[{"data":1,"prerenderedAt":796},["ShallowReactive",2],{"/en-us/blog/taming-tool-sprawl-how-to-boost-university-it-productivity":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Elisabeth Burrows":694,"blog-related-posts-en-us-taming-tool-sprawl-how-to-boost-university-it-productivity":708,"assessment-promotions-en-us":747,"next-steps-en-us":786},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":11,"meta":25,"navigation":11,"path":26,"publishedDate":20,"seo":27,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/taming-tool-sprawl-how-to-boost-university-it-productivity.yml","Taming Tool Sprawl How To Boost University It Productivity",[7],"elisabeth-burrows",null,"product",{"featured":11,"template":12,"slug":13},true,"BlogPost","taming-tool-sprawl-how-to-boost-university-it-productivity",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Taming tool sprawl: How to boost university IT productivity","Discover how a unified DevSecOps platform drives user growth, automates compliance, and ensures responsible AI adoption in higher education.",[18],"Elisabeth Burrows","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756989645/fojzxakmfdea6jfqjkrl.png","2025-12-15","When Dr. James Quilty began developing engineering project management courses at Victoria University of Wellington's School of Engineering and Computer Science, he didn't find an organized system for delivering course content. Instead, he was faced with chaos.\n\nThe problem was that back in 2015 learning materials were scattered across a dozen different tools, like the Blackboard platform, customized Wiki pages, personal websites, and shared Google Docs. On top of that, students were left to choose their own tools for coursework. All of this led to a constant state of confusion. As if that weren’t enough, few of these disparate systems provided proper version history or reliable issue tracking.\n\nThis all-too-familiar lack of standardization was creating massive headaches for both lecturers and students.\n\n\"Information was fragmented across multiple files, multiple formats — sitting often on file systems, not necessarily under good version control,\" says Quilty, who is now program director for engineering at the New Zealand university.\n\nAfter consolidating on GitLab Self-Managed Ultimate in 2017, [Victoria University](https://about.gitlab.com/customers/victoria-university/) saw 483% growth in student users by 2021. They also added 35 GitLab-enabled courses and now host more than 8,000 projects. The university also deployed GitLab as a unified DevSecOps platform for academic coursework, replacing what had become a fragmented and complicated toolchain. More importantly, they've redirected faculty time from administration to actual education.\n\nThis pattern isn't unique. Across higher education, IT teams struggle with the same tool sprawl — multiple tools and incompatible systems that lead to hours lost to context switching and administering disparate and costly tools. Simplifying how teams build and deliver software is the answer to this widespread problem.\n\nThe teams making real progress are reducing complexity instead of creating it.\n\n### Facing the complexity problem\n\nHigher education IT teams are faced with managing aging infrastructure, legacy systems, and resource constraints that force difficult tradeoffs with every technology decision they have to make.\n\nDevelopment workflows exist in silos since many departments use different version control systems, CI/CD tools, and security scanners. That means teams struggle to collaborate on cross-functional projects because they're working with incompatible toolchains and a lack of shared visibility.\n\nLegacy technology compounds these problems. Many institutions run development environments that are outdated and incompatible with modern DevSecOps practices. But replacing them isn't realistic when budgets are tight and IT staff are already stretched thin.\n\nTo take on these problems, institutions need to modernize, but because of administrative processes, budget constraints, and the reality of managing critical systems, they have to do it in phases, not overnight. For instance, some workloads may move to the cloud while others remain on-premises. [A research department](https://edtechmagazine.com/higher/article/2024/09/how-approach-higher-eds-hybrid-cloud-migration), for instance, might shift large datasets off-site while central IT functions stay in-house.