GreenOps Autopilot

Inspiration

Writing code is only part of shipping software.

After a developer finishes a change, there is still an entire post-code lifecycle: code review, testing, security scanning, remediation, compliance checks, deployment, validation, monitoring, and reporting. In many teams, these steps are still fragmented across tools and require constant human intervention.

That inspired GreenOps Autopilot.

We wanted to explore a simple idea:

What if a developer could define an intent, walk away, and come back to a secure, tested, deployed, and documented result?

We also wanted that automation to be efficient. Autonomous systems can execute more work, but they should also avoid unnecessary pipelines, redundant jobs, wasted compute, and needless deployments.

GreenOps Autopilot combines hands-off DevSecOps automation with carbon-aware delivery.

What it does

GreenOps Autopilot uses the GitLab Duo Agent Platform to automate the post-code software lifecycle.

A developer starts with an issue or code change. From there, the system can coordinate an end-to-end workflow that:

• Reviews the change
• Runs automated tests
• Detects pipeline failures
• Runs security scans
• Analyzes and remediates eligible findings
• Re-validates the code
• Checks compliance requirements
• Builds the application
• Deploys to staging
• Validates the deployed environment
• Promotes a successful release
• Monitors the application
• Produces a final summary of everything that happened

The goal is hands-off autonomy: the developer defines the desired outcome, while GitLab coordinates the workflow from code to production.

GreenOps Autopilot also considers the environmental cost of CI/CD by reducing redundant work, reusing cached artifacts where possible, and making resource efficiency visible as part of the delivery process.

How we built it

We built GreenOps Autopilot around GitLab as the control plane for the entire lifecycle.

The project combines:

• GitLab Duo Agent Platform for agentic automation
• GitLab CI/CD for orchestration
• Merge Requests and Issues as sources of context and intent
• Automated testing and security scanning
• Remediation loops for recoverable failures
• Deployment and post-deployment validation
• Pipeline observability and final reporting
• Google Cloud deployment
• Sustainability-aware pipeline decisions

Instead of treating each DevSecOps stage as an isolated task, we designed the workflow as a connected loop.

For example, when a test or security check fails, the workflow does not simply stop. The system analyzes the failure, determines whether it can safely remediate it, applies the change, and runs the required checks again.

This turns the pipeline from a static sequence of jobs into an autonomous feedback loop.

Intent
  ↓
Review
  ↓
Test
  ↓
Security
  ↓
Remediate ─────┐
  ↓            │
Validate ◄─────┘
  ↓
Deploy
  ↓
Monitor
  ↓
Report

Built With

• ai-agents
• automated-testing
• ci/cd
• cloud
• cloud-deployment
• devsecops
• docker
• fastapi
• gitlab
• gitlab-ci/cd
• gitlab-duo-agent-platform
• google-cloud
• infrastructure-automation
• python
• security-scanning