[{"data":1,"prerenderedAt":818},["ShallowReactive",2],{"/en-us/blog/use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project":3,"navigation-en-us":44,"banner-en-us":453,"footer-en-us":463,"blog-post-authors-en-us-Cesar Saavedra":702,"blog-related-posts-en-us-use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project":716,"blog-promotions-en-us":756,"next-steps-en-us":808},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":30,"isFeatured":12,"meta":31,"navigation":12,"path":32,"publishedDate":20,"seo":33,"stem":38,"tagSlugs":39,"__hash__":43},"blogPosts/en-us/blog/use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project.yml","Use Gitlab Duo To Build And Deploy A Simple Quarkus Native Project",[7],"cesar-saavedra",null,"ai-ml",{"slug":11,"featured":12,"template":13},"use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Use GitLab Duo to build and deploy a simple Quarkus-native project","This tutorial shows how a Java application is compiled to machine code and deployed to a Kubernetes cluster using a CI/CD pipeline. See how AI makes the process faster and more efficient.",[18],"Cesar Saavedra","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749666069/Blog/Hero%20Images/AdobeStock_639935439.jpg","2024-10-17","In [“How to automate software delivery using Quarkus and GitLab,”](https://about.gitlab.com/blog/how-to-automate-software-delivery-using-quarkus-and-gitlab/) you learned how to develop and deploy a simple Quarkus-JVM application to a Kubernetes cluster using [GitLab Auto DevOps](https://docs.gitlab.com/ee/topics/autodevops/). Now, you'll learn how to use Quarkus-native to compile a Java application to machine code and deploy it to a Kubernetes cluster using a CI/CD pipeline. Follow our journey from development to deployment leveraging [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) as our AI companion, including the specific prompts we used.\n\n## What is Quarkus?\n\n[Quarkus](https://quarkus.io/), also known as the Supersonic Subatomic Java, is an open source, Kubernetes-native Java stack tailored to OpenJDK HotSpot and GraalVM. The Quarkus project recently moved to the [Commonhaus Foundation](https://www.commonhaus.org/), a nonprofit organization dedicated to the sustainability of open source libraries and frameworks that provides a balanced approach to governance and support.\n\n## Prerequisites\n\nThis tutorial assumes:\n\n- You have a running Kubernetes cluster, e.g. GKE.\n- You have access to the Kubernetes cluster from your local laptop via the `kubectl` command.\n- The cluster is connected to your GitLab project.\n- You have [Maven (Version 3.9.6 or later)](https://maven.apache.org/) installed on your local laptop.\n- You have Visual Studio Code installed on your local laptop.\n\nIf you’d like to set up a Kubernetes cluster connected to your GitLab project, you can follow the instructions in this [tutorial](https://about.gitlab.com/blog/eliminate-risk-with-feature-flags-tutorial/), up to but not including the “Creating an instance of MySQL database in your cluster via Flux” section (you do not need a database for this tutorial).\n\nYou will also need to install an nginx ingress in your Kubernetes cluster. Here are two ways to do this:\n1. You can follow the instructions in [“Creating and importing projects”](https://about.gitlab.com/blog/eliminate-risk-with-feature-flags-tutorial/#creating-and-importing-projects), up to the creation of the variable `KUBE_INGRESS_BASE_DOMAIN`.\n2. Or, just create an ingress in your Kubernetes cluster by following the instructions in our [Auto DevOps with GKE documentation](https://docs.gitlab.com/ee/topics/autodevops/cloud_deployments/auto_devops_with_gke.html#install-ingress).\n\n**NOTE:** For this article, we used the first method above to install an ingress and cert-manager in the Kubernetes cluster.\n\n## Creating necessary project files using GitLab Duo Chat\n\nWe started our endeavor from VS Code and an empty project called `quarkus-native`, which we had previously created in GitLab and had already cloned to our local laptop.\n\n1. We opened GitLab Duo Chat, within VS Code, and entered the following prompt:\n\n**_Create a “Hello World” Quarkus application that can be natively compiled_**\n\n![hello-world-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/hello-world-prompt.png)\n\nChat replied with the prerequisites and process of what to do to create a simple “Hello World” Quarkus application. At this point, we were after the generated body of the class `HelloResource.java`:\n\n```text\npackage org.acme;\n\nimport javax.ws.rs.GET;\nimport javax.ws.rs.Path;\nimport javax.ws.rs.Produces;\nimport javax.ws.rs.core.MediaType;\n\n@Path(\"/hello\")\npublic class HelloResource {\n\n @GET\n @Produces(MediaType.TEXT_PLAIN)\n public String hello() {\n return \"Hello World\";\n }\n}\n```\n\n2. We knew that for Quarkus we needed a special Dockerfile, named `Dockerfile.native`, as part of the creation of the image for the application executable. We then entered the following prompt in Chat:\n\n**_Create the Dockerfile.native file for the sample application above_**\n\n![create-dockerfile-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675941/Blog/Content%20Images/create-dockerfile-prompt.png)\n\nChat replied with a very thorough answer about this file, its contents, and the containerization process. At this point, we were just after the generated body of the file `Dockerfile.native`:\n\n```text\n####\n# This Dockerfile is used in order to build a container that runs the Quarkus application in native (no JVM) mode\n####\nFROM registry.access.redhat.com/ubi8/ubi-minimal:8.6\nWORKDIR /work/\nRUN chown 1001 /work \\\n && chmod \"g+rwX\" /work \\\n && chown 1001:root /work\nCOPY --chown=1001:root target/*-runner /work/application\n\nEXPOSE 8080\nUSER 1001\n\nCMD [\"./application\", \"-Dquarkus.http.host=0.0.0.0\"]\n```\n\n3. Next, we asked Chat to generate the `pom.xml` for this application by entering the following prompt:\n\n**_Create the pom.xml file for the sample application above_**\n\n![create-pom-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/create-pom-prompt.png)\n\nChat provided the content of the `pom.xml` file, an explanation of its contents, and how to use it. At this point, we were after the generated body of the file `pom.xml`:\n\n```xml\n\u003C?xml version=\"1.0\"?>\n\u003Cproject xsi:schemaLocation=\"http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd\" xmlns=\"http://maven.apache.org/POM/4.0.0\"\n xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\">\n \u003CmodelVersion>4.0.0\u003C/modelVersion>\n \u003CgroupId>org.acme\u003C/groupId>\n \u003CartifactId>hello-world-quarkus\u003C/artifactId>\n \u003Cversion>1.0.0-SNAPSHOT\u003C/version>\n \u003Cproperties>\n \u003Ccompiler-plugin.version>3.10.1\u003C/compiler-plugin.version>\n \u003Cmaven.compiler.release>11\u003C/maven.compiler.release>\n \u003Cproject.build.sourceEncoding>UTF-8\u003C/project.build.sourceEncoding>\n \u003Cproject.reporting.outputEncoding>UTF-8\u003C/project.reporting.outputEncoding>\n \u003Cquarkus.platform.artifact-id>quarkus-bom\u003C/quarkus.platform.artifact-id>\n \u003Cquarkus.platform.group-id>io.quarkus.platform\u003C/quarkus.platform.group-id>\n \u003Cquarkus.platform.version>2.16.5.Final\u003C/quarkus.platform.version>\n \u003CskipITs>true\u003C/skipITs>\n \u003Csurefire-plugin.version>3.0.0-M7\u003C/surefire-plugin.version>\n \u003C/properties>\n \u003CdependencyManagement>\n \u003Cdependencies>\n \u003Cdependency>\n \u003CgroupId>${quarkus.platform.group-id}\u003C/groupId>\n \u003CartifactId>${quarkus.platform.artifact-id}\u003C/artifactId>\n \u003Cversion>${quarkus.platform.version}\u003C/version>\n \u003Ctype>pom\u003C/type>\n \u003Cscope>import\u003C/scope>\n \u003C/dependency>\n \u003C/dependencies>\n \u003C/dependencyManagement>\n \u003Cdependencies>\n \u003Cdependency>\n \u003CgroupId>io.quarkus\u003C/groupId>\n \u003CartifactId>quarkus-arc\u003C/artifactId>\n \u003C/dependency>\n \u003Cdependency>\n \u003CgroupId>io.quarkus\u003C/groupId>\n \u003CartifactId>quarkus-resteasy\u003C/artifactId>\n \u003C/dependency>\n \u003Cdependency>\n \u003CgroupId>io.quarkus\u003C/groupId>\n \u003CartifactId>quarkus-junit5\u003C/artifactId>\n \u003Cscope>test\u003C/scope>\n \u003C/dependency>\n \u003Cdependency>\n \u003CgroupId>io.rest-assured\u003C/groupId>\n \u003CartifactId>rest-assured\u003C/artifactId>\n \u003Cscope>test\u003C/scope>\n \u003C/dependency>\n \u003C/dependencies>\n \u003Cbuild>\n \u003Cplugins>\n \u003Cplugin>\n \u003CgroupId>${quarkus.platform.group-id}\u003C/groupId>\n \u003CartifactId>quarkus-maven-plugin\u003C/artifactId>\n \u003Cversion>${quarkus.platform.version}\u003C/version>\n \u003Cextensions>true\u003C/extensions>\n \u003Cexecutions>\n \u003Cexecution>\n \u003Cgoals>\n \u003Cgoal>build\u003C/goal>\n \u003Cgoal>generate-code\u003C/goal>\n \u003Cgoal>generate-code-tests\u003C/goal>\n \u003C/goals>\n \u003C/execution>\n \u003C/executions>\n \u003C/plugin>\n \u003Cplugin>\n \u003CartifactId>maven-compiler-plugin\u003C/artifactId>\n \u003Cversion>${compiler-plugin.version}\u003C/version>\n \u003Cconfiguration>\n \u003CcompilerArgs>\n \u003Carg>-parameters\u003C/arg>\n \u003C/compilerArgs>\n \u003C/configuration>\n \u003C/plugin>\n \u003Cplugin>\n \u003CartifactId>maven-surefire-plugin\u003C/artifactId>\n \u003Cversion>${surefire-plugin.version}\u003C/version>\n \u003Cconfiguration>\n \u003CsystemPropertyVariables>\n \u003Cjava.util.logging.manager>org.jboss.logmanager.LogManager\u003C/java.util.logging.manager>\n \u003Cmaven.home>${maven.home}\u003C/maven.home>\n \u003C/systemPropertyVariables>\n \u003C/configuration>\n \u003C/plugin>\n \u003Cplugin>\n \u003CartifactId>maven-failsafe-plugin\u003C/artifactId>\n \u003Cversion>${surefire-plugin.version}\u003C/version>\n \u003Cexecutions>\n \u003Cexecution>\n \u003Cgoals>\n \u003Cgoal>integration-test\u003C/goal>\n \u003Cgoal>verify\u003C/goal>\n \u003C/goals>\n \u003Cconfiguration>\n \u003CsystemPropertyVariables>\n \u003Cnative.image.path>${project.build.directory}/${project.build.finalName}-runner\u003C/native.image.path>\n \u003Cjava.util.logging.manager>org.jboss.logmanager.LogManager\u003C/java.util.logging.manager>\n \u003Cmaven.home>${maven.home}\u003C/maven.home>\n \u003C/systemPropertyVariables>\n \u003C/configuration>\n \u003C/execution>\n \u003C/executions>\n \u003C/plugin>\n \u003C/plugins>\n \u003C/build>\n \u003Cprofiles>\n \u003Cprofile>\n \u003Cid>native\u003C/id>\n \u003Cactivation>\n \u003Cproperty>\n \u003Cname>native\u003C/name>\n \u003C/property>\n \u003C/activation>\n \u003Cproperties>\n \u003CskipITs>false\u003C/skipITs>\n \u003Cquarkus.package.type>native\u003C/quarkus.package.type>\n \u003C/properties>\n \u003C/profile>\n \u003C/profiles>\n\u003C/project>\n```\n\n4. We then asked Chat to generate the Quarkus test file for this application by entering the following prompt:\n\n**_Create the content for the Quarkus test for the application above_**\n\n![create-quarkus-test-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/create-quarkus-test-prompt.png)\n\nChat returned the body for the Quarkus test, how to use it, and the body of the Java class for the test needed for its native compilation. At this point, we were after the generated bodies of the file `HelloResourceTest.java` and `HelloResourceIT.java`.\n\nHere is the generated body for `HelloResourceTest.java`:\n\n```text\npackage org.acme;\n\nimport io.quarkus.test.junit.QuarkusTest;\nimport org.junit.jupiter.api.Test;\n\nimport static io.restassured.RestAssured.given;\nimport static org.hamcrest.CoreMatchers.is;\n\n@QuarkusTest\npublic class HelloResourceTest {\n\n @Test\n public void testHelloEndpoint() {\n given()\n .when().get(\"/hello\")\n .then()\n .statusCode(200)\n .body(is(\"Hello World\"));\n }\n\n}\n```\n\nHere is the generated body for `HelloResourceIT.java`:\n\n```text\npackage org.acme;\n\nimport io.quarkus.test.junit.QuarkusIntegrationTest;\n\n@QuarkusIntegrationTest\npublic class HelloResourceIT extends HelloResourceTest {\n // Execute the same tests but in native mode.\n}\n```\n\n5. We needed to know how to organize these files in the GitLab project, so we asked about the directory structure for all these files by entering the following prompt in Chat:\n\n**_Give me the entire directory structure for this project including the location of each file, e.g. pom.xml, Dockerfile.native, application.properties, HelloResource.java, HelloResourceTest.java, and the location of the target directory_**\n\n![create-dir-struct-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/create-dir-struct-prompt.png)\n\nChat replied with a detailed diagram about the entire directory structure for the project and where all these files should be located as well as a description of the purpose of each of them. It even mentioned that the directory `target/` and its contents should not be version controlled since it was generated by the build process. Another interesting aspect of the reply was the existence of a file called `resources/application.properties` in the directory structure.\n\n![dir-struct-chat-response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/dir-struct-chat-response.png)\n\nWith all this information in our hands, we were ready to start creating these files in our GitLab project.\n\n## Populating our project with the generated content for each file\n\nWe created each of the following files in their corresponding location and their generated content as provided by Chat:\n\n- `src/main/java/org/acme/HelloResource.java`\n- `resources/application.properties`\n- `src/test/java/org/acme/HelloResourceTest.java`\n- `src/test/java/org/acme/HelloResourceIT.java`\n- `pom.xml`\n- `Dockerfile.native`\n\n**NOTE:** We considered using GitLab Auto Deploy for this endeavor but later realized that it would not be a supported option. We are mentioning this because in the video at the end of this tutorial, you will see that we asked Chat: `How to set the service internalPort to 8080 for auto deploy`. Then we created a file named `.gitlab/auto-deploy-values.yaml` with the generated content from Chat. The creation of this file is not necessary for this tutorial.\n\nBefore we started tackling the pipeline to build, containerize, and deploy the application to our Kubernetes cluster, we decided to generate the executable locally on our Mac and test the application locally.\n\n## Testing the application locally\n\nHere is the process we went through to test the application on our local machine.\n\n1. To build the application on the local Mac laptop, from a Terminal window, we entered the following command:\n\n```shell\nmvn clean package -Pnative\n```\n\n![first-build](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/first-build.png)\n\nThe native compilation failed with the error message:\n\n`Cannot find the ‘native-image’ in the GRAALVM_HOME, JAVA_HOME and System PATH. Install it using ‘gu install native-image’`\n\n2. So, we used our trusty GitLab Duo Chat again and asked it the following:\n\n**_The command “mvn clean package -Pnative” is failing with error “java.lang.RuntimeException: Cannot find the ‘native-image’ in the GRAALVM_HOME, JAVA_HOME and System PATH. Install it using gu install native-image”. I’m using a MacOS Sonoma. How do I fix this error on my Mac?_**\n\n![how-to-fix-build-failure-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/how-to-fix-build-failure-prompt.png)\n\nChat replied with a detailed set of steps on how to install the necessary software and set the appropriate environment variables.\n\n3. We copied and pasted the following commands from the Chat window to a Terminal window:\n\n```shell\nbrew install –cask graalvm/tap/graalvm-ce-java17\nexport JAVA_HOME=/Library/Java/JavaVIrtualMachines/graalvm-ce-java17-22.3.1\nexport GRAALVM_HOME=${JAVA_HOME}\nexport PATH=${GRAALVM_HOME}/bin:$PATH\nxattr -r -d com.apple.quarantine ${GRAALVM_HOME}/../..\ngu install native-image\n```\n\nThe commands above installed the community edition of GraalVM Version 22.3.1 that supported Java 17. We noticed, during the brew install, that the version of the GraalVM being installed was `java17-22.3.1`, so we had to update the pasted value for `JAVA_HOME` from `graalvm-ce-java17-22.3.0` to `graalvm-ce-java17-22.3.1`.\n\nWe also had to run the `xattr` command to get the GraalVM, which we had downloaded and installed on our Mac, out of quarantine so that it could run locally. Lastly, we installed the GraalVM native-image.\n\n4. At this point, we again, from a Terminal window, entered the following command to build the application on the local Mac laptop:\n\n```shell\nmvn clean package -Pnative\n```\n\nThis time the compilation was successful and an executable was generated in the `target` directory.\n\n![successful-local-compilation](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/successful-local-compilation.png)\n\n5. We ran the executable by entering the following commands from a Terminal window:\n\n```shell\ncd target\n./quarkus-native-1.0.0-SNAPSHOT-runner “-Dquarkus.http.host=0.0.0.0”\n```\n\n![executable-local-run](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/executable-local-run.png)\n\n6. With the application running, we opened a browser window, and in the URL field, we entered:\n\n```text\nhttp://localhost:8080/hello\n```\n\n![app-running-locally](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/app-running-locally.png)\n\nThe application returned the string `Hello World`, which was displayed in the browser window.\n\nAt this point, we committed and pushed all the changes to our GitLab project and started working on creating a CI/CD pipeline that would build and deploy the application to a Kubernetes cluster running on the cloud.