\n\nOrganizations need the flexibility to be able to do that, and that’s what they get with GitLab Ultimate, the enterprise-ready DevSecOps platform that delivers the same capabilities whether you deploy on GitLab.com or self-host on your own infrastructure: on-premises servers, data centers, or cloud providers, including AWS, GCP, Azure, or even multi-cloud. Self-hosted deployments include all features, including air-gapped support for sensitive environments.\n\nThis means, with [GitLab Ultimate](https://about.gitlab.com/pricing/ultimate/), institutions can modernize on their own timeline without abandoning governance requirements or forcing wholesale infrastructure changes.\n\n### Moving from manual compliance to automated enforcement\n\nIT teams also have to work with regulatory mandates and that adds another layer of complexity. Student privacy requirements, research grant stipulations, and institutional security policies all demand audit trails and governance controls. For institutions supporting U.S. Department of Defense research or contractors, [CMMC 2.0 compliance requirements](https://www.meritalk.com/articles/dod-begins-rollout-of-cmmc-on-nov-10-heres-what-you-need-to-know/) add stringent cybersecurity controls based on NIST SP 800-171. Meeting these obligations while modernizing traditionally meant manually documenting everything — a process that didn't scale easily.\n\nIn conversations with team members from educational institutions at events like EDUCAUSE we've learned it's all too common for dedicated compliance staff to spend the majority of their time gathering evidence for audits, instead of actually improving security. Not building better software. Just proving that policies were followed. This administrative burden extends to development teams, as well. According to Forrester Consulting’s study [The Total Economic Impact™ of GitLab Ultimate](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/), which was commissioned by GitLab, software development team members save 90% of the time previously spent on annual auditing and compliance efforts after adopting GitLab's end-to-end platform.\n\nGitLab saves all of that time and effort by enabling automation through [custom compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that map multiple, overlapping controls from different standards and regulations into a single, unified structure. They then cascade automatically from the instance level to all subgroups and projects, ensuring consistent enforcement without manual configuration.\n\n[Pipeline execution policies](https://docs.gitlab.com/user/application_security/policies/pipeline_execution_policies/) enforce compliance directly in CI/CD pipelines where development work happens. Rather than operating disparate governance, risk, and compliance tools, compliance validation occurs automatically as code moves through the pipeline. To make all of this easier, GitLab’s [Compliance Center](https://docs.gitlab.com/user/compliance/compliance_center/) provides oversight through dashboards that show where projects fail to meet framework requirements — whether due to failed security scans or other control gaps.\n\nComplete [audit trails](https://docs.gitlab.com/user/compliance/audit_events/) also capture every code change with timestamps and attribution. And [policy-as-code](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/policy-as-code/) enforces security rules that can't be bypassed. When an auditor asks who changed what code and when, you have the answer instantly — without weeks spent manually gathering evidence. Every pipeline execution automatically generates compliance documentation, enabling teams to instantly prove adherence to requirements and quickly identify any control gaps.\n\n### AI: Governance over guesswork\n\nThis visibility across the entire security posture matters now more than ever. Artificial intelligence (AI) is changing how software gets built with many teams testing AI code generation tools to enable them to move faster. But higher education institutions are uniquely positioned to lead on a critical question: How do you adopt AI responsibly?\n\n[Cornell University](https://edtechmagazine.com/higher/article/2025/10/ai-playbook-comprehensive-strategy-higher-education-perfcon) and [Cal State Fullerton](https://www.fullerton.edu/it/ai/ethical-principles-ai-framework.html) already are developing ethical frameworks for AI use, asking essential questions about transparency, explainability, and bias. The [University of California San Diego](https://edtechmagazine.com/higher/article/2025/05/effective-ai-requires-effective-data-governance) is adapting its existing data governance framework — originally built for analytics platforms — to secure its on-premises AI assistants, ensuring the same access controls and approval workflows that protect institutional data now extend to AI-driven tools. Educational institutions understand that AI adoption requires more than just enabling new tools — it requires proper oversight and protection.