[{"data":1,"prerenderedAt":805},["ShallowReactive",2],{"/en-us/blog/streamline-test-management-with-the-smartbear-qmetry-gitlab-component":3,"navigation-en-us":37,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Matt Genelin|Matt Bonner":698,"blog-related-posts-en-us-streamline-test-management-with-the-smartbear-qmetry-gitlab-component":724,"assessment-promotions-en-us":756,"next-steps-en-us":795},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":25,"seo":30,"stem":34,"tagSlugs":35,"__hash__":36},"blogPosts/en-us/blog/streamline-test-management-with-the-smartbear-qmetry-gitlab-component.yml","Streamline Test Management With The Smartbear Qmetry Gitlab Component",[7,8],"matt-genelin","matt-bonner",null,"product",{"featured":12,"template":13,"slug":14},true,"BlogPost","streamline-test-management-with-the-smartbear-qmetry-gitlab-component",{"title":16,"description":17,"heroImage":18,"category":10,"tags":19,"authors":22,"date":25,"body":26},"Streamline test management with the SmartBear QMetry GitLab component","Learn how to automatically upload test results from GitLab CI/CD pipelines to SmartBear QMetry Test Management Enterprise using the CI/CD Catalog component.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1775486753/cswmwtygkgkbdsibo09v.png",[20,10,21],"tutorial","devops",[23,24],"Matt Genelin","Matt Bonner","2026-04-07","In modern software development, test management and continuous integration are two sides of the same coin. DevSecOps teams need seamless integration between their CI/CD pipelines and test management platforms to maintain visibility, traceability, and compliance across the software development lifecycle.\n\nThis becomes even more important as testing scales across automated pipelines, where execution data is spread across tools and harder to track in one place.\n\nFor organizations using GitLab for CI/CD and SmartBear QMetry for test management, manually uploading test results creates friction, delays feedback loops, and makes it harder to maintain a reliable, centralized view of testing.\n\nWhat if you could automatically publish your JUnit, TestNG, or other test results directly from your GitLab pipeline to QMetry with just a few lines of configuration?\n\nThat's exactly what the new **QMetry GitLab Component** enables. This reusable CI/CD component, now available in the [GitLab CI/CD Catalog](https://gitlab.com/explore/catalog), eliminates the manual overhead of test result management by automatically uploading test execution data to QMetry.  This is an AI-enabled, enterprise-grade test management platform that brings together test planning, execution, tracking, and reporting in one place.\n\nAs a centralized system of record for testing, QMetry helps teams understand coverage, track execution, and make more reliable release decisions.\n\nIn this guide, you'll learn:\n\n* How to set up the QMetry GitLab Component in your pipeline  \n* How to configure automated test result uploads  \n* Advanced configuration options for enterprise requirements  \n* A real-world aerospace industry use case  \n* Best practices for test management automation\n\nBy the end of this article, your GitLab pipelines will automatically feed test results into QMetry, giving your QA teams instant visibility into test execution and helping them make faster, more confident release decisions.\n\n![SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/ojt707rzxnm2yr3vqxdh.png)\n\n## Why integrate GitLab with QMetry?\n\nBefore diving into the technical implementation, let's understand the value this integration delivers:\n\n### Eliminate manual test result uploads\n\nDevSecOps engineers and QA teams no longer need to manually export test results from CI/CD runs and import them into test management systems. The component handles this automatically after every pipeline execution.\n\nThis reduces manual effort while ensuring test data stays consistent, up to date, and easy to access across teams.\n\n![Test results with SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/ajx64sihup2nursdpnxz.png)\n\n### Enable end-to-end traceability\n\nBy connecting GitLab's CI/CD execution data with QMetry's test management capabilities, teams gain complete traceability from requirements through test cases to actual test execution results. This is critical for regulated industries like financial services, aerospace, medical devices, and automotive, where audit trails are mandatory and regulatory compliance depends on demonstrating complete test coverage.\n\nIt also gives teams a clearer view of coverage and risk across releases, making it easier to understand what’s been tested and what still needs attention.\n\n### Accelerate feedback loops\n\nAutomated test result uploads mean QA teams, product managers, and stakeholders see test execution results immediately after pipeline completion – no waiting for manual data entry or report generation.\n\nWith faster access to results, teams can act immediately, reduce delays, and make quicker, more informed release decisions.\n\n### Support compliance and audit requirements\n\nFor organizations in regulated industries, maintaining comprehensive test records with proper versioning and traceability is non-negotiable. This integration ensures you can document every test execution properly in QMetry with links back to the specific GitLab pipeline, commit, and build.\n\nThis creates an audit-ready record of testing activity without adding manual overhead.\n\n![Audit-ready record of testing with SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/q2tbaw5otgdywjkcquqx.png)\n\n### Leverage AI-powered test insights\n\nQMetry uses AI to analyze test execution patterns, identify flaky tests, predict test failures, and recommend optimization opportunities. Feeding it real-time data from GitLab pipelines maximizes the value of these AI capabilities.\n\nWith continuous data flowing in, teams get more accurate insights and can focus their efforts where it matters most.\n\n![Accurage insights with SmartBear QMetry GitLab integration](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775488045/pl7ru4wx8ixnheedfyrs.png)\n\n## About the GitLab and SmartBear partnership\n\nThis component represents a growing partnership between GitLab and SmartBear to better connect CI/CD execution with test management in a single workflow. SmartBear brings deep expertise in testing, API management, and quality automation, while GitLab provides the most comprehensive AI-powered DevSecOps platform. Together, they help teams streamline how testing fits into the development lifecycle while maintaining the quality, security, and compliance standards their industries require.\n\nWhether you're managing test execution for aerospace flight control systems, financial services platforms, automotive safety applications, or medical device software, the combination of GitLab's CI/CD capabilities and QMetry's test management gives teams a centralized, reliable view of testing across the lifecycle, helping them track execution, maintain traceability, and make more confident release decisions.\n\n## What you'll need\n\nBefore getting started, ensure you have:\n\n* **A GitLab account** with a project containing automated tests that generate test result files (JUnit XML, TestNG XML, etc.)  \n* **QMetry Test Management Enterprise** account with API access enabled  \n* **QMetry API Key** generated  from your QMetry instance (we'll cover this shortly)  \n* **QMetry Project** already created where you will upload test results   \n* **Familiarity with GitLab CI/CD**, including understanding of basic `.gitlab-ci.yml` syntax and pipeline concepts  \n* **Test suite configuration** in QMetry (optional but recommended for better organization)\n\n### Understanding the test result flow\n\nHere's what happens when you integrate this component:\n\n1. **Test execution**: Your GitLab CI/CD pipeline runs automated tests (unit tests, integration tests, E2E tests, etc.).  \n2. **Result generation**: Tests produce output files in formats like JUnit XML, TestNG XML, or other supported formats.  \n3. **Component invocation**: The QMetry component executes as a job in your pipeline.  \n4. **Automatic upload**: The component reads your test result files and uploads them to QMetry via API.  \n5. **QMetry processing**: QMetry receives the results, processes them, and makes them available for reporting and analysis.\n\nThe beauty of this integration is that it happens automatically, with no manual intervention required once configured.\n\n## Part 1: Getting your QMetry API credentials\n\nBefore configuring the GitLab component, you need to obtain API access credentials from your QMetry instance. Here are the steps to follow:\n\n### 1. Access QMetry settings\n\n1. Log in to your **QMetry Test Management Enterprise** instance.  \n2. Navigate to your **user profile** (typically in the top-right corner).  \n3. Select **Settings** or **API Access** from the dropdown menu.\n\n### 2. Generate an API key\n\n1. In the API Access section, click **Generate New API Key.**  \n2. Provide a descriptive **name** for the key (e.g., \"GitLab CI/CD Integration\").  \n3. Set appropriate **permissions**. The key needs write access to upload test results.  \n4. Click **Generate.**  \n5. **Copy the API key immediately** as it will only be displayed once.\n\n**Important security note**: Treat your API key like a password. Never commit it directly to your `.gitlab-ci.yml` file or store it in plain text. We'll use GitLab CI/CD variables to store it securely.\n\n### 3. Note your QMetry instance URL\n\nYou'll also need your QMetry instance URL, which typically follows this format:\n\n```text\nhttps://your-company.qmetry.com\n```\n\nor, for self-hosted instances:\n\n```text\nhttps://qmetry.your-company.com\n```\n\nMake note of this URL because you'll need it in the next section.\n\n## Part 2: Configuring GitLab CI/CD variables\n\nNow that you have your QMetry credentials, let's store them securely in GitLab. Here are the next steps to follow:\n\n### 4. Navigate to CI/CD settings\n\n1. Open your **GitLab project.**  \n2. In the left sidebar, navigate to **Settings > CI/CD.**  \n3. Expand the **Variables** section.  \n4. Click **Add variable.**\n\n### 5. Add the QMetry API key\n\nConfigure the API key variable:\n\n| Field | Value |\n| ----- | ----- |\n| **Key** | `QMETRY_API_KEY` |\n| **Value** | Your QMetry API key from Step 2 |\n| **Type** | Variable |\n| **Flags** | ✅ Mask variable\u003Cbr>✅ Protect variable (recommended) |\n\nClick **Add variable** to save.\n\n### 6. Add the QMetry instance URL\n\nAdd a second variable for your instance URL:\n\n| Field | Value |\n| ----- | ----- |\n| **Key** | `INSTANCE_URL` |\n| **Value** | Your QMetry instance URL (e.g., `https://your-company.qmetry.com`) |\n| **Type** | Variable |\n| **Flags** | (optional: Protect variable) |\n\nClick **Add variable** to save.\n\n**Why use CI/CD variables?**\n\n* **Security**: Masked variables are hidden in job logs.  \n* **Reusability**: You can use the same credentials across multiple pipelines.  \n* **Flexibility**: It is easy to rotate credentials without modifying pipeline code.  \n* **Access control**: Protected variables are only available on protected branches.\n\n## Part 3: Understanding your test result files\n\nBefore integrating the component, ensure your tests generate output files that QMetry can process. Here are the next steps to follow:\n\n### 7. Verify test output format\n\nThe QMetry component supports multiple test result formats. The most common is **JUnit XML**, which most testing frameworks can generate:\n\n**Example JUnit XML output** (`results.xml`):\n\n```xml\n\u003C?xml version=\"1.0\" encoding=\"UTF-8\"?>\n\u003Ctestsuites>\n  \u003Ctestsuite name=\"Flight Control System Tests\" tests=\"15\" failures=\"1\" errors=\"0\" time=\"45.231\">\n    \u003Ctestcase classname=\"FlightControlTests\" name=\"testAltitudeHold\" time=\"2.341\">\n      \u003Csystem-out>Altitude hold engaged at 10,000 feet\u003C/system-out>\n    \u003C/testcase>\n    \u003Ctestcase classname=\"FlightControlTests\" name=\"testAutopilotEngagement\" time=\"3.125\">\n      \u003Csystem-out>Autopilot engaged successfully\u003C/system-out>\n    \u003C/testcase>\n    \u003Ctestcase classname=\"FlightControlTests\" name=\"testEmergencyLanding\" time=\"5.892\">\n      \u003Cfailure message=\"Landing gear failed to deploy\">\n        Expected: Landing gear deployed\n        Actual: Landing gear malfunction detected\n      \u003C/failure>\n    \u003C/testcase>\n    \u003C!-- Additional test cases... -->\n  \u003C/testsuite>\n\u003C/testsuites>\n```\n\nMost testing frameworks generate this format automatically:\n\n* **JUnit** (Java): Native format  \n* **pytest** (Python): Use `--junitxml=results.xml` flag  \n* **Jest** (JavaScript): Use `jest-junit` reporter  \n* **RSpec** (Ruby): Use `rspec_junit_formatter`  \n* **NUnit** (.NET): Use `nunit-console` with XML output  \n* **Go test**: Use `go-junit-report`\n\n### 8. Confirm test artifact configuration\n\nEnsure your existing pipeline saves test results as **artifacts**. This allows the QMetry component to access them:\n\n```yaml\ntest:\n  stage: test\n  script:\n    - npm install\n    - npm test -- --reporter=junit --reporter-options=output=results.xml\n  artifacts:\n    reports:\n      junit: results.xml\n    paths:\n      - results.xml\n    when: always  # Upload even if tests fail\n```\n\n**Key points**:\n\n* `artifacts.reports.junit` makes results visible in GitLab's test report UI.  \n* `artifacts.paths` ensures the file is available to downstream jobs.  \n* `when: always` ensures results upload even if tests fail.\n\n## Part 4: Integrating the QMetry component\n\nNow for the main event – adding the QMetry component to your pipeline. Here are the next steps to follow:\n\n### 9. Basic component integration\n\nAdd the component to your `.gitlab-ci.yml` file. The component should run **after** your tests complete:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\nLet's break down each input parameter:\n\n| Parameter | Description | Example |\n| ----- | ----- | ----- |\n| `stage` | Which CI/CD stage runs the upload job | `test` |\n| `project` | Your QMetry project name or key | `\"Aerospace Flight Control System\"` |\n| `file_name` | Path to your test results file | `\"results.xml\"` |\n| `testing_type` | Format of your test results | `\"JUNIT\"` (also supports: `TESTNG`, `NUNIT`, etc.) |\n| `instance_url` | Your QMetry instance URL | `${INSTANCE_URL}` (from CI/CD variables) |\n| `api_key` | QMetry API key for authentication | `${QMETRY_API_KEY}` (from CI/CD variables) |\n\n### 10. Complete pipeline example\n\nHere's a complete `.gitlab-ci.yml` example showing test execution followed by QMetry upload:\n\n```yaml\nstages:\n  - test\n  - report\n\nvariables:\n  # Your app-specific variables\n  NODE_VERSION: \"18\"\n\n# Run your automated tests\nunit-tests:\n  stage: test\n  image: node:${NODE_VERSION}\n  script:\n    - npm ci\n    - npm run test:unit -- --reporter=junit --reporter-options=output=results.xml\n  artifacts:\n    reports:\n      junit: results.xml\n    paths:\n      - results.xml\n    when: always\n  tags:\n    - docker\n\n# Upload results to QMetry\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test  # Runs in same stage as tests\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### 11. Run your pipeline\n\nCommit and push your changes:\n\n```shell\ngit add .gitlab-ci.yml\ngit commit -m \"Add QMetry test result integration\"\ngit push origin main\n```\n\nNavigate to your GitLab project's **CI/CD > Pipelines** to watch the execution.