\n\nBut before continuing, we remembered to add, commit, and push a `.gitignore` file to our project that included the path `target/`, since this was the directory where the executable would be created and we didn’t need to keep it - or its contents - under version control.\n\n## Creating the pipeline with GitLab Duo Chat\n\nNow that we had already successfully tested the application locally on our Mac, we needed to create the CI/CD pipeline that would compile the application, containerize it, and deploy it to our Kubernetes cluster. We wanted to keep the pipeline simple, brief, and have a single environment in which to deploy it. To this end, the pipeline would not tackle multiple environments or feature branches, for example.\n\n1. To avoid manually creating a pipeline from scratch, we decided to once again leverage Chat. We entered the following prompt\n\n**_Create a .gitlab-ci.yml file with 3 stages: build, containerize, and deploy. Each of these stages should have a single job with the same name. The build job should compile the application natively using the -Pnative maven option and the builder image for mandrel jdk-22 for java17 and store the application executable and its Dockerfile as artifacts. The containerize job should use docker to build and push the image to the built-in container registry. The deploy job should rollout the containerized application, named quarkus-app, to the production environment in the Kubernetes cluster by creating a deployment resource, a service resource and an ingress rule with host ${KUBE_INGRESS_BASE_DOMAIN} and /hello prefix. The deploy job should also include the url for the production environment. When setting the Kubernetes context, use ${KUBE_CONTEXT}, and for application namespace, use ${KUBE_NAMESPACE}_**\n\n![create-pipeline-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/create-pipeline-prompt.png)\n\nThe prompt above provided some points that we thought would be useful and necessary for a good outcome. The requirements listed by the prompt were:\n\n- It specified that the pipeline needed to have three stages: build, containerize, and deploy.\n- Each stage would have a single job.\n- The build job should natively compile (not build) the application using the -Pnative option.\n- The build job should use the open source software `mandrel` and use the version that would support jdk-22 for Java 17. Remember that these were the versions that we tested on our local machine.\n- The build job would also need to pass the resulting executable and the project Dockerfile to the next job, i.e. containerize, in the pipeline.\n- The containerize job should build a docker image with the executable using the Dockerfile, both of which were passed as artifacts.\n- The containerize job should push the image to the GitLab container registry.\n- The deploy job should use the image from the GitLab container registry, name it `quarkus-app`, and deploy it to the Kubernetes cluster using deployment, service and ingress rule resources.\n- The deploy job should use the environment variables `${KUBE_INGRESS_BASE_DOMAIN}`, `${KUBE_CONTEXT}`, and `${KUBE_NAMESPACE}`, which had been defined as group/project variables, accordingly.\n\nChat responded with the following `.gitlab-ci.yml` example:\n\n```text\n# Define the stages for the pipeline\nstages:\n - build\n - containerize\n - deploy\n\n# Variables section to use GitLab CI/CD variables\nvariables:\n MAVEN_OPTS: \"-Dmaven.repo.local=.m2/repository\"\n MAVEN_CLI_OPTS: \"--batch-mode --errors --fail-at-end --show-version -DinstallAtEnd=true -DdeployAtEnd=true\"\n CONTAINER_IMAGE: ${CI_REGISTRY_IMAGE}:${CI_COMMIT_SHA}\n\n# Cache configuration for Maven dependencies\ncache:\n paths:\n - .m2/repository\n\n# Build stage: Compile the application natively\nbuild:\n stage: build\n image:\n name: quay.io/quarkus/ubi-quarkus-mandrel-builder-image:22.3-java17\n entrypoint:\n - \"\"\n script:\n - ./mvnw ${MAVEN_CLI_OPTS} clean package -Pnative\n artifacts:\n paths:\n - target/*-runner\n - src/main/docker/Dockerfile.native-micro\n # This job runs only on the main branch\n only:\n - main\n\n# Containerize stage: Build and push Docker image\ncontainerize:\n stage: containerize\n image: docker:20.10.16\n services:\n - docker:20.10.16-dind\n variables:\n DOCKER_TLS_CERTDIR: \"/certs\"\n before_script:\n - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n script:\n - docker build -f src/main/docker/Dockerfile.native-micro -t ${CONTAINER_IMAGE} .\n - docker push ${CONTAINER_IMAGE}\n # This job runs only on the main branch\n only:\n - main\n\n# Deploy stage: Deploy to Kubernetes\ndeploy:\n stage: deploy\n image: \n name: bitnami/kubectl:latest\n entrypoint: ['']\n script:\n - kubectl config use-context ${KUBE_CONTEXT}\n - kubectl config set-context --current --namespace=${KUBE_NAMESPACE}\n - |\n cat \u003C\u003CEOF | kubectl apply -f -\n apiVersion: apps/v1\n kind: Deployment\n metadata:\n name: quarkus-app\n spec:\n replicas: 1\n selector:\n matchLabels:\n app: quarkus-app\n template:\n metadata:\n labels:\n app: quarkus-app\n spec:\n containers:\n - name: quarkus-app\n image: ${CONTAINER_IMAGE}\n ports:\n - containerPort: 8080\n ---\n apiVersion: v1\n kind: Service\n metadata:\n name: quarkus-app\n spec:\n selector:\n app: quarkus-app\n ports:\n - port: 80\n targetPort: 8080\n ---\n apiVersion: networking.k8s.io/v1\n kind: Ingress\n metadata:\n name: quarkus-app\n spec:\n rules:\n - host: ${KUBE_INGRESS_BASE_DOMAIN}\n http:\n paths:\n - path: /hello\n pathType: Prefix\n backend:\n service:\n name: quarkus-app\n port: \n number: 80\n EOF\n - echo \"Application deployed to https://${KUBE_INGRESS_BASE_DOMAIN}/hello\"\n # This job runs only on the main branch\n only:\n - main\n\n```\n\n2. There were some things we needed to adjust in the sample `.gitlab-ci.yml` file above before we could commit it to our `main` branch. These are the updates we made to the file:\n\n- We deleted all occurrences of `only: -main` because we wanted to keep of pipeline definition file simple and with no branch-related rules.\n- We fixed the name of the file `Dockerfile.native-micro` to `Dockerfile.native`.\n\n3. At this point, we wanted to ensure that the deployment would be to the `production` environment so we asked Chat the following prompt:\n\n**_What is the syntax to specify an environment with its url in a pipeline?_**\n\n![how-to-add-env-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/how-to-add-env-prompt.png)\n\nThe response from Chat included an example of how to do this so we used this information to add the following environment block to our pipeline:\n\n```text\n\n environment:\n name: production\n url: http://${KUBE_INGRESS_BASE_DOMAIN}/hello\n\n```\n\n4. The example provided by Chat includes a URL that started with `https` and we modified that to `http` since we didn’t really need a secure connection for this simple application.\n\n5. Lastly, we noticed that in the `build` job, there was a script `mvnw` that we didn’t have in our project. So, we asked Chat the following:\n\n**_How can I get the mvnw script for Quarkus?_**\n\n![how-to-add-mvnw-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/how-to-add-mvnw-prompt.png)\n\nChat responded with the command to execute to bootstrap and create this script. We executed this command from a Terminal window:\n\n```shell\nmvn wrapper:wrapper\n```\n\nWe were now ready to commit all of our changes to the `main` branch and have the pipeline executed. However, on our first attempt, our first pipeline failed at the build job.\n\n## Troubleshooting using GitLab Duo Root Cause Analysis\n\nOur first attempt at running our brand-new pipeline failed. So, we took advantage of [GitLab Duo Root Cause Analysis](https://about.gitlab.com/blog/developing-gitlab-duo-blending-ai-and-root-cause-analysis-to-fix-ci-cd/), which looks at the job logs and provides a thorough natural language explanation (with examples) of the root cause of the problem and, most importantly, how to fix it.\n\n![build-job-troubleshooting](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/build-job-troubleshooting.png)\n\nRoot Cause Analysis recommended we look at the compatibility of the command that was trying to be executed with the image of mandrel used in the build job. We were not using any command with the image so we concluded that it must have been the predefined `entrypoint` for the image itself. We needed to override this so we asked Chat the following:\n\n**_How do I override the entrypoint of an image using gitlab keywords?_**\n\n![how-to-override-entrypoint-prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/how-to-override-entrypoint-prompt.png)\n\nChat replied with some use case examples of overriding an image entry point. We used that information to update the build job image definition:\n\n```yaml\nbuild:\n stage: build\n image: quay.io/quarkus/ubi-quarkus-mandrel-builder-image:22.3-java17\n entrypoint:\n - “”\n\n```\n\nWe committed our changes to the `main` branch, which launched a new instance of the pipeline. This time the build job executed successfully but the pipeline failed at the `containerize` job.