You’ve been asked to adopt AI tools with a promise they will accelerate your time to production and improve the quality of your code. Along the way, you’ve noticed a huge increase in the number of changes submitted to code and you’ve also likely felt the pain of your systems and processes not being able to keep up. The inner loop of development is moving at lightning speed, while the outer loop struggles to keep up. In many CI/CD workflows, a pipeline failure, which is often caused by a security vulnerability or a configuration error, can leave a developer with the manual, time-consuming task of figuring out what went wrong. They spend too much time investigating, debugging, researching fixes, and resubmitting code. This creates friction between developers, security, and operations teams as they place blame for the issue and identify who is responsible for fixing it. At OpenShift Commons Gathering at this year's Red Hat Summit, we took a closer look at the tools in Red Hat OpenShift and explored how to troubleshoot and fix a failed pipeline through an agentic workflow. Our developer advocate, Natale Vinto, demoed how Red Hat OpenShift helps secure and automate the development pipeline by:Defining and enforcing security gates. Accelerating failure detection and remediation using an AI agent. Fixing and validating new code with human-in-the-loop oversight before rerunning the pipeline to ensure the fix is effective and all security criteria are met.To start, Natale leads us to Red Hat OpenShift DevSpaces where he’s submitting an update to a new java application and kicks off a new DevSecOps pipeline. Red Hat OpenShift has defined requirements already in place to proactively enforce security. As code is pushed, the system automatically checks against these defined security policies. The security and ops teams have built robust policy-as-code through Red Hat Advanced Cluster Security for Kubernetes, meaning security definitions are consistent, version-controlled, and deeply integrated with Red Hat OpenShift. When a critical vulnerability is detected—such as a compromised base image showcased in the demo—the pipeline halts deployment before the code can reach a production environment, preventing vulnerable software from ever being deployed.