\n\nThe problem isn't AI itself. It's AI without guardrails integrated into development workflows. Most organizations haven't considered what secure AI development looks like — what governance is needed for AI-generated code, how to maintain visibility into what gets committed to repositories, or how to ensure the same rigor applies whether code comes from a human or AI.\n\nThis is exactly where platform-level AI integration becomes essential. [GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/) goes beyond fragmented AI tools and coding assistants alone to provide an orchestration layer that integrates AI across the entire software development lifecycle.\n\nAI agents handle planning, testing, security remediation, and deployment tasks, while working alongside developers rather than just generating code on command. When security scans identify vulnerabilities, for example, AI agents explain findings, assess risks, and prioritize issues to reduce noise and accelerate mean time to recovery (MTTR). This platform approach ensures AI accelerates development without compromising the security standards and governance controls institutions require.\n\nThe benefits extend beyond technical capabilities. Through GitLab's [AI Transparency Center](https://about.gitlab.com/ai-transparency-center/), institutions get clear documentation of data privacy protections, AI ethics principles, and vendor selection processes. This means schools can adopt AI tools while maintaining the governance standards they're developing institution-wide.\n\nAI will change how we build software. The question is whether institutions can do it with the same responsible approach they're bringing to AI adoption across campus.\n\n## See results in your education environment\n\nThe universities making real progress aren't adding more tools to manage complexity. They're consolidating onto platforms that prevent problems rather than just detecting them, creating visibility and automation across their development workflows.\n\nForrester's [The Total Economic Impact™ of GitLab Ultimate](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/) study found that a composite organization representative of interviewed customers reclaimed up to 305 hours per developer year through automated testing within a single interface, eliminating constant context switching between tools. New hires ramped to full productivity 75% faster — in 1.5 weeks instead of 1.5 months. Teams spend their time building rather than maintaining fragmented toolchains.\n\n**Your institution can achieve similar results.** Learn more about how GitLab Ultimate can help your institution deliver secure software faster while meeting compliance requirements. [Talk to our team](https://about.gitlab.com/sales/) about platform approaches for higher education IT.",[23,9],"education","yml",{},"/en-us/blog/taming-tool-sprawl-how-to-boost-university-it-productivity",{"config":28,"title":15,"description":16},{"noIndex":29},false,"en-us/blog/taming-tool-sprawl-how-to-boost-university-it-productivity",[23,9],"zwCFhnP2GP58RkaZ-g5F2mxtQf3PjdmTM0JRvEarisg",{"data":34},{"logo":35,"freeTrial":40,"sales":45,"login":50,"items":55,"search":363,"minimal":394,"duo":413,"switchNav":422,"pricingDeployment":433},{"config":36},{"href":37,"dataGaName":38,"dataGaLocation":39},"/","gitlab logo","header",{"text":41,"config":42},"Get free trial",{"href":43,"dataGaName":44,"dataGaLocation":39},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":46,"config":47},"Talk to sales",{"href":48,"dataGaName":49,"dataGaLocation":39},"/sales/","sales",{"text":51,"config":52},"Sign in",{"href":53,"dataGaName":54,"dataGaLocation":39},"https://gitlab.com/users/sign_in/","sign in",[56,83,178,183,284,344],{"text":57,"config":58,"cards":60},"Platform",{"dataNavLevelOne":59},"platform",[61,67,75],{"title":57,"description":62,"link":63},"The intelligent orchestration platform for DevSecOps",{"text":64,"config":65},"Explore our Platform",{"href":66,"dataGaName":59,"dataGaLocation":39},"/platform/",{"title":68,"description":69,"link":70},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":71,"config":72},"Meet GitLab Duo",{"href":73,"dataGaName":74,"dataGaLocation":39},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":76,"description":77,"link":78},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":79,"config":80},"Learn