\n\n## Running a successful pipeline\n\nBefore drilling down into the log of the failed `containerize` job, we decided to drill into the log of the successfully completed build job first. Everything looked good in the log of the build job with the exception of this warning message at the very end of it:\n\n```text\nWARNING: src/main/docker/Dockerfile.native: no matching files. Ensure that the artifact path is relative to the working directory …\n``` \n\nWe took notice of this warning and then headed to the log of the failed `containerize` job. In it, we saw that the `docker build` command had failed due to a non-existent Dockerfile. We ran Root Cause Analysis on the job and among its suggested fixes was for us to verify that the project structure matched the path of the specified `Dockerfile.native` file.\n\n![containerize-job-troubleshooting](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/containerize-job-troubleshooting.png)\n\nThis information confirmed our suspicion of the misplaced `Dockerfile.native` file. Instead of being at the directory `src/main/docker` as specified in the pipeline, it was located at the root directory of the project.\n\nSo, we went back to our project and updated every occurrence of the location of this file in our `.gitlab-ci.yml` file. We modified the two locations where this happened, one in the `build` job and one in the `containerize` job, as follows:\n\n```text\nsrc/main/docker/Dockerfile.native\n```\n\nto\n\n```text\nDockerfile.native\n```\n\nWe committed our updates to the `main` branch and this time our entire pipeline executed successfully!\n\n![pipeline-successful-run](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/pipeline-successful-run.png)\n\nOur last step was to check the running application in the `production` environment in our Kubernetes cluster.\n\n## Accessing the deployed application running in cluster\n\nOnce the pipeline ran successfully to completion, we drilled in the log file for the `deploy` job. Remember, this job printed the URL of the application at the end of its execution. We scrolled down to the bottom of the log and clicked on the `https` application link, which opened a browser window warning us that the connection was not private (we disabled `https` for the environment URL but forgot it for this string). We proceeded past the browser warning and then the string \"Hello World\" was displaced in the browser window indicating that the application was up and running in the Kubernetes cluster.\n\nFinally, to double-check our production deployment URL, we headed to the project **Operate > Environments** window, and clicked on the \"Open\" button for it, which immediately opened a browser window with the \"Hello World\" message.\n\n![app-running-on-k8s](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675940/Blog/Content%20Images/app-running-on-k8s.png)\n\n## Try it \n\nWe created, compiled, built, and deployed a simple Quarkus application to a Kubernetes cluster using [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/). This approach allowed us to be more efficient and productive in all the tasks that we performed and it helped us streamline our DevSecOps processes. We have shown only a small portion of how GitLab Duo's AI-powered capabilities can help you, namely Chat and Root Cause Analysis. There’s so much more you can leverage in GitLab Duo to help you create better software faster and more securely.\n\nWatch this whole use case in action:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n \u003Ciframe src=\"https://www.youtube.com/embed/xDpycxz3RPY?si=HHZrFt1O_8XoLATf\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nAll the project assets we used are available [here](https://gitlab.com/gitlab-da/use-cases/ai/ai-applications/quarkusn/quarkus-native).\n\n> [Try GitLab Duo for free](https://about.gitlab.com/sales/?type=free-trial&toggle=gitlab-duo-pro) and get started on exciting projects like this.\n",[23,24,25,26,27,28,29],"AI/ML","tutorial","DevSecOps","demo","features","product","CI/CD","yml",{},"/en-us/blog/use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":34,"ogImage":19,"ogUrl":35,"ogSiteName":36,"ogType":37,"canonicalUrls":35},false,"https://about.gitlab.com/blog/use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project","https://about.gitlab.com","article","en-us/blog/use-gitlab-duo-to-build-and-deploy-a-simple-quarkus-native-project",[40,24,41,26,27,28,42],"aiml","devsecops","cicd","YcXX5nyqTCLSH6Z58PSF8rnJSaBXjfI7VDKZR2uegjY",{"data":45},{"logo":46,"freeTrial":51,"sales":56,"login":61,"items":66,"search":373,"minimal":404,"duo":423,"switchNav":432,"pricingDeployment":443},{"config":47},{"href":48,"dataGaName":49,"dataGaLocation":50},"/","gitlab logo","header",{"text":52,"config":53},"Get free trial",{"href":54,"dataGaName":55,"dataGaLocation":50},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":57,"config":58},"Talk to sales",{"href":59,"dataGaName":60,"dataGaLocation":50},"/sales/","sales",{"text":62,"config":63},"Sign in",{"href":64,"dataGaName":65,"dataGaLocation":50},"https://gitlab.com/users/sign_in/","sign in",[67,94,188,193,294,354],{"text":68,"config":69,"cards":71},"Platform",{"dataNavLevelOne":70},"platform",[72,78,86],{"title":68,"description":73,"link":74},"The intelligent orchestration platform for DevSecOps",{"text":75,"config":76},"Explore our Platform",{"href":77,"dataGaName":70,"dataGaLocation":50},"/platform/",{"title":79,"description":80,"link":81},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":82,"config":83},"Meet GitLab Duo",{"href":84,"dataGaName":85,"dataGaLocation":50},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":87,"description":88,"link":89},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":90,"config":91},"Learn more",{"href":92,"dataGaName":93,"dataGaLocation":50},"/why-gitlab/","why gitlab",{"text":95,"left":12,"config":96,"link":98,"lists":102,"footer":170},"Product",{"dataNavLevelOne":97},"solutions",{"text":99,"config":100},"View all Solutions",{"href":101,"dataGaName":97,"dataGaLocation":50},"/solutions/",[103,126,149],{"title":104,"description":105,"link":106,"items":111},"Automation","CI/CD and automation to accelerate deployment",{"config":107},{"icon":108,"href":109,"dataGaName":110,"dataGaLocation":50},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[112,115,118,122],{"text":29,"config":113},{"href":114,"dataGaLocation":50,"dataGaName":29},"/solutions/continuous-integration/",{"text":79,"config":116},{"href":84,"dataGaLocation":50,"dataGaName":117},"gitlab duo agent platform - product menu",{"text":119,"config":120},"Source Code Management",{"href":121,"dataGaLocation":50,"dataGaName":119},"/solutions/source-code-management/",{"text":123,"config":124},"Automated Software Delivery",{"href":109,"dataGaLocation":50,"dataGaName":125},"Automated software delivery",{"title":127,"description":128,"link":129,"items":134},"Security","Deliver code faster without compromising security",{"config":130},{"href":131,"dataGaName":132,"dataGaLocation":50,"icon":133},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[135,139,144],{"text":136,"config":137},"Application Security Testing",{"href":131,"dataGaName":138,"dataGaLocation":50},"Application security testing",{"text":140,"config":141},"Software Supply Chain Security",{"href":142,"dataGaLocation":50,"dataGaName":143},"/solutions/supply-chain/","Software supply chain security",{"text":145,"config":146},"Software Compliance",{"href":147,"dataGaName":148,"dataGaLocation":50},"/solutions/software-compliance/","software compliance",{"title":150,"link":151,"items":156},"Measurement",{"config":152},{"icon":153,"href":154,"dataGaName":155,"dataGaLocation":50},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[157,161,165],{"text":158,"config":159},"Visibility & Measurement",{"href":154,"dataGaLocation":50,"dataGaName":160},"Visibility and Measurement",{"text":162,"config":163},"Value Stream Management",{"href":164,"dataGaLocation":50,"dataGaName":162},"/solutions/value-stream-management/",{"text":166,"config":167},"Analytics & Insights",{"href":168,"dataGaLocation":50,"dataGaName":169},"/solutions/analytics-and-insights/","Analytics and insights",{"title":171,"items":172},"GitLab for",[173,178,183],{"text":174,"config":175},"Enterprise",{"href":176,"dataGaLocation":50,"dataGaName":177},"/enterprise/","enterprise",{"text":179,"config":180},"Small Business",{"href":181,"dataGaLocation":50,"dataGaName":182},"/small-business/","small business",{"text":184,"config":185},"Public Sector",{"href":186,"dataGaLocation":50,"dataGaName":187},"/solutions/public-sector/","public sector",{"text":189,"config":190},"Pricing",{"href":191,"dataGaName":192,"dataGaLocation":50,"dataNavLevelOne":192},"/pricing/","pricing",{"text":194,"config":195,"link":197,"lists":201,"feature":281},"Resources",{"dataNavLevelOne":196},"resources",{"text":198,"config":199},"View