\n\n### 12. Verify successful upload\n\nAfter the pipeline completes, you should see:\n\n**In GitLab**:\n\n1. A new job in your pipeline named `qmetry-import` (or similar)  \n2. Job logs showing successful API communication  \n3. Green checkmark indicating successful upload\n\n**Example successful job log**:\n\n```json\n$ curl -X POST https://your-company.qmetry.com/api/v3/test-results/import \\\n  -H \"Authorization: Bearer ${QMETRY_API_KEY}\" \\\n  -H \"Content-Type: application/json\" \\\n  -d @payload.json\n\n{\n  \"status\": \"success\",\n  \"message\": \"Test results uploaded successfully\",\n  \"results_processed\": 15,\n  \"test_cases_created\": 3,\n  \"test_cases_updated\": 12,\n  \"execution_id\": \"EXE-12345\"\n}\n\nJob succeeded ```\n\n**In QMetry**:\n\n1. Navigate to your project dashboard.  \n2. Check the **Test Executions** section.  \n3. You should see a new test execution with results from your GitLab pipeline.  \n4. Click into the execution to see detailed test case results.\n\n\n## Part 5: Advanced configuration options\n\nNow that you have the basic integration working, let's explore advanced configuration for enterprise requirements. Here are the next steps to follow:\n\n### 13. Organizing results with test suites\n\nFor better organization, you can specify which QMetry test suite should receive results:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Sprint 23 Regression Tests\"\n      testsuite_id: \"TS-456\"  # Optional: Use existing test suite ID\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n**When to use test suites**:\n\n* Organizing tests by sprint or release  \n* Separating regression tests from new feature tests  \n* Grouping tests by component or subsystem  \n* Creating test execution hierarchies for reporting\n\n### 14. Configuring automation hierarchy levels\n\nQMetry supports hierarchical test organization. Use the `automation_hierarchy` parameter to specify the organization level:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      automation_hierarchy: \"2\"  # Level 2 hierarchy\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n**Hierarchy levels explained**:\n\n* **Level 1**: Top-level test suites (e.g., \"All Regression Tests\")  \n* **Level 2**: Sub-suites (e.g., \"Flight Control Tests\" under \"Regression Tests\")  \n* **Level 3**: Granular test groups (e.g., \"Altitude Hold Tests\" under \"Flight Control\")\n\n### 15. Multiple test result files\n\nFor complex projects with multiple test jobs, you can invoke the component multiple times:\n\n```yaml\nstages:\n  - test\n\n# Unit tests\nunit-tests:\n  stage: test\n  script:\n    - npm run test:unit\n  artifacts:\n    paths:\n      - unit-results.xml\n    when: always\n\n# Integration tests\nintegration-tests:\n  stage: test\n  script:\n    - npm run test:integration\n  artifacts:\n    paths:\n      - integration-results.xml\n    when: always\n\n# Upload unit test results\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"unit-results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Unit Tests - Sprint 23\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n\n  # Upload integration test results\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"integration-results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Integration Tests - Sprint 23\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### 16. Custom runner tags\n\nFor enterprise environments with dedicated runners, specify runner tags:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      runner_tag: \"production-runners\"  # Use specific runner pool\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### 17. Custom test suite folders\n\nOrganize test suites into folders for better project structure:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_folder_path: \"/Regression/Sprint-23/Flight-Controls\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\nThis creates a folder hierarchy in QMetry:\n\n```none\nAerospace Flight Control System/\n└── Regression/\n    └── Sprint-23/\n        └── Flight-Controls/\n            └── [Your test execution]\n```\n\n### 18. Advanced field mapping\n\nFor enterprise QMetry instances with custom fields, use the `testcase_fields` and `testsuite_fields` parameters:\n\n```yaml\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: test\n      project: \"Aerospace Flight Control System\"\n      file_name: \"results.xml\"\n      testing_type: \"JUNIT\"\n      testcase_fields: \"priority=P1,component=FlightControl,certification=DO-178C\"\n      testsuite_fields: \"release=v2.4.0,sprint=23\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\nThis adds custom metadata to test cases and suites for enhanced filtering and reporting.\n\n## Part 6: Real-world use cases\n\nLet's explore how organizations across different industries are using this integration to solve critical quality and compliance challenges.\n\n### Financial services: Enterprise banking platforms\n\nLeading financial institutions are evolving their engineering practices with integrated DevOps platforms. These organizations face unique challenges when managing test automation at scale.\n\n**The challenge for financial services**:\n\n* **Regulatory compliance**: Financial services must maintain detailed audit trails for all testing activities.  \n* **Multiple compliance frameworks**: Firms must adhere to FCA, PSD2, GDPR, and internal risk management policies.  \n* **High-frequency deployments**: Multiple production deployments are required daily across microservices.  \n* **Zero-tolerance for failures**: Banking systems require extremely high reliability.  \n* **Distributed teams**: QA teams need real-time visibility across global engineering teams.\n\n**The solution**: Financial services organizations implementing the QMetry GitLab Component can automate test result uploads across their CI/CD pipelines for:\n\n* Unit tests for hundreds of microservices  \n* API contract tests for inter-service communication  \n* End-to-end transaction flow tests  \n* Security and compliance scanning results  \n* Performance and load testing results\n\n**Example implementation approach**:\n\n```yaml\n# Financial services approach: Separate test uploads by test type\nstages:\n  - test\n  - security\n  - report\n\n# Unit tests for payment processing service\nunit-tests:\n  stage: test\n  script:\n    - mvn clean test\n  artifacts:\n    paths:\n      - target/surefire-reports/TEST-*.xml\n    when: always\n\n# Upload to QMetry with compliance metadata\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Payment Processing Platform\"\n      file_name: \"target/surefire-reports/TEST-*.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Payment Services - Unit Tests\"\n      testsuite_folder_path: \"/Regulatory/FCA-Compliance/Unit-Tests\"\n      testcase_fields: \"compliance=FCA,risk_level=high,service=payments\"\n      automation_hierarchy: \"2\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n**Potential business outcomes for financial services**:\n\n* **Significant reduction** in manual test reporting time  \n* **Complete audit trail coverage** for regulatory reviews  \n* **Real-time visibility** for distributed QA teams  \n* **Faster time-to-production** with automated quality gates  \n* **Enhanced compliance posture** with complete traceability from requirements to test execution\n\n### Aerospace flight control testing\n\nLet's explore how an aerospace company might use this integration for critical flight control system testing.\n\n**Aerospace software development faces unique requirements and challenges:**\n\n* **DO-178C compliance**: Aviation software must follow strict certification standards  \n* **Complete traceability**: Every requirement must link to test cases and execution results  \n* **Audit trails**: Regulators require detailed records of all testing activities  \n* **Safety-critical quality**: Failures can have catastrophic consequences  \n* **Multiple test levels**: Unit, integration, system, and certification tests\n\n**The solution:** By integrating GitLab CI/CD with QMetry, the aerospace engineering team achieves automated test execution and reporting.