more",{"href":81,"dataGaName":82,"dataGaLocation":39},"/why-gitlab/","why gitlab",{"text":84,"left":11,"config":85,"link":87,"lists":91,"footer":160},"Product",{"dataNavLevelOne":86},"solutions",{"text":88,"config":89},"View all Solutions",{"href":90,"dataGaName":86,"dataGaLocation":39},"/solutions/",[92,116,139],{"title":93,"description":94,"link":95,"items":100},"Automation","CI/CD and automation to accelerate deployment",{"config":96},{"icon":97,"href":98,"dataGaName":99,"dataGaLocation":39},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[101,105,108,112],{"text":102,"config":103},"CI/CD",{"href":104,"dataGaLocation":39,"dataGaName":102},"/solutions/continuous-integration/",{"text":68,"config":106},{"href":73,"dataGaLocation":39,"dataGaName":107},"gitlab duo agent platform - product menu",{"text":109,"config":110},"Source Code Management",{"href":111,"dataGaLocation":39,"dataGaName":109},"/solutions/source-code-management/",{"text":113,"config":114},"Automated Software Delivery",{"href":98,"dataGaLocation":39,"dataGaName":115},"Automated software delivery",{"title":117,"description":118,"link":119,"items":124},"Security","Deliver code faster without compromising security",{"config":120},{"href":121,"dataGaName":122,"dataGaLocation":39,"icon":123},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[125,129,134],{"text":126,"config":127},"Application Security Testing",{"href":121,"dataGaName":128,"dataGaLocation":39},"Application security testing",{"text":130,"config":131},"Software Supply Chain Security",{"href":132,"dataGaLocation":39,"dataGaName":133},"/solutions/supply-chain/","Software supply chain security",{"text":135,"config":136},"Software Compliance",{"href":137,"dataGaName":138,"dataGaLocation":39},"/solutions/software-compliance/","software compliance",{"title":140,"link":141,"items":146},"Measurement",{"config":142},{"icon":143,"href":144,"dataGaName":145,"dataGaLocation":39},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[147,151,155],{"text":148,"config":149},"Visibility & Measurement",{"href":144,"dataGaLocation":39,"dataGaName":150},"Visibility and Measurement",{"text":152,"config":153},"Value Stream Management",{"href":154,"dataGaLocation":39,"dataGaName":152},"/solutions/value-stream-management/",{"text":156,"config":157},"Analytics & Insights",{"href":158,"dataGaLocation":39,"dataGaName":159},"/solutions/analytics-and-insights/","Analytics and insights",{"title":161,"items":162},"GitLab for",[163,168,173],{"text":164,"config":165},"Enterprise",{"href":166,"dataGaLocation":39,"dataGaName":167},"/enterprise/","enterprise",{"text":169,"config":170},"Small Business",{"href":171,"dataGaLocation":39,"dataGaName":172},"/small-business/","small business",{"text":174,"config":175},"Public Sector",{"href":176,"dataGaLocation":39,"dataGaName":177},"/solutions/public-sector/","public sector",{"text":179,"config":180},"Pricing",{"href":181,"dataGaName":182,"dataGaLocation":39,"dataNavLevelOne":182},"/pricing/","pricing",{"text":184,"config":185,"link":187,"lists":191,"feature":271},"Resources",{"dataNavLevelOne":186},"resources",{"text":188,"config":189},"View all resources",{"href":190,"dataGaName":186,"dataGaLocation":39},"/resources/",[192,225,243],{"title":193,"items":194},"Getting started",[195,200,205,210,215,220],{"text":196,"config":197},"Install",{"href":198,"dataGaName":199,"dataGaLocation":39},"/install/","install",{"text":201,"config":202},"Quick start guides",{"href":203,"dataGaName":204,"dataGaLocation":39},"/get-started/","quick setup checklists",{"text":206,"config":207},"Learn",{"href":208,"dataGaLocation":39,"dataGaName":209},"https://university.gitlab.com/","learn",{"text":211,"config":212},"Product documentation",{"href":213,"dataGaName":214,"dataGaLocation":39},"https://docs.gitlab.com/","product documentation",{"text":216,"config":217},"Best practice videos",{"href":218,"dataGaName":219,"dataGaLocation":39},"/getting-started-videos/","best practice videos",{"text":221,"config":222},"Integrations",{"href":223,"dataGaName":224,"dataGaLocation":39},"/integrations/","integrations",{"title":226,"items":227},"Discover",[228,233,238],{"text":229,"config":230},"Customer success stories",{"href":231,"dataGaName":232,"dataGaLocation":39},"/customers/","customer success stories",{"text":234,"config":235},"Blog",{"href":236,"dataGaName":237,"dataGaLocation":39},"/blog/","blog",{"text":239,"config":240},"Remote",{"href":241,"dataGaName":242,"dataGaLocation":39},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":244,"items":245},"Connect",[246,251,256,261,266],{"text":247,"config":248},"GitLab