all resources",{"href":200,"dataGaName":196,"dataGaLocation":50},"/resources/",[202,235,253],{"title":203,"items":204},"Getting started",[205,210,215,220,225,230],{"text":206,"config":207},"Install",{"href":208,"dataGaName":209,"dataGaLocation":50},"/install/","install",{"text":211,"config":212},"Quick start guides",{"href":213,"dataGaName":214,"dataGaLocation":50},"/get-started/","quick setup checklists",{"text":216,"config":217},"Learn",{"href":218,"dataGaLocation":50,"dataGaName":219},"https://university.gitlab.com/","learn",{"text":221,"config":222},"Product documentation",{"href":223,"dataGaName":224,"dataGaLocation":50},"https://docs.gitlab.com/","product documentation",{"text":226,"config":227},"Best practice videos",{"href":228,"dataGaName":229,"dataGaLocation":50},"/getting-started-videos/","best practice videos",{"text":231,"config":232},"Integrations",{"href":233,"dataGaName":234,"dataGaLocation":50},"/integrations/","integrations",{"title":236,"items":237},"Discover",[238,243,248],{"text":239,"config":240},"Customer success stories",{"href":241,"dataGaName":242,"dataGaLocation":50},"/customers/","customer success stories",{"text":244,"config":245},"Blog",{"href":246,"dataGaName":247,"dataGaLocation":50},"/blog/","blog",{"text":249,"config":250},"Remote",{"href":251,"dataGaName":252,"dataGaLocation":50},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":254,"items":255},"Connect",[256,261,266,271,276],{"text":257,"config":258},"GitLab Services",{"href":259,"dataGaName":260,"dataGaLocation":50},"/services/","services",{"text":262,"config":263},"Community",{"href":264,"dataGaName":265,"dataGaLocation":50},"/community/","community",{"text":267,"config":268},"Forum",{"href":269,"dataGaName":270,"dataGaLocation":50},"https://forum.gitlab.com/","forum",{"text":272,"config":273},"Events",{"href":274,"dataGaName":275,"dataGaLocation":50},"/events/","events",{"text":277,"config":278},"Partners",{"href":279,"dataGaName":280,"dataGaLocation":50},"/partners/","partners",{"backgroundColor":282,"textColor":283,"text":284,"image":285,"link":289},"#2f2a6b","#fff","Insights for the future of software development",{"altText":286,"config":287},"the source promo card",{"src":288},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":290,"config":291},"Read the latest",{"href":292,"dataGaName":293,"dataGaLocation":50},"/the-source/","the source",{"text":295,"config":296,"lists":298},"Company",{"dataNavLevelOne":297},"company",[299],{"items":300},[301,306,312,314,319,324,329,334,339,344,349],{"text":302,"config":303},"About",{"href":304,"dataGaName":305,"dataGaLocation":50},"/company/","about",{"text":307,"config":308,"footerGa":311},"Jobs",{"href":309,"dataGaName":310,"dataGaLocation":50},"/jobs/","jobs",{"dataGaName":310},{"text":272,"config":313},{"href":274,"dataGaName":275,"dataGaLocation":50},{"text":315,"config":316},"Leadership",{"href":317,"dataGaName":318,"dataGaLocation":50},"/company/team/e-group/","leadership",{"text":320,"config":321},"Team",{"href":322,"dataGaName":323,"dataGaLocation":50},"/company/team/","team",{"text":325,"config":326},"Handbook",{"href":327,"dataGaName":328,"dataGaLocation":50},"https://handbook.gitlab.com/","handbook",{"text":330,"config":331},"Investor relations",{"href":332,"dataGaName":333,"dataGaLocation":50},"https://ir.gitlab.com/","investor relations",{"text":335,"config":336},"Trust Center",{"href":337,"dataGaName":338,"dataGaLocation":50},"/security/","trust center",{"text":340,"config":341},"AI Transparency Center",{"href":342,"dataGaName":343,"dataGaLocation":50},"/ai-transparency-center/","ai transparency center",{"text":345,"config":346},"Newsletter",{"href":347,"dataGaName":348,"dataGaLocation":50},"/company/contact/#contact-forms","newsletter",{"text":350,"config":351},"Press",{"href":352,"dataGaName":353,"dataGaLocation":50},"/press/","press",{"text":355,"config":356,"lists":357},"Contact us",{"dataNavLevelOne":297},[358],{"items":359},[360,363,368],{"text":57,"config":361},{"href":59,"dataGaName":362,"dataGaLocation":50},"talk to sales",{"text":364,"config":365},"Support portal",{"href":366,"dataGaName":367,"dataGaLocation":50},"https://support.gitlab.com","support portal",{"text":369,"config":370},"Customer portal",{"href":371,"dataGaName":372,"dataGaLocation":50},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":374,"login":375,"suggestions":382},"Close",{"text":376,"link":377},"To search repositories and projects, login to",{"text":378,"config":379},"gitlab.com",{"href":64,"dataGaName":380,"dataGaLocation":381},"search login","search",{"text":383,"default":384},"Suggestions",[385,387,391,393,397,401],{"text":79,"config":386},{"href":84,"dataGaName":79,"dataGaLocation":381},{"text":388,"config":389},"Code Suggestions (AI)",{"href":390,"dataGaName":388,"dataGaLocation":381},"/solutions/code-suggestions/",{"text":29,"config":392},{"href":114,"dataGaName":29,"dataGaLocation":381},{"text":394,"config":395},"GitLab on AWS",{"href":396,"dataGaName":394,"dataGaLocation":381},"/partners/technology-partners/aws/",{"text":398,"config":399},"GitLab on Google Cloud",{"href":400,"dataGaName":398,"dataGaLocation":381},"/partners/technology-partners/google-cloud-platform/",{"text":402,"config":403},"Why GitLab?",{"href":92,"dataGaName":402,"dataGaLocation":381},{"freeTrial":405,"mobileIcon":410,"desktopIcon":415,"secondaryButton":418},{"text":406,"config":407},"Start free trial",{"href":408,"dataGaName":55,"dataGaLocation":409},"https://gitlab.com/-/trials/new/","nav",{"altText":411,"config":412},"Gitlab Icon",{"src":413,"dataGaName":414,"dataGaLocation":409},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":411,"config":416},{"src":417,"dataGaName":414,"dataGaLocation":409},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":419,"config":420},"Get Started",{"href":421,"dataGaName":422,"dataGaLocation":409},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/get-started/","get started",{"freeTrial":424,"mobileIcon":428,"desktopIcon":430},{"text":425,"config":426},"Learn more about GitLab Duo",{"href":84,"dataGaName":427,"dataGaLocation":409},"gitlab duo",{"altText":411,"config":429},{"src":413,"dataGaName":414,"dataGaLocation":409},{"altText":411,"config":431},{"src":417,"dataGaName":414,"dataGaLocation":409},{"button":433,"mobileIcon":438,"desktopIcon":440},{"text":434,"config":435},"/switch",{"href":436,"dataGaName":437,"dataGaLocation":409},"#contact","switch",{"altText":411,"config":439},{"src":413,"dataGaName":414,"dataGaLocation":409},{"altText":411,"config":441},{"src":442,"dataGaName":414,"dataGaLocation":409},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1773335277/ohhpiuoxoldryzrnhfrh.png",{"freeTrial":444,"mobileIcon":449,"desktopIcon":451},{"text":445,"config":446},"Back to pricing",{"href":191,"dataGaName":447,"dataGaLocation":409,"icon":448},"back to pricing","GoBack",{"altText":411,"config":450},{"src":413,"dataGaName":414,"dataGaLocation":409},{"altText":411,"config":452},{"src":417,"dataGaName":414,"dataGaLocation":409},{"title":454,"button":455,"config":460},"See how agentic AI transforms software delivery",{"text":456,"config":457},"Watch GitLab Transcend now",{"href":458,"dataGaName":459,"dataGaLocation":50},"/events/transcend/virtual/","transcend event",{"layout":461,"icon":462,"disabled":12},"release","AiStar",{"data":464},{"text":465,"source":466,"edit":472,"contribute":477,"config":482,"items":487,"minimal":691},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":467,"config":468},"View page source",{"href":469,"dataGaName":470,"dataGaLocation":471},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":473,"config":474},"Edit this page",{"href":475,"dataGaName":476,"dataGaLocation":471},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":478,"config":479},"Please contribute",{"href":480,"dataGaName":481,"dataGaLocation":471},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":483,"facebook":484,"youtube":485,"linkedin":486},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[488,535,586,630,657],{"title":189,"links":489,"subMenu":504},[490,494,499],{"text":491,"config":492},"View plans",{"href":191,"dataGaName":493,"dataGaLocation":471},"view plans",{"text":495,"config":496},"Why Premium?",{"href":497,"dataGaName":498,"dataGaLocation":471},"/pricing/premium/","why premium",{"text":500,"config":501},"Why Ultimate?",{"href":502,"dataGaName":503,"dataGaLocation":471},"/pricing/ultimate/","why ultimate",[505],{"title":506,"links":507},"Contact Us",[508,511,513,515,520,525,530],{"text":509,"config":510},"Contact sales",{"href":59,"dataGaName":60,"dataGaLocation":471},{"text":364,"config":512},{"href":366,"dataGaName":367,"dataGaLocation":471},{"text":369,"config":514},{"href":371,"dataGaName":372,"dataGaLocation":471},{"text":516,"config":517},"Status",{"href":518,"dataGaName":519,"dataGaLocation":471},"https://status.gitlab.com/","status",{"text":521,"config":522},"Terms