Autopilot-CI

Inspiration

Every engineer knows the feeling. It is 2 AM. Your phone buzzes. The CI pipeline failed. You drag yourself out of bed, open your laptop, dig through cryptic logs, find the bug, write a fix, push it, wait for the pipeline to run again, and finally go back to sleep an hour later. This happens to engineering teams every single day across the world. We asked one simple question — what if the pipeline could just fix itself? That question became AutoPilot CI.

Challenges we ran into The biggest challenge was getting the Gradient AI Agent Development Kit to work correctly with our multi-agent architecture. The ADK requires a specific entrypoint format with the correct function signature, and getting this right took significant debugging. Encoding issues with special characters in Python files caused repeated S3 upload failures during deployment. We had to carefully strip all non-ASCII characters from every file before deployment would succeed. Environment variable management for the deployed agent was tricky since the ADK does not have a straightforward way to set env vars through the CLI. We had to bundle the configuration carefully. Getting the four agents to communicate state correctly through the pipeline required careful design of the shared state object and handling edge cases where the LLM returned unexpected formats.

What it does

AutoPilot CI is a fully autonomous self-healing CI/CD agent built on DigitalOcean Gradient AI. When a developer pushes code to GitHub, a webhook triggers four specialized AI agents that handle the entire pipeline without any human involvement. Agent one, the Build Analyst, reads the commit diff and CI build log and diagnoses exactly what failed and why using the Llama model via DigitalOcean Gradient AI Serverless Inference. Agent two, the Code Fixer, generates the minimal code patch needed to resolve the issue and commits it directly back to the GitHub repository automatically. Agent three, the Deploy Decider, analyzes the risk of the change and autonomously selects the safest deployment strategy — blue-green for security changes, canary for database migrations, direct for small bug fixes, and rolling for new features. Agent four, the Health Monitor, pings the application after deployment to verify it is live and healthy. If the health check fails, the agent automatically triggers a rollback to the last stable version. The result — the developer wakes up to a fixed, deployed, healthy application. They never had to do anything.

How we built it

We built AutoPilot CI using the DigitalOcean Gradient AI stack from end to end. The agent pipeline is orchestrated using the DigitalOcean Gradient AI Agent Development Kit with LangGraph powering the multi-agent workflow. Each agent is a specialized Python function that calls the Llama 3.1 8B Instruct model through DigitalOcean Gradient AI Serverless Inference API. The GitHub integration uses PyGithub to read real commit diffs and push fixes back to repositories. The deployment integration calls the DigitalOcean App Platform API directly to trigger real deployments. A FastAPI webhook server receives GitHub push events and routes them to the agent pipeline. A live dashboard built in HTML and JavaScript shows every agent reasoning step in real time. The entire system is deployed on DigitalOcean Gradient AI Agent Development Kit as a managed serverless agent accessible via a public API endpoint.

Challenges we ran into

Accomplishments that we're proud of

We are proud of building a genuinely working end-to-end autonomous pipeline in under 10 hours. The agent actually reads real GitHub commits, generates real code fixes, makes real commits back to GitHub, and calls the real DigitalOcean App Platform API to trigger real deployments. The multi-agent architecture using DigitalOcean Gradient AI ADK works reliably and the live dashboard showing agent reasoning steps makes the system fully transparent and debuggable. Most importantly — the core vision works. A developer can push buggy code, walk away, and come back to find the pipeline fixed itself completely autonomously.

What we learned

What's next for Autopilot-CI

The immediate next step is integrating the DigitalOcean Gradient AI Knowledge Base with full RAG support. Every fix the agent makes will be stored in the knowledge base so the agent learns from past failures and gets smarter over time. The second time it sees the same error, it already knows the fix. We also plan to add support for running actual test suites inside the agent pipeline, integration with Slack and email for human-readable summaries, support for multiple programming languages and frameworks beyond Python, and a web interface where teams can manage multiple repositories connected to AutoPilot CI. The long term vision is a fully autonomous DevOps platform where engineering teams never have to touch CI/CD again.

Built With

• ai
• api
• digitalocean
• fastapi
• github
• gradient
• inference
• langgraph
• llama
• python