Services",{"href":249,"dataGaName":250,"dataGaLocation":39},"/services/","services",{"text":252,"config":253},"Community",{"href":254,"dataGaName":255,"dataGaLocation":39},"/community/","community",{"text":257,"config":258},"Forum",{"href":259,"dataGaName":260,"dataGaLocation":39},"https://forum.gitlab.com/","forum",{"text":262,"config":263},"Events",{"href":264,"dataGaName":265,"dataGaLocation":39},"/events/","events",{"text":267,"config":268},"Partners",{"href":269,"dataGaName":270,"dataGaLocation":39},"/partners/","partners",{"backgroundColor":272,"textColor":273,"text":274,"image":275,"link":279},"#2f2a6b","#fff","Insights for the future of software development",{"altText":276,"config":277},"the source promo card",{"src":278},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":280,"config":281},"Read the latest",{"href":282,"dataGaName":283,"dataGaLocation":39},"/the-source/","the source",{"text":285,"config":286,"lists":288},"Company",{"dataNavLevelOne":287},"company",[289],{"items":290},[291,296,302,304,309,314,319,324,329,334,339],{"text":292,"config":293},"About",{"href":294,"dataGaName":295,"dataGaLocation":39},"/company/","about",{"text":297,"config":298,"footerGa":301},"Jobs",{"href":299,"dataGaName":300,"dataGaLocation":39},"/jobs/","jobs",{"dataGaName":300},{"text":262,"config":303},{"href":264,"dataGaName":265,"dataGaLocation":39},{"text":305,"config":306},"Leadership",{"href":307,"dataGaName":308,"dataGaLocation":39},"/company/team/e-group/","leadership",{"text":310,"config":311},"Team",{"href":312,"dataGaName":313,"dataGaLocation":39},"/company/team/","team",{"text":315,"config":316},"Handbook",{"href":317,"dataGaName":318,"dataGaLocation":39},"https://handbook.gitlab.com/","handbook",{"text":320,"config":321},"Investor relations",{"href":322,"dataGaName":323,"dataGaLocation":39},"https://ir.gitlab.com/","investor relations",{"text":325,"config":326},"Trust Center",{"href":327,"dataGaName":328,"dataGaLocation":39},"/security/","trust center",{"text":330,"config":331},"AI Transparency Center",{"href":332,"dataGaName":333,"dataGaLocation":39},"/ai-transparency-center/","ai transparency center",{"text":335,"config":336},"Newsletter",{"href":337,"dataGaName":338,"dataGaLocation":39},"/company/contact/#contact-forms","newsletter",{"text":340,"config":341},"Press",{"href":342,"dataGaName":343,"dataGaLocation":39},"/press/","press",{"text":345,"config":346,"lists":347},"Contact us",{"dataNavLevelOne":287},[348],{"items":349},[350,353,358],{"text":46,"config":351},{"href":48,"dataGaName":352,"dataGaLocation":39},"talk to sales",{"text":354,"config":355},"Support portal",{"href":356,"dataGaName":357,"dataGaLocation":39},"https://support.gitlab.com","support portal",{"text":359,"config":360},"Customer portal",{"href":361,"dataGaName":362,"dataGaLocation":39},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":364,"login":365,"suggestions":372},"Close",{"text":366,"link":367},"To search repositories and projects, login to",{"text":368,"config":369},"gitlab.com",{"href":53,"dataGaName":370,"dataGaLocation":371},"search login","search",{"text":373,"default":374},"Suggestions",[375,377,381,383,387,391],{"text":68,"config":376},{"href":73,"dataGaName":68,"dataGaLocation":371},{"text":378,"config":379},"Code Suggestions (AI)",{"href":380,"dataGaName":378,"dataGaLocation":371},"/solutions/code-suggestions/",{"text":102,"config":382},{"href":104,"dataGaName":102,"dataGaLocation":371},{"text":384,"config":385},"GitLab on AWS",{"href":386,"dataGaName":384,"dataGaLocation":371},"/partners/technology-partners/aws/",{"text":388,"config":389},"GitLab on Google Cloud",{"href":390,"dataGaName":388,"dataGaLocation":371},"/partners/technology-partners/google-cloud-platform/",{"text":392,"config":393},"Why GitLab?",{"href":81,"dataGaName":392,"dataGaLocation":371},{"freeTrial":395,"mobileIcon":400,"desktopIcon":405,"secondaryButton":408},{"text":396,"config":397},"Start free trial",{"href":398,"dataGaName":44,"dataGaLocation":399},"https://gitlab.com/-/trials/new/","nav",{"altText":401,"config":402},"Gitlab Icon",{"src":403,"dataGaName":404,"dataGaLocation":399},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":401,"config":406},{"src":407,"dataGaName":404,"dataGaLocation":399},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":409,"config":410},"Get Started",{"href":411,"dataGaName":412,"dataGaLocation":399},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/get-started/","get started",{"freeTrial":414,"mobileIcon":418,"desktopIcon":420},{"text":415,"config":416},"Learn more about GitLab