I Break Things, AI Fixes Them: Building a Self-Healing CI/CD Pipeline

A broken deployment is every developer's nightmare. Standard CI/CD pipelines fail on minor typos or missing dependencies, forcing engineers to dig through logs and push manual fixes. What if your infrastructure could safely fix itself?

This session explores the transition from rigid automation to AI-driven recovery, introducing the "Pipeline Doctor" in Python: an agentic AI system acting as a self-healing safety net for your repositories.

Allowing AI to write production code sounds terrifying, doesn't it? This is why this talk prioritizes strict execution guardrails. Attendees will learn to set strict tool-calling boundaries around the LLM that securely restrict its capabilities.

Watch me intentionally break a live deployment pipeline to demonstrate how the autonomous agent catches the failure, securely verifies its fix inside an isolated GitHub Actions sandbox, and submits a Pull Request paired with an auto-generated Root Cause Analysis (RCA). Attendees will leave with a practical, open-source blueprint to build self-healing pipelines in their own environments.

1. Understand how self-healing CI/CD pipelines differ from traditional CI/CD workflows.
2. Learn how LangGraph, GitHub Actions, Docker can be combined to build an agentic repair system.
3. Discover practical guardrails for safely integrating LLMs in software developement workflows.
4. Learn how sandbox validation, human approval, and tool restrictions reduce risk.
5. See an end-to-end demonstration of automated failure diagnosis, repair, and pull request generation.