\n\n\n```yaml\nstages:\n  - build\n  - unit-test\n  - integration-test\n  - system-test\n  - report\n\n# Build flight control firmware\nbuild-firmware:\n  stage: build\n  script:\n    - make clean\n    - make build TARGET=flight-control\n  artifacts:\n    paths:\n      - build/flight-control.bin\n\n# Unit tests (DO-178C Level A)\nunit-tests:\n  stage: unit-test\n  script:\n    - make test-unit OUTPUT=junit\n  artifacts:\n    paths:\n      - test-results/unit-tests.xml\n    when: always\n\n# Hardware-in-the-loop integration tests\nhil-integration-tests:\n  stage: integration-test\n  tags:\n    - hil-test-bench  # Dedicated hardware test environment\n  script:\n    - ./scripts/deploy-to-test-bench.sh\n    - ./scripts/run-hil-tests.sh\n  artifacts:\n    paths:\n      - test-results/hil-tests.xml\n    when: always\n\n# System-level certification tests\ncertification-tests:\n  stage: system-test\n  tags:\n    - certification-environment\n  script:\n    - ./scripts/run-certification-suite.sh\n  artifacts:\n    paths:\n      - test-results/certification-tests.xml\n    when: always\n  only:\n    - main  # Only run on main branch\n\n# Upload unit test results to QMetry\ninclude:\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Flight Control System v2.4\"\n      file_name: \"test-results/unit-tests.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Unit Tests - DO-178C Level A\"\n      testsuite_folder_path: \"/Certification/DO-178C/Unit\"\n      testcase_fields: \"compliance=DO-178C,level=A,safety_critical=true\"\n      automation_hierarchy: \"2\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n\n  # Upload HIL test results\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Flight Control System v2.4\"\n      file_name: \"test-results/hil-tests.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"Hardware-in-Loop Integration Tests\"\n      testsuite_folder_path: \"/Certification/DO-178C/Integration\"\n      testcase_fields: \"compliance=DO-178C,level=A,test_type=HIL\"\n      automation_hierarchy: \"2\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n\n  # Upload certification test results\n  - component: gitlab.com/sb9945614/qtm-gitlab-component/qmetry-import@1.0.5\n    inputs:\n      stage: report\n      project: \"Flight Control System v2.4\"\n      file_name: \"test-results/certification-tests.xml\"\n      testing_type: \"JUNIT\"\n      testsuite_name: \"System Certification Tests\"\n      testsuite_folder_path: \"/Certification/DO-178C/System\"\n      testcase_fields: \"compliance=DO-178C,level=A,certification_ready=true\"\n      automation_hierarchy: \"1\"\n      instance_url: ${INSTANCE_URL}\n      api_key: ${QMETRY_API_KEY}\n```\n\n### The results\n\n**Before integration**:\n\n* QA engineers manually exported test results from GitLab  \n* Imported results into QMetry through UI uploads  \n* Process took 2-3 hours per test cycle  \n* Human error risk in data entry  \n* Delayed feedback to stakeholders\n\n**After integration**:\n\n* Test results automatically flow from GitLab to QMetry  \n* Complete audit trail from commit → test → result  \n* Zero manual intervention required  \n* Real-time visibility for certification auditors  \n* Compliance reports generated automatically\n\n**Example QMetry dashboard after integration**:\n\n```none\n╔════════════════════════════════════════════════════════════╗\n║  Flight Control System v2.4 - Test Execution Dashboard     ║\n╠════════════════════════════════════════════════════════════╣\n║                                                            ║\n║  📊 Test Execution Summary (Last 7 Days)                   ║\n║  ───────────────────────────────────────────────────────── ║\n║  ✓ Total Tests Executed: 1,247                             ║\n║  ✓ Passed: 1,241 (99.5%)                                   ║\n║  ✗ Failed: 6 (0.5%)                                        ║\n║  ⏸ Skipped: 0                                              ║\n║                                                            ║\n║  📁 Test Suite Organization                                ║\n║  ───────────────────────────────────────────────────────── ║\n║  └─ Certification/                                         ║\n║     └─ DO-178C/                                            ║\n║        ├─ Unit/ (487 tests, 100% pass)                     ║\n║        ├─ Integration/ (623 tests, 99.2% pass)             ║\n║        └─ System/ (137 tests, 100% pass)                   ║\n║                                                            ║\n║  🔗 Traceability                                           ║\n║  ───────────────────────────────────────────────────────── ║\n║  Requirements Covered: 342/342 (100%)                      ║\n║  Test Cases Linked: 1,247/1,247 (100%)                     ║\n║  GitLab Pipeline Executions: 47 (automated)                ║\n║                                                            ║\n║  ⚠️  Action Items                                          ║\n║  ───────────────────────────────────────────────────────── ║\n║  • 6 failed tests require investigation                    ║\n║  • Last execution: 2 minutes ago (Pipeline #1543)          ║\n║  • GitLab Commit: a7f8c23 \"Fix altitude hold logic\"        ║\n║                                                            ║\n╚════════════════════════════════════════════════════════════╝\n```\n\n### Compliance and audit benefits\n\nBoth financial services and aerospace organizations can leverage this integration for compliance:\n\n**For financial services (FCA, PSD2, SOX)**:\n\n1. **Automated traceability**: Link regulatory requirements → test cases → execution results → GitLab commits  \n2. **Audit-ready documentation**: Complete test execution history with timestamps and pipeline references  \n3. **Risk management**: Real-time quality dashboards for risk assessment  \n4. **Regulatory reporting**: Generate compliance reports directly from QMetry test data\n\n**For aerospace certification (DO-178C, DO-254)**:\n\n1. **Automated traceability matrix**: QMetry links requirements → test cases → execution results → GitLab commits  \n2. **Immutable audit trail**: Every test execution is timestamped with pipeline ID, commit SHA, and executor  \n3. **Certification package generation**: QMetry generates compliant documentation pulling data from GitLab pipelines  \n4. **Real-time compliance dashboards**: Auditors can view test coverage and execution history in real-time\n\n## Complete configuration reference\n\nHere's a comprehensive reference of all available component inputs:\n\n| Input Parameter | Required | Default | Description |\n| ----- | ----- | ----- | ----- |\n| `stage` | No | `test` | GitLab CI/CD stage for the upload job |\n| `runner_tag` | No | `\"\"` | Specific runner tag to use (empty = any available runner) |\n| `project` | Yes | - | QMetry project name or key |\n| `file_name` | Yes | - | Path to test results file (relative to project root) |\n| `testing_type` | Yes | - | Test result format: `JUNIT`, `TESTNG`, `NUNIT`, etc. |\n| `skip_warning` | No | `\"1\"` | Skip warnings during import (`\"1\"` = skip, `\"0\"` = show) |\n| `is_matching_required` | No | `\"false\"` | Match existing test cases by name (`\"true\"` or `\"false\"`) |\n| `testsuite_name` | No | `\"\"` | Name for the test suite in QMetry |\n| `testsuite_id` | No | `\"\"` | Existing test suite ID to append results to |\n| `testsuite_folder_path` | No | `\"\"` | Folder path for organizing test suites (e.g., `/Regression/Sprint-23`) |\n| `automation_hierarchy` | No | `\"\"` | Hierarchy level for test organization (`\"1\"`, `\"2\"`, `\"3\"`, etc.) |\n| `testcase_fields` | No | `\"\"` | Custom fields for test cases (comma-separated: `field1=value1,field2=value2`) |\n| `testsuite_fields` | No | `\"\"` | Custom fields for test suites (comma-separated: `field1=value1,field2=value2`) |\n| `instance_url` | Yes | - | QMetry instance URL (store in CI/CD variable) |\n| `api_key` | Yes | - | QMetry API key (store in CI/CD variable, masked) |\n\n## Best practices for production use\n\nAs you scale your