # AI optimizes the Dockerfile itself
        optimized_dockerfile = self.llm.optimize(
            prompt=f"""
            Optimize this Dockerfile for the current changes:
            {self.current_dockerfile}

            Changes: {changes.summary}

            Focus on: layer caching, multi-stage builds, image size.
            """,
            constraints=["must pass security scan", "under 500MB"]
        )

        self.build(optimized_dockerfile)

# deploy_agent.py
class DeployAgent:
    """Handles deployment strategy and rollback decisions."""

    def on_build_pass(self, artifact):
        # AI decides deployment strategy
        strategy = self.llm.decide(
            prompt=f"""
            Decide deployment strategy for:
            - Change type: {artifact.change_type}
            - Risk level: {artifact.risk_score}
            - Affected services: {artifact.services}
            - Time of day: {datetime.now()}

            Options: rolling, blue-green, canary, hotfix
            """,
            rules=self.deployment_rules
        )

        # Execute with monitoring
        result = self.deploy(artifact, strategy)

        # Watch metrics for anomalies
        anomalies = self.monitor_deployment(duration="10m")
        if anomalies:
            self.auto_rollback(reason=anomalies.summary)
            self.notify_team(f"🚨 Auto-rolled back: {anomalies.summary}")
        else:
            self.notify_team(f"✅ Deploy successful! {strategy.name}")

# pipeline.yaml
pipeline:
  trigger: on_push

  stages:
    - name: analyze
      agent: orchestrator
      action: "Analyze commit, determine risk, route to appropriate pipeline"

    - name: test
      agent: test_agent
      timeout: 10m
      on_failure: "Generate fix suggestions, retry once"

    - name: build
      agent: build_agent
      timeout: 5m
      depends_on: test

    - name: deploy
      agent: deploy_agent
      timeout: 15m
      depends_on: build
      strategy: "AI-selected based on risk score"

    - name: monitor
      agent: deploy_agent
      duration: 10m
      on_anomaly: auto_rollback

🟡 Low risk  (typo fix, docs)   → Skip tests, fast build, rolling deploy
🟠 Medium risk (feature code)   → Full tests, standard build, rolling deploy
🔴 High risk (DB migration, auth) → Full tests + extra, canary deploy, 30min monitor

# minimal_test_agent.py
import openai
from github import Github

def analyze_and_test(pr_number):
    g = Github(os.environ["GITHUB_TOKEN"])
    repo = g.get_repo("your-org/your-repo")
    pr = repo.get_pull(pr_number)

    # Get the diff
    diff = "\n".join([f.filename for f in pr.get_files()])

    # Ask AI what to test
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{
            "role": "system",
            "content": "You are a senior QA engineer. Analyze code changes and suggest test cases."
        }, {
            "role": "user",
            "content": f"Files changed: {diff}\n\nWhat tests should we add or update?"
        }]
    )

    # Generate test code
    test_suggestions = response.choices[0].message.content
    return test_suggestions

# build_optimizer.py
def optimize_build(changed_files):
    """Decide if we need a full rebuild or can use cache."""

    needs_full_rebuild = any(
        f in changed_files for f in [
            "package.json", "requirements.txt",
            "Dockerfile", "docker-compose.yml"
        ]
    )

    if needs_full_rebuild:
        return "full"
    else:
        return "cached"  # Saves 8-12 minutes!

# .github/workflows/ai-pipeline.yml
name: AI-Powered CI/CD

on:
  push:
    branches: [main]

jobs:
  ai-analyze:
    runs-on: ubuntu-latest
    outputs:
      risk_level: ${{ steps.analyze.outputs.risk }}
    steps:
      - uses: actions/checkout@v4
      - id: analyze
        run: python scripts/ai_analyze.py

  test:
    needs: ai-analyze
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: python scripts/ai_test_agent.py

  build:
    needs: test
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - run: python scripts/ai_build_agent.py

  deploy:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - run: python scripts/ai_deploy_agent.py