of use",{"href":523,"dataGaName":524,"dataGaLocation":471},"/terms/","terms of use",{"text":526,"config":527},"Privacy statement",{"href":528,"dataGaName":529,"dataGaLocation":471},"/privacy/","privacy statement",{"text":531,"config":532},"Cookie preferences",{"dataGaName":533,"dataGaLocation":471,"id":534,"isOneTrustButton":12},"cookie preferences","ot-sdk-btn",{"title":95,"links":536,"subMenu":545},[537,541],{"text":538,"config":539},"DevSecOps platform",{"href":77,"dataGaName":540,"dataGaLocation":471},"devsecops platform",{"text":542,"config":543},"AI-Assisted Development",{"href":84,"dataGaName":544,"dataGaLocation":471},"ai-assisted development",[546],{"title":547,"links":548},"Topics",[549,553,558,563,568,571,576,581],{"text":550,"config":551},"CICD",{"href":552,"dataGaName":42,"dataGaLocation":471},"/topics/ci-cd/",{"text":554,"config":555},"GitOps",{"href":556,"dataGaName":557,"dataGaLocation":471},"/topics/gitops/","gitops",{"text":559,"config":560},"DevOps",{"href":561,"dataGaName":562,"dataGaLocation":471},"/topics/devops/","devops",{"text":564,"config":565},"Version Control",{"href":566,"dataGaName":567,"dataGaLocation":471},"/topics/version-control/","version control",{"text":25,"config":569},{"href":570,"dataGaName":41,"dataGaLocation":471},"/topics/devsecops/",{"text":572,"config":573},"Cloud Native",{"href":574,"dataGaName":575,"dataGaLocation":471},"/topics/cloud-native/","cloud native",{"text":577,"config":578},"AI for Coding",{"href":579,"dataGaName":580,"dataGaLocation":471},"/topics/devops/ai-for-coding/","ai for coding",{"text":582,"config":583},"Agentic AI",{"href":584,"dataGaName":585,"dataGaLocation":471},"/topics/agentic-ai/","agentic ai",{"title":587,"links":588},"Solutions",[589,591,593,598,602,605,609,612,614,617,620,625],{"text":136,"config":590},{"href":131,"dataGaName":136,"dataGaLocation":471},{"text":125,"config":592},{"href":109,"dataGaName":110,"dataGaLocation":471},{"text":594,"config":595},"Agile development",{"href":596,"dataGaName":597,"dataGaLocation":471},"/solutions/agile-delivery/","agile delivery",{"text":599,"config":600},"SCM",{"href":121,"dataGaName":601,"dataGaLocation":471},"source code management",{"text":550,"config":603},{"href":114,"dataGaName":604,"dataGaLocation":471},"continuous integration & delivery",{"text":606,"config":607},"Value stream management",{"href":164,"dataGaName":608,"dataGaLocation":471},"value stream management",{"text":554,"config":610},{"href":611,"dataGaName":557,"dataGaLocation":471},"/solutions/gitops/",{"text":174,"config":613},{"href":176,"dataGaName":177,"dataGaLocation":471},{"text":615,"config":616},"Small business",{"href":181,"dataGaName":182,"dataGaLocation":471},{"text":618,"config":619},"Public sector",{"href":186,"dataGaName":187,"dataGaLocation":471},{"text":621,"config":622},"Education",{"href":623,"dataGaName":624,"dataGaLocation":471},"/solutions/education/","education",{"text":626,"config":627},"Financial services",{"href":628,"dataGaName":629,"dataGaLocation":471},"/solutions/finance/","financial services",{"title":194,"links":631},[632,634,636,638,641,643,645,647,649,651,653,655],{"text":206,"config":633},{"href":208,"dataGaName":209,"dataGaLocation":471},{"text":211,"config":635},{"href":213,"dataGaName":214,"dataGaLocation":471},{"text":216,"config":637},{"href":218,"dataGaName":219,"dataGaLocation":471},{"text":221,"config":639},{"href":223,"dataGaName":640,"dataGaLocation":471},"docs",{"text":244,"config":642},{"href":246,"dataGaName":247,"dataGaLocation":471},{"text":239,"config":644},{"href":241,"dataGaName":242,"dataGaLocation":471},{"text":249,"config":646},{"href":251,"dataGaName":252,"dataGaLocation":471},{"text":257,"config":648},{"href":259,"dataGaName":260,"dataGaLocation":471},{"text":262,"config":650},{"href":264,"dataGaName":265,"dataGaLocation":471},{"text":267,"config":652},{"href":269,"dataGaName":270,"dataGaLocation":471},{"text":272,"config":654},{"href":274,"dataGaName":275,"dataGaLocation":471},{"text":277,"config":656},{"href":279,"dataGaName":280,"dataGaLocation":471},{"title":295,"links":658},[659,661,663,665,667,669,671,675,680,682,684,686],{"text":302,"config":660},{"href":304,"dataGaName":297,"dataGaLocation":471},{"text":307,"config":662},{"href":309,"dataGaName":310,"dataGaLocation":471},{"text":315,"config":664},{"href":317,"dataGaName":318,"dataGaLocation":471},{"text":320,"config":666},{"href":322,"dataGaName":323,"dataGaLocation":471},{"text":325,"config":668},{"href":327,"dataGaName":328,"dataGaLocation":471},{"text":330,"config":670},{"href":332,"dataGaName":333,"dataGaLocation":471},{"text":672,"config":673},"Sustainability",{"href":674,"dataGaName":672,"dataGaLocation":471},"/sustainability/",{"text":676,"config":677},"Diversity, inclusion and belonging (DIB)",{"href":678,"dataGaName":679,"dataGaLocation":471},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":335,"config":681},{"href":337,"dataGaName":338,"dataGaLocation":471},{"text":345,"config":683},{"href":347,"dataGaName":348,"dataGaLocation":471},{"text":350,"config":685},{"href":352,"dataGaName":353,"dataGaLocation":471},{"text":687,"config":688},"Modern Slavery Transparency Statement",{"href":689,"dataGaName":690,"dataGaLocation":471},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":692},[693,696,699],{"text":694,"config":695},"Terms",{"href":523,"dataGaName":524,"dataGaLocation":471},{"text":697,"config":698},"Cookies",{"dataGaName":533,"dataGaLocation":471,"id":534,"isOneTrustButton":12},{"text":700,"config":701},"Privacy",{"href":528,"dataGaName":529,"dataGaLocation":471},[703],{"id":704,"title":18,"body":8,"config":705,"content":707,"description":8,"extension":30,"meta":711,"navigation":12,"path":712,"seo":713,"stem":714,"__hash__":715},"blogAuthors/en-us/blog/authors/cesar-saavedra.yml",{"template":706},"BlogAuthor",{"name":18,"config":708},{"headshot":709,"ctfId":710},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659600/Blog/Author%20Headshots/csaavedra1-headshot.jpg","csaavedra1",{},"/en-us/blog/authors/cesar-saavedra",{},"en-us/blog/authors/cesar-saavedra","SMqRf-z0W5m5GROz_dXGjmuIb3YaOwm_n_RfeK16GcA",[717,729,744],{"content":718,"config":727},{"title":719,"description":720,"authors":721,"heroImage":723,"date":724,"body":725,"category":9,"tags":726},"GitHub Copilot's new policy for AI training is a governance wake-up call","Learn what GitHub's Copilot policy change means for regulated industries, and why GitLab's commitment to customer data privacy matters.",[722],"Allie Holland","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-20","GitHub recently [announced](https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/) a significant change to how it handles data from Copilot users. Starting April 24, 2026, interaction data from Copilot Free, Pro, and Pro+ users, including inputs, outputs, code snippets, and associated context, will be used to train AI models by default, unless users actively opt out. Copilot Business and Enterprise customers are exempt under existing contract terms.\n\nFor organizations in regulated industries, including finance, healthcare, defense, and public sector, the policy shift raises questions that go beyond individual developer preferences. It forces a harder look at a question that engineering and security leaders should be asking every AI vendor in their stack: Do you train on our code? \n\nGitLab's answer is no. GitLab does not train AI models on customer code at any tier, and AI vendors are contractually prohibited from using customer inputs or outputs for their own purposes. The [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) makes that commitment auditable: a single location documenting which models power which features, how data is handled, subprocessor relationships, and data retention periods. The GitLab AI Transparency Center also lists the compliance status of each feature, including confirmation that GitLab's current AI features do not qualify as high-risk systems under the EU AI Act. It's a standard GitLab CEO Bill Staples has consistently [reiterated](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf?utm_source=share&utm_medium=member_desktop&rcm=ACoAABsu7EUBcb_a1-JHKS9RC0B5rf8Ye-5XM60) and one reflected in GitLab's mission and [Trust Center](https://trust.gitlab.com/).\n\n## What the policy change actually means\n\nGitHub's announcement also specifies that the data may be shared with GitHub affiliates, including Microsoft, for AI development purposes.\n\nA policy change of this nature forces organizations to re-examine their AI governance posture, audit their Copilot license tiers, and confirm that the right controls are configured across their teams.\n\n## Why AI governance matters in regulated environments\n\nSource code is often among an organization's most sensitive intellectual property. It may contain references to internal systems, reflect proprietary business logic, or touch data flows governed by strict retention and access policies. When that code passes through an AI assistant, questions about training data usage, model vendor relationships, and data residency become compliance concerns.