integration, follow these best practices:\n\n### Security\n\n* ✅ **Always use CI/CD variables** for sensitive data (API keys, URLs)  \n* ✅ **Mask and protect** API key variables  \n* ✅ **Rotate API keys** periodically (quarterly recommended)  \n* ✅ **Restrict API key permissions** to minimum required (write to test results only)  \n* ✅ **Use protected branches** for production test uploads\n\n### Performance\n\n* ✅ **Keep test result files reasonable size** (\\\u003C 10 MB recommended)  \n* ✅ **Split large test suites** into multiple jobs/files  \n* ✅ **Use parallel test execution** to reduce pipeline duration  \n* ✅ **Cache dependencies** to speed up test execution\n\n### Organization\n\n* ✅ **Use consistent naming conventions** for test suites and folder paths  \n* ✅ **Leverage custom fields** for filtering and reporting  \n* ✅ **Create folder hierarchies** that mirror your test strategy  \n* ✅ **Document your integration** in project README files\n\n### Troubleshooting\n\n* ✅ **Review job logs** for API communication details  \n* ✅ **Verify test result file format** matches `testing_type` parameter  \n* ✅ **Check QMetry project exists** and API key has access  \n* ✅ **Ensure test result files** are available as pipeline artifacts\n\n## Summary and next steps\n\nCongratulations! You've successfully integrated GitLab CI/CD with QMetry Test Management Enterprise. Your setup now provides:\n\n* **Automated test result uploads** – No more manual exports and imports \n\n* **Real-time visibility** – QA teams see results immediately after pipeline execution \n\n* **Complete traceability** – Link GitLab commits, pipelines, and test executions \n\n* **Enhanced compliance** – Maintain audit trails for regulated industries \n\n* **Scalable quality processes** – Support growing test suites without added overhead\n\n### What happens now\n\nEvery time your GitLab pipeline runs:\n\n1. Tests execute and generate result files.  \n2. The QMetry component automatically uploads results to your instance.  \n3. QA teams, stakeholders, and auditors see results in QMetry dashboards.  \n4. AI-powered insights analyze execution patterns and identify improvements.  \n5. Compliance reports generate automatically with full traceability.\n\n### Expand your integration\n\nNow that you have the basic integration working, consider these advanced scenarios:\n\n* **Bi-directional integration**: Use QMetry's API to trigger GitLab pipelines from test management workflows.\n\n* **Multi-project deployments**: Scale the component across your organization's GitLab projects.\n\n* **Custom reporting**: Build dashboards combining GitLab pipeline metrics with QMetry test analytics.\n\n* **Scheduled test execution**: Use GitLab scheduled pipelines to run regression suites nightly.\n\n## Learn more and get help\n\n### Documentation and resources\n\n* **Component documentation**: [GitLab CI/CD Catalog](https://gitlab.com/explore/catalog)  \n* **QMetry documentation**: [QMetry Support Portal](https://qmetrysupport.atlassian.net/wiki/spaces/QPro/overview)  \n* **SmartBear resources**: [SmartBear Academy](https://smartbear.com/resources/)  \n* **GitLab CI/CD documentation**: [GitLab CI/CD Documentation](https://docs.gitlab.com/ee/ci/)\n\n### Support\n\n**For component technical questions**:\n\n* Visit the [component repository](https://gitlab.com/sb9945614/qtm-gitlab-component).  \n* Open an issue on the project.  \n* Check existing issues for common questions.\n\n**For QMetry product questions**:\n\n* Contact SmartBear support at support@smartbear.com.  \n* Visit the [QMetry Community Forum](https://community.smartbear.com/).","yml",{},"/en-us/blog/streamline-test-management-with-the-smartbear-qmetry-gitlab-component",{"config":31,"title":33},{"noIndex":32},false,"Streamline test management with SmartBear QMetry GitLab component","en-us/blog/streamline-test-management-with-the-smartbear-qmetry-gitlab-component",[20,10,21],"HuM0otR-QuGWQ5StzMxVmE3C-nhTAhnTyhlqISmJsjs",{"data":38},{"logo":39,"freeTrial":44,"sales":49,"login":54,"items":59,"search":367,"minimal":398,"duo":417,"switchNav":426,"pricingDeployment":437},{"config":40},{"href":41,"dataGaName":42,"dataGaLocation":43},"/","gitlab logo","header",{"text":45,"config":46},"Get free trial",{"href":47,"dataGaName":48,"dataGaLocation":43},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":50,"config":51},"Talk to sales",{"href":52,"dataGaName":53,"dataGaLocation":43},"/sales/","sales",{"text":55,"config":56},"Sign in",{"href":57,"dataGaName":58,"dataGaLocation":43},"https://gitlab.com/users/sign_in/","sign in",[60,87,182,187,288,348],{"text":61,"config":62,"cards":64},"Platform",{"dataNavLevelOne":63},"platform",[65,71,79],{"title":61,"description":66,"link":67},"The intelligent orchestration platform for DevSecOps",{"text":68,"config":69},"Explore our Platform",{"href":70,"dataGaName":63,"dataGaLocation":43},"/platform/",{"title":72,"description":73,"link":74},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":75,"config":76},"Meet GitLab Duo",{"href":77,"dataGaName":78,"dataGaLocation":43},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":80,"description":81,"link":82},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":83,"config":84},"Learn more",{"href":85,"dataGaName":86,"dataGaLocation":43},"/why-gitlab/","why gitlab",{"text":88,"left":12,"config":89,"link":91,"lists":95,"footer":164},"Product",{"dataNavLevelOne":90},"solutions",{"text":92,"config":93},"View all Solutions",{"href":94,"dataGaName":90,"dataGaLocation":43},"/solutions/",[96,120,143],{"title":97,"description":98,"link":99,"items":104},"Automation","CI/CD and automation to accelerate deployment",{"config":100},{"icon":101,"href":102,"dataGaName":103,"dataGaLocation":43},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[105,109,112,116],{"text":106,"config":107},"CI/CD",{"href":108,"dataGaLocation":43,"dataGaName":106},"/solutions/continuous-integration/",{"text":72,"config":110},{"href":77,"dataGaLocation":43,"dataGaName":111},"gitlab duo agent platform - product menu",{"text":113,"config":114},"Source Code Management",{"href":115,"dataGaLocation":43,"dataGaName":113},"/solutions/source-code-management/",{"text":117,"config":118},"Automated Software Delivery",{"href":102,"dataGaLocation":43,"dataGaName":119},"Automated software delivery",{"title":121,"description":122,"link":123,"items":128},"Security","Deliver code faster without compromising security",{"config":124},{"href":125,"dataGaName":126,"dataGaLocation":43,"icon":127},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[129,133,138],{"text":130,"config":131},"Application Security Testing",{"href":125,"dataGaName":132,"dataGaLocation":43},"Application security testing",{"text":134,"config":135},"Software Supply Chain Security",{"href":136,"dataGaLocation":43,"dataGaName":137},"/solutions/supply-chain/","Software supply chain security",{"text":139,"config":140},"Software Compliance",{"href":141,"dataGaName":142,"dataGaLocation":43},"/solutions/software-compliance/","software compliance",{"title":144,"link":145,"items":150},"Measurement",{"config":146},{"icon":147,"href":148,"dataGaName":149,"dataGaLocation":43},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[151,155,159],{"text":152,"config":153},"Visibility & Measurement",{"href":148,"dataGaLocation":43,"dataGaName":154},"Visibility and Measurement",{"text":156,"config":157},"Value Stream Management",{"href":158,"dataGaLocation":43,"dataGaName":156},"/solutions/value-stream-management/",{"text":160,"config":161},"Analytics & Insights",{"href":162,"dataGaLocation":43,"dataGaName":163},"/solutions/analytics-and-insights/","Analytics and insights",{"title":165,"items":166},"GitLab for",[167,172,177],{"text":168,"config":169},"Enterprise",{"href":170,"dataGaLocation":43,"dataGaName":171},"/enterprise/","enterprise",{"text":173,"config":174},"Small Business",{"href":175,"dataGaLocation":43,"dataGaName":176},"/small-business/","small