Green Gate

About GreenGate

Inspiration 🌱

Every software deployment has an environmental cost, but most CI/CD pipelines focus only on speed and reliability. We wanted to challenge that idea.

Inspired by the growing environmental impact of cloud computing, we built GreenGate, an AI-powered, carbon-aware CI/CD system that makes sustainability an integral part of software delivery.

Our vision was simple: What if deployments could automatically wait for cleaner energy? What if AI could continuously optimize pipelines to reduce their carbon footprint?

What We Built

GreenGate combines the GitLab Duo Agent Platform with AWS to create an intelligent, self-optimizing CI/CD workflow.

Our system measures the Software Carbon Intensity (SCI) of pipeline executions, identifies inefficient configurations, and uses AI agents to optimize them. Every optimization is verified against a baseline, with a minimum 10% carbon reduction requirement.

Beyond optimization, GreenGate introduces:

• Carbon-aware deployment scheduling
• Automated CI pipeline optimization
• Canary deployments with SLO-based promotion
• Automated rollback on deployment failures
• AI-powered incident investigation and self-healing

We integrated AWS services including Lambda, S3, EventBridge Scheduler, ECR, IAM, and Amplify to bring the system to life.

How We Built It

We used the GitLab Duo Agent Platform to orchestrate specialized AI agents and custom flows:

• GreenGate Optimizer: Analyzes CI pipelines and proposes carbon-reducing optimizations.
• Phoenix Responder: Investigates deployment failures and generates potential fixes.
• GreenGate Advisor: Provides insights into carbon footprints, deployment decisions, and incidents.

AWS Lambda handles serverless execution, EventBridge Scheduler enables scheduled workflows, and S3 stores relevant artifacts.

We also implemented GitLab OIDC for secure, keyless AWS authentication, avoiding long-lived credentials.

Challenges We Faced

One of our biggest challenges was balancing sustainability with reliability. Delaying deployments for cleaner energy is useful, but it cannot come at the cost of critical releases.

We addressed this by introducing a maximum deferral window of four hours, alongside automated canary deployments and SLO-based promotion or rollback.

Another challenge was ensuring that AI-generated optimizations were actually beneficial. Rather than blindly trusting the AI, we implemented a verification process that compares carbon measurements before and after optimization.

Integrating multiple AI agents with GitLab workflows and AWS services also required careful orchestration, permissions management, and error handling.

What We Learned

Building GreenGate taught us that AI agents are most useful when paired with deterministic verification and well-defined boundaries.

We explored carbon-aware computing, the Software Carbon Intensity metric, the GitLab Duo Agent Platform, AWS serverless architecture, and secure CI/CD automation.

Most importantly, we learned that sustainability doesn't have to be an afterthought in software engineering. By making carbon awareness part of the deployment process, we can move toward a future where software is not only faster and more reliable, but also greener.

GreenGate is our step toward making every deployment a little more sustainable. 🌍

Built With

• ai-agents
• amazon-ecr
• amazon-eventbridge
• amazon-web-services
• aws-amplify
• aws-lambda
• carbon-aware-computing
• devops
• docker
• gitlab-ci/cd
• gitlab-duo-agent-platform
• gitops
• llm
• oidc
• python
• serverless
• sustainability
• yaml