Duo",{"href":73,"dataGaName":417,"dataGaLocation":399},"gitlab duo",{"altText":401,"config":419},{"src":403,"dataGaName":404,"dataGaLocation":399},{"altText":401,"config":421},{"src":407,"dataGaName":404,"dataGaLocation":399},{"button":423,"mobileIcon":428,"desktopIcon":430},{"text":424,"config":425},"/switch",{"href":426,"dataGaName":427,"dataGaLocation":399},"#contact","switch",{"altText":401,"config":429},{"src":403,"dataGaName":404,"dataGaLocation":399},{"altText":401,"config":431},{"src":432,"dataGaName":404,"dataGaLocation":399},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1773335277/ohhpiuoxoldryzrnhfrh.png",{"freeTrial":434,"mobileIcon":439,"desktopIcon":441},{"text":435,"config":436},"Back to pricing",{"href":181,"dataGaName":437,"dataGaLocation":399,"icon":438},"back to pricing","GoBack",{"altText":401,"config":440},{"src":403,"dataGaName":404,"dataGaLocation":399},{"altText":401,"config":442},{"src":407,"dataGaName":404,"dataGaLocation":399},{"title":444,"button":445,"config":450},"See how agentic AI transforms software delivery",{"text":446,"config":447},"Watch GitLab Transcend now",{"href":448,"dataGaName":449,"dataGaLocation":39},"/events/transcend/virtual/","transcend event",{"layout":451,"icon":452,"disabled":11},"release","AiStar",{"data":454},{"text":455,"source":456,"edit":462,"contribute":467,"config":472,"items":477,"minimal":683},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":457,"config":458},"View page source",{"href":459,"dataGaName":460,"dataGaLocation":461},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":463,"config":464},"Edit this page",{"href":465,"dataGaName":466,"dataGaLocation":461},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":468,"config":469},"Please contribute",{"href":470,"dataGaName":471,"dataGaLocation":461},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":473,"facebook":474,"youtube":475,"linkedin":476},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[478,525,579,622,649],{"title":179,"links":479,"subMenu":494},[480,484,489],{"text":481,"config":482},"View plans",{"href":181,"dataGaName":483,"dataGaLocation":461},"view plans",{"text":485,"config":486},"Why Premium?",{"href":487,"dataGaName":488,"dataGaLocation":461},"/pricing/premium/","why premium",{"text":490,"config":491},"Why Ultimate?",{"href":492,"dataGaName":493,"dataGaLocation":461},"/pricing/ultimate/","why ultimate",[495],{"title":496,"links":497},"Contact Us",[498,501,503,505,510,515,520],{"text":499,"config":500},"Contact sales",{"href":48,"dataGaName":49,"dataGaLocation":461},{"text":354,"config":502},{"href":356,"dataGaName":357,"dataGaLocation":461},{"text":359,"config":504},{"href":361,"dataGaName":362,"dataGaLocation":461},{"text":506,"config":507},"Status",{"href":508,"dataGaName":509,"dataGaLocation":461},"https://status.gitlab.com/","status",{"text":511,"config":512},"Terms of use",{"href":513,"dataGaName":514,"dataGaLocation":461},"/terms/","terms of use",{"text":516,"config":517},"Privacy statement",{"href":518,"dataGaName":519,"dataGaLocation":461},"/privacy/","privacy statement",{"text":521,"config":522},"Cookie preferences",{"dataGaName":523,"dataGaLocation":461,"id":524,"isOneTrustButton":11},"cookie preferences","ot-sdk-btn",{"title":84,"links":526,"subMenu":535},[527,531],{"text":528,"config":529},"DevSecOps platform",{"href":66,"dataGaName":530,"dataGaLocation":461},"devsecops platform",{"text":532,"config":533},"AI-Assisted Development",{"href":73,"dataGaName":534,"dataGaLocation":461},"ai-assisted development",[536],{"title":537,"links":538},"Topics",[539,544,549,554,559,564,569,574],{"text":540,"config":541},"CICD",{"href":542,"dataGaName":543,"dataGaLocation":461},"/topics/ci-cd/","cicd",{"text":545,"config":546},"GitOps",{"href":547,"dataGaName":548,"dataGaLocation":461},"/topics/gitops/","gitops",{"text":550,"config":551},"DevOps",{"href":552,"dataGaName":553,"dataGaLocation":461},"/topics/devops/","devops",{"text":555,"config":556},"Version Control",{"href":557,"dataGaName":558,"dataGaLocation":461},"/topics/version-control/","version control",{"text":560,"config":561},"DevSecOps",{"href":562,"dataGaName":563,"dataGaLocation":461},"/topics/devsecops/","devsecops",{"text":565,"config":566},"Cloud Native",{"href":567,"dataGaName":568,"dataGaLocation":461},"/topics/cloud-native/","cloud native",{"text":570,"config":571},"AI for Coding",{"href":572,"dataGaName":573,"dataGaLocation":461},"/topics/devops/ai-for-coding/","ai for coding",{"text":575,"config":576},"Agentic AI",{"href":577,"dataGaName":578,"dataGaLocation":461},"/topics/agentic-ai/","agentic