business",{"text":178,"config":179},"Public Sector",{"href":180,"dataGaLocation":43,"dataGaName":181},"/solutions/public-sector/","public sector",{"text":183,"config":184},"Pricing",{"href":185,"dataGaName":186,"dataGaLocation":43,"dataNavLevelOne":186},"/pricing/","pricing",{"text":188,"config":189,"link":191,"lists":195,"feature":275},"Resources",{"dataNavLevelOne":190},"resources",{"text":192,"config":193},"View all resources",{"href":194,"dataGaName":190,"dataGaLocation":43},"/resources/",[196,229,247],{"title":197,"items":198},"Getting started",[199,204,209,214,219,224],{"text":200,"config":201},"Install",{"href":202,"dataGaName":203,"dataGaLocation":43},"/install/","install",{"text":205,"config":206},"Quick start guides",{"href":207,"dataGaName":208,"dataGaLocation":43},"/get-started/","quick setup checklists",{"text":210,"config":211},"Learn",{"href":212,"dataGaLocation":43,"dataGaName":213},"https://university.gitlab.com/","learn",{"text":215,"config":216},"Product documentation",{"href":217,"dataGaName":218,"dataGaLocation":43},"https://docs.gitlab.com/","product documentation",{"text":220,"config":221},"Best practice videos",{"href":222,"dataGaName":223,"dataGaLocation":43},"/getting-started-videos/","best practice videos",{"text":225,"config":226},"Integrations",{"href":227,"dataGaName":228,"dataGaLocation":43},"/integrations/","integrations",{"title":230,"items":231},"Discover",[232,237,242],{"text":233,"config":234},"Customer success stories",{"href":235,"dataGaName":236,"dataGaLocation":43},"/customers/","customer success stories",{"text":238,"config":239},"Blog",{"href":240,"dataGaName":241,"dataGaLocation":43},"/blog/","blog",{"text":243,"config":244},"Remote",{"href":245,"dataGaName":246,"dataGaLocation":43},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":248,"items":249},"Connect",[250,255,260,265,270],{"text":251,"config":252},"GitLab Services",{"href":253,"dataGaName":254,"dataGaLocation":43},"/services/","services",{"text":256,"config":257},"Community",{"href":258,"dataGaName":259,"dataGaLocation":43},"/community/","community",{"text":261,"config":262},"Forum",{"href":263,"dataGaName":264,"dataGaLocation":43},"https://forum.gitlab.com/","forum",{"text":266,"config":267},"Events",{"href":268,"dataGaName":269,"dataGaLocation":43},"/events/","events",{"text":271,"config":272},"Partners",{"href":273,"dataGaName":274,"dataGaLocation":43},"/partners/","partners",{"backgroundColor":276,"textColor":277,"text":278,"image":279,"link":283},"#2f2a6b","#fff","Insights for the future of software development",{"altText":280,"config":281},"the source promo card",{"src":282},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":284,"config":285},"Read the latest",{"href":286,"dataGaName":287,"dataGaLocation":43},"/the-source/","the source",{"text":289,"config":290,"lists":292},"Company",{"dataNavLevelOne":291},"company",[293],{"items":294},[295,300,306,308,313,318,323,328,333,338,343],{"text":296,"config":297},"About",{"href":298,"dataGaName":299,"dataGaLocation":43},"/company/","about",{"text":301,"config":302,"footerGa":305},"Jobs",{"href":303,"dataGaName":304,"dataGaLocation":43},"/jobs/","jobs",{"dataGaName":304},{"text":266,"config":307},{"href":268,"dataGaName":269,"dataGaLocation":43},{"text":309,"config":310},"Leadership",{"href":311,"dataGaName":312,"dataGaLocation":43},"/company/team/e-group/","leadership",{"text":314,"config":315},"Team",{"href":316,"dataGaName":317,"dataGaLocation":43},"/company/team/","team",{"text":319,"config":320},"Handbook",{"href":321,"dataGaName":322,"dataGaLocation":43},"https://handbook.gitlab.com/","handbook",{"text":324,"config":325},"Investor relations",{"href":326,"dataGaName":327,"dataGaLocation":43},"https://ir.gitlab.com/","investor relations",{"text":329,"config":330},"Trust Center",{"href":331,"dataGaName":332,"dataGaLocation":43},"/security/","trust center",{"text":334,"config":335},"AI Transparency Center",{"href":336,"dataGaName":337,"dataGaLocation":43},"/ai-transparency-center/","ai transparency center",{"text":339,"config":340},"Newsletter",{"href":341,"dataGaName":342,"dataGaLocation":43},"/company/contact/#contact-forms","newsletter",{"text":344,"config":345},"Press",{"href":346,"dataGaName":347,"dataGaLocation":43},"/press/","press",{"text":349,"config":350,"lists":351},"Contact us",{"dataNavLevelOne":291},[352],{"items":353},[354,357,362],{"text":50,"config":355},{"href":52,"dataGaName":356,"dataGaLocation":43},"talk to sales",{"text":358,"config":359},"Support portal",{"href":360,"dataGaName":361,"dataGaLocation":43},"https://support.gitlab.com","support portal",{"text":363,"config":364},"Customer portal",{"href":365,"dataGaName":366,"dataGaLocation":43},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":368,"login":369,"suggestions":376},"Close",{"text":370,"link":371},"To search repositories and projects, login to",{"text":372,"config":373},"gitlab.com",{"href":57,"dataGaName":374,"dataGaLocation":375},"search login","search",{"text":377,"default":378},"Suggestions",[379,381,385,387,391,395],{"text":72,"config":380},{"href":77,"dataGaName":72,"dataGaLocation":375},{"text":382,"config":383},"Code Suggestions (AI)",{"href":384,"dataGaName":382,"dataGaLocation":375},"/solutions/code-suggestions/",{"text":106,"config":386},{"href":108,"dataGaName":106,"dataGaLocation":375},{"text":388,"config":389},"GitLab on AWS",{"href":390,"dataGaName":388,"dataGaLocation":375},"/partners/technology-partners/aws/",{"text":392,"config":393},"GitLab on Google Cloud",{"href":394,"dataGaName":392,"dataGaLocation":375},"/partners/technology-partners/google-cloud-platform/",{"text":396,"config":397},"Why GitLab?",{"href":85,"dataGaName":396,"dataGaLocation":375},{"freeTrial":399,"mobileIcon":404,"desktopIcon":409,"secondaryButton":412},{"text":400,"config":401},"Start free trial",{"href":402,"dataGaName":48,"dataGaLocation":403},"https://gitlab.com/-/trials/new/","nav",{"altText":405,"config":406},"Gitlab Icon",{"src":407,"dataGaName":408,"dataGaLocation":403},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":405,"config":410},{"src":411,"dataGaName":408,"dataGaLocation":403},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":413,"config":414},"Get Started",{"href":415,"dataGaName":416,"dataGaLocation":403},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/get-started/","get started",{"freeTrial":418,"mobileIcon":422,"desktopIcon":424},{"text":419,"config":420},"Learn more about GitLab Duo",{"href":77,"dataGaName":421,"dataGaLocation":403},"gitlab duo",{"altText":405,"config":423},{"src":407,"dataGaName":408,"dataGaLocation":403},{"altText":405,"config":425},{"src":411,"dataGaName":408,"dataGaLocation":403},{"button":427,"mobileIcon":432,"desktopIcon":434},{"text":428,"config":429},"/switch",{"href":430,"dataGaName":431,"dataGaLocation":403},"#contact","switch",{"altText":405,"config":433},{"src":407,"dataGaName":408,"dataGaLocation":403},{"altText":405,"config":435},{"src":436,"dataGaName":408,"dataGaLocation":403},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1773335277/ohhpiuoxoldryzrnhfrh.png",{"freeTrial":438,"mobileIcon":443,"desktopIcon":445},{"text":439,"config":440},"Back to pricing",{"href":185,"dataGaName":441,"dataGaLocation":403,"icon":442},"back to pricing","GoBack",{"altText":405,"config":444},{"src":407,"dataGaName":408,"dataGaLocation":403},{"altText":405,"config":446},{"src":411,"dataGaName":408,"dataGaLocation":403},{"title":448,"button":449,"config":454},"See how agentic AI transforms software delivery",{"text":450,"config":451},"Watch GitLab Transcend now",{"href":452,"dataGaName":453,"dataGaLocation":43},"/events/transcend/virtual/","transcend event",{"layout":455,"icon":456,"disabled":12},"release","AiStar",{"data":458},{"text":459,"source":460,"edit":466,"contribute":471,"config":476,"items":481,"minimal":687},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":461,"config":462},"View page