\n\nThe exposure is particularly acute for financial services firms that have invested in proprietary algorithms, fraud detection logic, credit risk models, underwriting rules, trading strategies. When AI tooling processes that code and uses it to train models serving competitors, vendor data practices become an IP concern.\n\nFinancial institutions operating under [the Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7) and the](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) [Digital Operational Resilience Act (DORA)](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) are required to maintain documented, auditable oversight of third-party technology providers, including understanding how those providers handle data. Third-party AI tools used in development workflows increasingly fall within the scope of model risk oversight, and material changes to vendor data practices require updated documentation. \n\nIn the public sector, [the National Institute of Standards and Technology Special Publication 800-53 (NIST 800-53)](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and the [Federal Information Security Modernization Act (FISMA)](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) establish that sensitive or classified code must never leave a controlled boundary. For U.S. Department of Defense and intelligence community environments in particular, a vendor's default data posture is an operational concern. In healthcare, [the Health Insurance Portability and Accountability Act (HIPAA)](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties, and development environments that touch clinical systems increasingly fall within that scope.\n\nAcross all of these contexts, the common thread is the same: A vendor policy that changes data usage defaults, requires individual opt-out, and offers different protections depending on account tier introduces exactly the kind of uncontrolled variable that compliance teams cannot afford.\n\n## What regulated industries actually need from AI vendors\n\nRegulated organizations have largely moved past debating whether to adopt AI in development workflows. The focus now is on doing so in a way they can defend to regulators, boards, and customers. That shift has surfaced a consistent set of requirements regardless of sector.\n\n**Contractual certainty.** Regulated firms need to know, with specificity, what happens to their data. A clear, documented, unconditional commitment is what's required, not something that varies by plan or requires action before a deadline.\n\n**Auditability.** Model risk management frameworks require organizations to understand and validate the AI systems they deploy, including the training data behind those models and the third parties involved in their development. Vendors who cannot answer these questions create documentation risk for the organizations relying on them.\n\n**Separation from vendor incentives.** When an AI vendor trains models on customer usage data, code and workflows become inputs to a system that also serves competitors. For institutions with proprietary trading logic, underwriting models, or fraud detection systems, that's a genuine IP exposure.\n\n## GitLab's position on AI data governance\n\nGitLab does not use customer code to train AI models. This commitment applies at every tier, and AI vendors are contractually prohibited from using inputs or outputs associated with GitLab customers for their own purposes.\n\nThis is a deliberate architectural and policy choice, not a feature of a particular pricing tier. As GitLab's [post on enterprise independence](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) notes, data governance has become \"an increasingly critical factor in enterprise technology decisions, driven by a complex web of national and regional data protection laws and growing concern about control over sensitive intellectual property.\"\n\nGitLab is also cloud-neutral and model-neutral while supporting self-hosted deployments, not commercially tied to any single cloud provider or large language model (LLM). That i[ndependence matters](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) for regulated organizations evaluating vendor concentration risk. The [AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including material changes to how AI vendors treat customer data, a direct response to the governance requirements under frameworks like [DORA](https://handbook.gitlab.com/handbook/legal/dora/). \n\n## The governance gap AI teams need to close\n\nGitHub's policy update is a reminder that for organizations in regulated industries, understanding exactly how an AI tool handles data is a prerequisite for using it at all. That means asking vendors for clear, documented answers: Is our data used for model training? Who are your AI model subprocessors? What happens if a vendor changes its data practices? Can we deploy in a way that keeps all AI processing within our own infrastructure? What indemnification do you offer for AI-generated output?\n\nVendors who can answer those questions clearly, and document those answers in an auditable form, are vendors you can build on. **Those who cannot will create compliance debt every time they ship a policy update.** And when a vendor can change its data practices with 30 days notice, that's not a partnership built for regulated industries. That's a liability.\n\n> Learn more about GitLab's approach to AI governance at the [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).",[23,28],{"featured":34,"template":13,"slug":728},"github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",{"content":730,"config":742},{"title":731,"description":732,"authors":733,"body":736,"heroImage":737,"date":738,"category":9,"tags":739},"GitLab and Vertex AI on Google Cloud: Advancing agentic software development","Learn how Google Cloud customers are standardizing on GitLab and Vertex AI for foundation models, enterprise controls, and Model Garden breadth.\n",[734,735],"Regnard Raquedan","Rajesh Agadi","GitLab Duo Agent Platform is helping redefine how organizations build, secure, and deliver software. Since its general availability in January 2026, the platform is bringing agentic AI to every phase of the software development lifecycle. Duo Agent Platform is an intelligent orchestration layer where software teams, and their specialized agents plan, code, review, and remediate security vulnerabilities together.\n\nThrough this exciting partnership, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) automates software development orchestration and lifecycle context via its integration with Vertex AI on Google Cloud, which powers the model tier for agent calls. Software teams keep working on issues, merge requests, pipelines, and security workflows while inference follows the Google Cloud posture they already defined. \n\nAdvances in Google Cloud’s Vertex AI models expand how Google Cloud customers can use GitLab Duo Agent Platform in their environment. Customers get an AI-powered DevSecOps control plane in GitLab, backed by a rapidly advancing AI infrastructure foundation in Vertex AI and Duo Agent Platform’s flexible deployment and integration options. The combination enables more capable, governed agentic workflows that operate at enterprise scale.\n\n![Conceptual illustration of the GitLab Duo Agent Platform integrated with Google Cloud's Vertex AI to power agentic software development and governed AI workflows](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776165990/b7jlux9kydafncwy8spc.png)\n\n## Agents that work across the full lifecycle\n\nMany AI tools focus on a single task: generating code faster. GitLab Duo Agent Platform goes further. It orchestrates AI agents across the entire software development lifecycle (SDLC), from planning through security review to delivery, across many teams with many projects and releases. At this scale, AI coding assistants are necessary for continuous innovation but not sufficient. \n\nSingle-purpose coding assistants rarely see the full state of a project. Backlog shape, open merge requests, failing jobs, and security findings live in GitLab, but a separate chat window in a coding assistant does not inherit that full picture of the SDLC. The gap shows up as manual handoffs, duplicate explanations to an AI that lacks context, and governance teams trying to map data flows across tools that were never designed as one system.