ai",{"title":580,"links":581},"Solutions",[582,584,586,591,595,598,602,605,607,610,613,617],{"text":126,"config":583},{"href":121,"dataGaName":126,"dataGaLocation":461},{"text":115,"config":585},{"href":98,"dataGaName":99,"dataGaLocation":461},{"text":587,"config":588},"Agile development",{"href":589,"dataGaName":590,"dataGaLocation":461},"/solutions/agile-delivery/","agile delivery",{"text":592,"config":593},"SCM",{"href":111,"dataGaName":594,"dataGaLocation":461},"source code management",{"text":540,"config":596},{"href":104,"dataGaName":597,"dataGaLocation":461},"continuous integration & delivery",{"text":599,"config":600},"Value stream management",{"href":154,"dataGaName":601,"dataGaLocation":461},"value stream management",{"text":545,"config":603},{"href":604,"dataGaName":548,"dataGaLocation":461},"/solutions/gitops/",{"text":164,"config":606},{"href":166,"dataGaName":167,"dataGaLocation":461},{"text":608,"config":609},"Small business",{"href":171,"dataGaName":172,"dataGaLocation":461},{"text":611,"config":612},"Public sector",{"href":176,"dataGaName":177,"dataGaLocation":461},{"text":614,"config":615},"Education",{"href":616,"dataGaName":23,"dataGaLocation":461},"/solutions/education/",{"text":618,"config":619},"Financial services",{"href":620,"dataGaName":621,"dataGaLocation":461},"/solutions/finance/","financial services",{"title":184,"links":623},[624,626,628,630,633,635,637,639,641,643,645,647],{"text":196,"config":625},{"href":198,"dataGaName":199,"dataGaLocation":461},{"text":201,"config":627},{"href":203,"dataGaName":204,"dataGaLocation":461},{"text":206,"config":629},{"href":208,"dataGaName":209,"dataGaLocation":461},{"text":211,"config":631},{"href":213,"dataGaName":632,"dataGaLocation":461},"docs",{"text":234,"config":634},{"href":236,"dataGaName":237,"dataGaLocation":461},{"text":229,"config":636},{"href":231,"dataGaName":232,"dataGaLocation":461},{"text":239,"config":638},{"href":241,"dataGaName":242,"dataGaLocation":461},{"text":247,"config":640},{"href":249,"dataGaName":250,"dataGaLocation":461},{"text":252,"config":642},{"href":254,"dataGaName":255,"dataGaLocation":461},{"text":257,"config":644},{"href":259,"dataGaName":260,"dataGaLocation":461},{"text":262,"config":646},{"href":264,"dataGaName":265,"dataGaLocation":461},{"text":267,"config":648},{"href":269,"dataGaName":270,"dataGaLocation":461},{"title":285,"links":650},[651,653,655,657,659,661,663,667,672,674,676,678],{"text":292,"config":652},{"href":294,"dataGaName":287,"dataGaLocation":461},{"text":297,"config":654},{"href":299,"dataGaName":300,"dataGaLocation":461},{"text":305,"config":656},{"href":307,"dataGaName":308,"dataGaLocation":461},{"text":310,"config":658},{"href":312,"dataGaName":313,"dataGaLocation":461},{"text":315,"config":660},{"href":317,"dataGaName":318,"dataGaLocation":461},{"text":320,"config":662},{"href":322,"dataGaName":323,"dataGaLocation":461},{"text":664,"config":665},"Sustainability",{"href":666,"dataGaName":664,"dataGaLocation":461},"/sustainability/",{"text":668,"config":669},"Diversity, inclusion and belonging (DIB)",{"href":670,"dataGaName":671,"dataGaLocation":461},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":325,"config":673},{"href":327,"dataGaName":328,"dataGaLocation":461},{"text":335,"config":675},{"href":337,"dataGaName":338,"dataGaLocation":461},{"text":340,"config":677},{"href":342,"dataGaName":343,"dataGaLocation":461},{"text":679,"config":680},"Modern Slavery Transparency Statement",{"href":681,"dataGaName":682,"dataGaLocation":461},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":684},[685,688,691],{"text":686,"config":687},"Terms",{"href":513,"dataGaName":514,"dataGaLocation":461},{"text":689,"config":690},"Cookies",{"dataGaName":523,"dataGaLocation":461,"id":524,"isOneTrustButton":11},{"text":692,"config":693},"Privacy",{"href":518,"dataGaName":519,"dataGaLocation":461},[695],{"id":696,"title":18,"body":8,"config":697,"content":699,"description":8,"extension":24,"meta":703,"navigation":11,"path":704,"seo":705,"stem":706,"__hash__":707},"blogAuthors/en-us/blog/authors/elisabeth-burrows.yml",{"template":698},"BlogAuthor",{"name":18,"config":700},{"headshot":701,"ctfId":702},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659535/Blog/Author%20Headshots/liz_burrows_headshot.png","6Nj2Lio5W7HdeNYoysVgCf",{},"/en-us/blog/authors/elisabeth-burrows",{},"en-us/blog/authors/elisabeth-burrows","Aa3Md9daKifGCswl2xZz_ybV8wPpCajPpr3-IX9wRRg",[709,720,734],{"content":710,"config":718},{"title":711,"description":712,"heroImage":713,"date":714,"category":9,"tags":715},"GitLab Patch Release: 18.10.3, 18.9.5, 18.8.9","Learn more this patch release for GitLab Community Edition and Enterprise Edition.