source",{"href":463,"dataGaName":464,"dataGaLocation":465},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":467,"config":468},"Edit this page",{"href":469,"dataGaName":470,"dataGaLocation":465},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":472,"config":473},"Please contribute",{"href":474,"dataGaName":475,"dataGaLocation":465},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":477,"facebook":478,"youtube":479,"linkedin":480},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[482,529,582,626,653],{"title":183,"links":483,"subMenu":498},[484,488,493],{"text":485,"config":486},"View plans",{"href":185,"dataGaName":487,"dataGaLocation":465},"view plans",{"text":489,"config":490},"Why Premium?",{"href":491,"dataGaName":492,"dataGaLocation":465},"/pricing/premium/","why premium",{"text":494,"config":495},"Why Ultimate?",{"href":496,"dataGaName":497,"dataGaLocation":465},"/pricing/ultimate/","why ultimate",[499],{"title":500,"links":501},"Contact Us",[502,505,507,509,514,519,524],{"text":503,"config":504},"Contact sales",{"href":52,"dataGaName":53,"dataGaLocation":465},{"text":358,"config":506},{"href":360,"dataGaName":361,"dataGaLocation":465},{"text":363,"config":508},{"href":365,"dataGaName":366,"dataGaLocation":465},{"text":510,"config":511},"Status",{"href":512,"dataGaName":513,"dataGaLocation":465},"https://status.gitlab.com/","status",{"text":515,"config":516},"Terms of use",{"href":517,"dataGaName":518,"dataGaLocation":465},"/terms/","terms of use",{"text":520,"config":521},"Privacy statement",{"href":522,"dataGaName":523,"dataGaLocation":465},"/privacy/","privacy statement",{"text":525,"config":526},"Cookie preferences",{"dataGaName":527,"dataGaLocation":465,"id":528,"isOneTrustButton":12},"cookie preferences","ot-sdk-btn",{"title":88,"links":530,"subMenu":539},[531,535],{"text":532,"config":533},"DevSecOps platform",{"href":70,"dataGaName":534,"dataGaLocation":465},"devsecops platform",{"text":536,"config":537},"AI-Assisted Development",{"href":77,"dataGaName":538,"dataGaLocation":465},"ai-assisted development",[540],{"title":541,"links":542},"Topics",[543,548,553,557,562,567,572,577],{"text":544,"config":545},"CICD",{"href":546,"dataGaName":547,"dataGaLocation":465},"/topics/ci-cd/","cicd",{"text":549,"config":550},"GitOps",{"href":551,"dataGaName":552,"dataGaLocation":465},"/topics/gitops/","gitops",{"text":554,"config":555},"DevOps",{"href":556,"dataGaName":21,"dataGaLocation":465},"/topics/devops/",{"text":558,"config":559},"Version Control",{"href":560,"dataGaName":561,"dataGaLocation":465},"/topics/version-control/","version control",{"text":563,"config":564},"DevSecOps",{"href":565,"dataGaName":566,"dataGaLocation":465},"/topics/devsecops/","devsecops",{"text":568,"config":569},"Cloud Native",{"href":570,"dataGaName":571,"dataGaLocation":465},"/topics/cloud-native/","cloud native",{"text":573,"config":574},"AI for Coding",{"href":575,"dataGaName":576,"dataGaLocation":465},"/topics/devops/ai-for-coding/","ai for coding",{"text":578,"config":579},"Agentic AI",{"href":580,"dataGaName":581,"dataGaLocation":465},"/topics/agentic-ai/","agentic ai",{"title":583,"links":584},"Solutions",[585,587,589,594,598,601,605,608,610,613,616,621],{"text":130,"config":586},{"href":125,"dataGaName":130,"dataGaLocation":465},{"text":119,"config":588},{"href":102,"dataGaName":103,"dataGaLocation":465},{"text":590,"config":591},"Agile development",{"href":592,"dataGaName":593,"dataGaLocation":465},"/solutions/agile-delivery/","agile delivery",{"text":595,"config":596},"SCM",{"href":115,"dataGaName":597,"dataGaLocation":465},"source code management",{"text":544,"config":599},{"href":108,"dataGaName":600,"dataGaLocation":465},"continuous integration & delivery",{"text":602,"config":603},"Value stream management",{"href":158,"dataGaName":604,"dataGaLocation":465},"value stream management",{"text":549,"config":606},{"href":607,"dataGaName":552,"dataGaLocation":465},"/solutions/gitops/",{"text":168,"config":609},{"href":170,"dataGaName":171,"dataGaLocation":465},{"text":611,"config":612},"Small business",{"href":175,"dataGaName":176,"dataGaLocation":465},{"text":614,"config":615},"Public sector",{"href":180,"dataGaName":181,"dataGaLocation":465},{"text":617,"config":618},"Education",{"href":619,"dataGaName":620,"dataGaLocation":465},"/solutions/education/","education",{"text":622,"config":623},"Financial services",{"href":624,"dataGaName":625,"dataGaLocation":465},"/solutions/finance/","financial services",{"title":188,"links":627},[628,630,632,634,637,639,641,643,645,647,649,651],{"text":200,"config":629},{"href":202,"dataGaName":203,"dataGaLocation":465},{"text":205,"config":631},{"href":207,"dataGaName":208,"dataGaLocation":465},{"text":210,"config":633},{"href":212,"dataGaName":213,"dataGaLocation":465},{"text":215,"config":635},{"href":217,"dataGaName":636,"dataGaLocation":465},"docs",{"text":238,"config":638},{"href":240,"dataGaName":241,"dataGaLocation":465},{"text":233,"config":640},{"href":235,"dataGaName":236,"dataGaLocation":465},{"text":243,"config":642},{"href":245,"dataGaName":246,"dataGaLocation":465},{"text":251,"config":644},{"href":253,"dataGaName":254,"dataGaLocation":465},{"text":256,"config":646},{"href":258,"dataGaName":259,"dataGaLocation":465},{"text":261,"config":648},{"href":263,"dataGaName":264,"dataGaLocation":465},{"text":266,"config":650},{"href":268,"dataGaName":269,"dataGaLocation":465},{"text":271,"config":652},{"href":273,"dataGaName":274,"dataGaLocation":465},{"title":289,"links":654},[655,657,659,661,663,665,667,671,676,678,680,682],{"text":296,"config":656},{"href":298,"dataGaName":291,"dataGaLocation":465},{"text":301,"config":658},{"href":303,"dataGaName":304,"dataGaLocation":465},{"text":309,"config":660},{"href":311,"dataGaName":312,"dataGaLocation":465},{"text":314,"config":662},{"href":316,"dataGaName":317,"dataGaLocation":465},{"text":319,"config":664},{"href":321,"dataGaName":322,"dataGaLocation":465},{"text":324,"config":666},{"href":326,"dataGaName":327,"dataGaLocation":465},{"text":668,"config":669},"Sustainability",{"href":670,"dataGaName":668,"dataGaLocation":465},"/sustainability/",{"text":672,"config":673},"Diversity, inclusion and belonging (DIB)",{"href":674,"dataGaName":675,"dataGaLocation":465},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":329,"config":677},{"href":331,"dataGaName":332,"dataGaLocation":465},{"text":339,"config":679},{"href":341,"dataGaName":342,"dataGaLocation":465},{"text":344,"config":681},{"href":346,"dataGaName":347,"dataGaLocation":465},{"text":683,"config":684},"Modern Slavery Transparency Statement",{"href":685,"dataGaName":686,"dataGaLocation":465},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":688},[689,692,695],{"text":690,"config":691},"Terms",{"href":517,"dataGaName":518,"dataGaLocation":465},{"text":693,"config":694},"Cookies",{"dataGaName":527,"dataGaLocation":465,"id":528,"isOneTrustButton":12},{"text":696,"config":697},"Privacy",{"href":522,"dataGaName":523,"dataGaLocation":465},[699,712],{"id":700,"title":23,"body":9,"config":701,"content":703,"description":9,"extension":27,"meta":707,"navigation":12,"path":708,"seo":709,"stem":710,"__hash__":711},"blogAuthors/en-us/blog/authors/matt-genelin.yml",{"template":702},"BlogAuthor",{"name":23,"config":704},{"headshot":705,"ctfId":706},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664522/Blog/Author%20Headshots/matty_genelin.png","6x9dTYZik3lSViI8hu6dYQ",{},"/en-us/blog/authors/matt-genelin",{},"en-us/blog/authors/matt-genelin","BNd-jZWck4DOyJIDc9GI-734vQkyzXJgudpPaFgn5pM",{"id":713,"title":24,"body":9,"config":714,"content":715,"description":9,"extension":27,"meta":719,"navigation":12,"path":720,"seo":721,"stem":722,"__hash__":723},"blogAuthors/en-us/blog/authors/matt-bonner.yml",{"template":702},{"config":716,"name":24,"bio":718},{"headshot":717},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1775484069/x33hdq785qqrkryorkna.jpg","Solutions