\n\nGitLab Duo Agent Platform helps close that gap by running agents and flows on the same objects engineers use every day. Vertex AI then supplies the models and services those agents call when Google Cloud is your chosen inference home, with GitLab’s AI Gateway mediating access so administrators keep a clear map of what connects to what. For instance, GitLab Duo Planner Agent analyzes backlogs, breaks epics into structured tasks, and applies prioritization frameworks to help teams decide what to build next. Security Analyst Agent triages vulnerabilities, details risks in plain language, and recommends remediation in priority order. Built-in flows connect these agents into end-to-end processes, without requiring developers to manage every handoff manually.\n\nAgentic Chat in GitLab Duo Agent Platform ties the experience together for developers. They query in natural language to get context-aware responses with multi-step reasoning that draws on the full state of a project: its issues, merge requests, pipelines, security findings, and codebase. Because GitLab serves as the system of record for the SDLC with a unified data model, GitLab Duo agents operate with lifecycle context that falls outside the reach of standalone, tool-specific AI assistants.\n\n### Amplified by Vertex AI\n\nGitLab Duo Agent Platform is designed to be model-flexible, routing different capabilities to different models based on what performs best for a given task. That architectural choice pays off on Google Cloud, where Vertex AI acts as the managed environment for foundation models and related services, providing a broad model ecosystem and managed infrastructure that helps push the platform's capabilities further.\n\nThe latest generations of AI models available through Vertex AI bring significant improvements in reasoning, tool use, and long-context understanding compared to previous iterations — the same properties that GitLab's agents rely on across many projects and teams with large, complex codebases. Longer context windows and richer tool integration in the underlying models expand what agents can accomplish in a single pass, which is especially important for workloads like deep backlog analysis or monorepo security review.\n\n[Vertex AI Model Garden](https://cloud.google.com/model-garden), with access to a wide range of foundation models, gives customers the breadth to make these choices based on performance, cost, and regulatory requirements rather than vendor lock-in.\n\nMoreover, GitLab customers can use Bring Your Own Model (BYOM) for Duo Agent Platform so approved providers and gateways land where your security model expects them. GitLab’s [18.9 launch coverage of self-hosted Duo Agent Platform and BYOM](https://about.gitlab.com/blog/agentic-ai-enterprise-control-self-hosted-duo-agent-platform-and-byom/) describes how that wiring works. With this deployment option, customers gain access to a wider set of model options they can tailor to their software development process: the right model for the right workflow, with the right guardrails.\n\nFor GitLab, the decision to build on Vertex AI was driven by the need for enterprise-grade reliability and unparalleled model breadth. Vertex AI and Model Garden completely abstract the heavy lifting of LLM hosting — meaning rapid version delivery, robust security, and strict governance are seamlessly built into the integration. Beyond offering Gemini models, Vertex AI provides global, low-latency access to a vast catalog of third-party and open-source models. \n\nCombined with Google Cloud's industry-leading approach to data privacy and model protection, Vertex AI emerged as the clear choice to power GitLab's next-generation developer experience. \n\nBy integrating Vertex AI Model Garden into its backend, GitLab supercharges its DevSecOps platform without passing any complexity on to users. Development teams are not burdened with evaluating or managing underlying LLMs; instead, they experience a streamlined, AI-assisted workflow for building their applications. \n\nGitLab completely abstracts cloud orchestration, enabling developers to focus entirely on writing great code, while Vertex AI powers the features and functionality that assist them.\n\n## What this means for customers on Google Cloud\n\nGitLab Duo Agent Platform already delivers AI agents that operate across the full software lifecycle within a single, governed system of record. On Google Cloud, it enables rapid innovation as Vertex AI continues to advance the model and infrastructure layers. \n\nFor Google Cloud customers, this integration means streamlined software delivery while maintaining strict enterprise governance. For platform engineering groups, it means normalizing which Vertex-backed models power suggestions, analysis, and remediation inside GitLab instead of cataloging dozens of client-side tools. Security programs benefit when agents propose and validate fixes in the same place developers already triage findings, cutting context switching and reducing work that would otherwise spill into unmanaged channels.\n\nFrom a cloud economics and policy angle, drawing agent inference toward Vertex from within GitLab keeps usage nearer to the agreements and controls you already run on Google Cloud, which helps avoid duplicate spend and shadow paths that bypass procurement.\n\nBecause Vertex AI is an underlying infrastructure provider for GitLab Duo Agent Platform, organizations are enabled to dramatically lift developer productivity without the overhead and risk of managing fragmented AI toolchains. Teams stay aligned within a single, secure system of record, helping them build applications faster and ship with confidence.\n\nThe GitLab and Google Cloud collaboration has been building since 2018. Today, it represents one of the most comprehensive paths for organizations moving from AI experiments to fully governed, agentic software development on Google Cloud. As both platforms continue to advance — GitLab expanding its agent orchestration and developer context, and Vertex AI pushing the boundaries of model capability and agent infrastructure — the value for joint customers will continue to grow.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/free-trial/) to experience the power of GitLab and Vertex AI on Google Cloud.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2026-04-14",[23,280,740,741,28],"google","news",{"featured":12,"template":13,"slug":743},"gitlab-and-vertex-ai-on-google-cloud",{"content":745,"config":754},{"heroImage":746,"title":747,"description":748,"authors":749,"date":751,"category":9,"tags":752,"body":753},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643639/sapu29gmlgtwvhggmj6k.png","Extend GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[750],"Albert Rabassa","2026-03-05",[9,28,24],"Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n * Click **Create** → **OAuth 2.0 integration**\n\n * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n * Navigate to **Permissions** in the left sidebar.\n\n * Add **Jira API** and configure the following scopes:\n\n * `read:jira-work` — Read issues, projects, and boards\n\n * `write:jira-work` — Create and update issues\n\n * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n * Go to **Authorization** in the left sidebar\n\n * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n * Save your changes\n\n5. **Retrieve credentials**\n\n * Navigate to **Settings**\n\n * Copy your **Client ID** and **Client Secret**\n\n * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n \"mcpServers\": {\n \"atlassian\": {\n \"type\": \"http\",\n \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n \"auth\": {\n \"type\": \"oauth2\",\n \"clientId\": \"YOUR_CLIENT_ID\",\n \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n },\n \"approvedTools\": true\n }\n }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":34,"template":13,"slug":755},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"promotions":757},[758,771,782,794],{"id":759,"categories":760,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":247},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[28,41],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":247},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":786,"text":762,"button":787,"image":791},"security-modernization",[785],"security","Are you trading speed for security?",{"text":788,"config":789},"Get your security maturity score",{"href":790,"dataGaName":767,"dataGaLocation":247},"/assessments/security-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":795,"paths":796,"header":799,"text":800,"button":801,"image":806},"github-azure-migration",[797,798],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":802,"config":803},"See how GitLab compares to GitHub",{"href":804,"dataGaName":805,"dataGaLocation":247},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":807},{"src":781},{"header":809,"blurb":810,"button":811,"secondaryButton":816},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":812,"config":813},"Get your free trial",{"href":814,"dataGaName":55,"dataGaLocation":815},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":509,"config":817},{"href":59,"dataGaName":60,"dataGaLocation":815},1777302624972]