architect, SmartBear",{},"/en-us/blog/authors/matt-bonner",{},"en-us/blog/authors/matt-bonner","LH2t-4u_lptPQtHVnEjAbAY95_MIODPOJ_kTCdmcfhk",[725,739,745],{"content":726,"config":737},{"title":727,"description":728,"authors":729,"heroImage":731,"date":732,"body":733,"category":10,"tags":734},"GitLab 18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[730],"Bryan Rothwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[10,735,736],"AI/ML","news",{"featured":32,"template":13,"slug":738},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":740,"config":743},{"title":741,"heroImage":731,"description":742,"date":732,"category":10},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":32,"template":13,"externalUrl":744},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":746,"config":754},{"title":747,"description":748,"authors":749,"heroImage":731,"date":732,"body":751,"category":10,"tags":752},"GitLab 18.11: CI Expert and Data Analyst AI agents target development gaps","Set up CI and query your software development lifecycle data with two new GitLab Duo Agent Platform foundational agents available in GitLab 18.11.",[750],"Corinne Dent","AI-generated code moves faster than the systems around it can keep up with. More code means more merge requests queued, more pipelines to configure, more questions about delivery that nobody has time to answer — and most of the tooling teams rely on wasn't built for this pace.\n\nIn GitLab 18.11, two new foundational agents for Duo Agent Platform address specific gaps in the development lifecycle that AI has largely left untouched:\n* CI Expert Agent (now in beta) focuses on the gap between writing code and getting it into a running pipeline\n* Data Analyst Agent (now generally available) focuses on the gap between shipping code and being able to answer basic questions about how that delivery is actually going.\n\n\nThese are problem areas that couldn't be solved by a general-purpose assistant. A tool running outside GitLab can generate a YAML file or answer a question, but it has no awareness of how your pipelines have historically performed, where failures cluster, or what your actual MR cycle times look like. That context lives in GitLab. These agents do too.\n## Fast CI setup with CI Expert Agent\n\nAI has made it easier than ever to write code. Getting that code into a running pipeline is still something most teams do days, or weeks, later — if at all. The blank-page problem isn't in the editor anymore. The blank page is now in `.gitlab-ci.yml`.\n\nDevelopers who have never configured CI don't know what language detection looks like in YAML, what their test commands should be, or how to validate the result before pushing. Teams either copy a config from a previous project that may not fit, stitch together examples from documentation, or wait for the one person who's done it before. If that person isn't available, CI becomes the thing you'll \"get to later.\" Later becomes never.\n\nWhen CI never happens, the impact shows up everywhere else. Changes ship without a reliable safety net, regressions surface in production instead of in pipelines, and work piles up in bigger, riskier batches because no one wants to be the person who “breaks the build.” Over time, teams normalize working in the dark, often relying on undocumented institutional knowledge and ad-hoc testing, instead of having a fast, predictable feedback loop baked into every change.\n\nCI Expert Agent, now available in beta, removes that friction. It inspects your repository, identifies your language and framework, and proposes a working build and test pipeline tailored to what's actually there — then explains every decision in plain language. The target: a running pipeline in minutes, with no YAML written by hand.\n\nWhat CI Expert Agent does:\n\n* Repo-aware pipeline generation detects language, framework, and test setup \n* Generates valid, runnable build and test configurations   \n* Guided first-pipeline flow with plain-language explanation of each step in Agentic Chat  \n* Native GitLab CI semantics with no config translation required\n\nBecause it runs inside GitLab and sees real pipeline behavior over time, each improvement can build on how teams actually work, not just on static examples.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183458036?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=\"CI/CD Expert Agent\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nCI Expert Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium, Ultimate Editions with Duo Agent Platform enabled.\n\n## Query GitLab data in plain language with Data Analyst Agent\n\nAI has sped up how teams ship. Answering basic questions about how that work is going has gotten harder, not easier.\n\nHow long are MRs sitting in review? Which pipelines are slowing teams down? Are deployment targets actually being hit? These questions used to be answerable by glancing at a dashboard. Now, with more code, more teams, and more complexity, the data exists — it's in GitLab — but accessing it still means waiting on an analytics team, filing a dashboard request, or learning GLQL.\n\nData Analyst Agent targets that gap. Ask a natural-language question and get an instant visualization in Agentic Chat. No query language, no dashboard request, no waiting for the answers to be assembled by someone else.\n\nFor example, the agent can answer questions about the following topics for these roles:\n\n* Engineering managers: MR cycle time, throughput by project, where reviews get stuck  \n* Developers: Contribution patterns, flaky tests blocking their MRs, pipeline speed trends  \n* DevOps and platform engineers: Pipeline success/failure rates, runner utilization, deployment frequency  \n* Engineering leadership: Cross-portfolio deployment frequency, project health metrics, lead time comparisons\n\nNow generally available in 18.11, the agent covers MRs, issues, projects, pipelines, and jobs — full software development lifecycle coverage, expanded from the beta scope. Because Data Analyst Agent queries what's already in GitLab, the context is always current, and there's no pipeline to maintain or third-party tool to keep synchronized. Generated GitLab Query Language queries can be copied and used anywhere GitLab Flavored Markdown is supported, with direct export to work items and dashboards on the roadmap.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1183094817?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=\"Data Analyst agent demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nData Analyst Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium and Ultimate Edition with Duo Agent Platform enabled.\n\n## One platform, connected context\n\nBoth agents run inside GitLab, with access to the code, pipelines, issues, and merge requests already there. That's what separates platform-native AI from a disconnected assistant: the context is always current, and it only gets more useful over time. CI Expert Agent and Data Analyst Agent represent two concrete steps toward a platform where AI doesn't just help you write code faster; it helps you understand, ship, and maintain what gets built.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) to experience these foundational AI agents.",[735,753,10],"features",{"featured":12,"template":13,"slug":755},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":757},[758,772,783],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[10,566],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":787,"text":763,"button":788,"image":792},"security-modernization",[786],"security","Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":768,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":796,"blurb":797,"button":798,"secondaryButton":803},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":799,"config":800},"Get your free trial",{"href":801,"dataGaName":48,"dataGaLocation":802},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":503,"config":804},{"href":52,"dataGaName":53,"dataGaLocation":802},1776436848325]