\n\n","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749661926/Blog/Hero%20Images/security-patch-blog-image-r2-0506-700x400-fy25_2x.jpg","2026-04-08",[716,717],"security releases","patch releases",{"featured":29,"template":12,"externalUrl":719},"https://about.gitlab.com/releases/2026/04/08/patch-release-gitlab-18-10-3-released/",{"content":721,"config":732},{"title":722,"description":723,"heroImage":724,"category":9,"tags":725,"authors":727,"date":730,"body":731},"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",[726,9,553],"tutorial",[728,729],"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/).",{"featured":11,"template":12,"slug":733},"streamline-test-management-with-the-smartbear-qmetry-gitlab-component",{"content":735,"config":745},{"title":736,"description":737,"authors":738,"heroImage":740,"date":730,"body":741,"category":9,"tags":742},"GitLab Duo CLI: Agentic AI for the development lifecycle, now in the terminal","Developers who work outside the IDE and GitLab UI can access GitLab Duo Agent Platform in the terminal with built-in security controls and headless mode support.",[739],"John Coghlan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1775561395/bhe1as7ttjvzltxwgo5m.png","Debugging a broken pipeline at the end of a sprint, or wiring AI into a CI/CD workflow that runs without anyone watching, is exactly where today's AI assistants fall short given their focus on coding – which is only a portion of the software lifecycle. They're built for interactive coding sessions, not automation across different stages of software development. GitLab Duo CLI, now in public beta, is built for both.\n\nGitLab Duo CLI brings agentic AI powered by [Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) to the terminal with full support for automated workflows, alongside an interactive chat mode when you need a human in the loop. This article highlights what Duo CLI does, how its two operating modes work, and the security model behind it.\n\n## How to install GitLab Duo CLI\n\nIf you already have GLab (the GitLab CLI) installed, enter:\n\n```\nglab duo cli\n```\n\nThen follow the prompts.\n\nIf you don't have GLab yet, you can [install it here](https://gitlab.com/gitlab-org/cli/#installation) or [use GitLab Duo CLI as a standalone tool](https://docs.gitlab.com/user/gitlab_duo_cli/#without-the-gitlab-cli).\n\n## Why the terminal, and why now\n\nThe first wave of AI assistants for software development lived in the IDE, and focused solely on coding. That made sense when the job was autocomplete. But as AI agents start *doing things* across every stage of the software lifecycle, e.g. running tests, triggering pipelines, monitoring vulnerability scans, and more, the IDE may no longer be the only abstraction needed to get the job done.\n\nThe best developer tools are ones that work for both humans and machines. CLIs have had decades of design iteration toward that goal. They're composable. You can pipe output, chain commands, and drop them into scripts. They're debuggable: when something goes wrong, you run the same command yourself and see exactly what the agent saw. And they're transparent. No background processes, no initialization dance, no protocol to decode when things break.\n\nTerminal interfaces are better for automation, scripting, and environment portability. IDE interfaces are better for interactive, context-rich development. GitLab Duo CLI is designed for the former, while Duo Agentic Chat in the IDE and UI covers the latter.\n\n## What GitLab Duo CLI can do\n\nWith GitLab Duo CLI, developers can build, modify, refactor, and modernize code — similar to other AI-powered coding assistants built for the terminal. But that’s not where they stop. Any agent and flow defined within GitLab Duo Agent Platform is accessible via Duo CLI, whether it is to automate CI/CD configuration and optimize pipelines, or to perform multi-step development tasks autonomously across the entire software development lifecycle.\n\nGitLab Duo CLI runs in two modes:\n\n* **Interactive mode**, an editor-agnostic terminal chat experience with human-in-the-loop approval before any action is taken. Use it to understand codebase structure, create code, fix errors, or troubleshoot broken pipelines.  \n* **Headless mode**, non-interactive, designed for runners, scripts, and automated workflows. Drop it into CI/CD and let it work without handholding.\n\n## AI with guardrails\n\nAgentic AI that can take actions creates real security exposure. GitLab Duo CLI addresses this at the platform level, not as an afterthought:\n\n* **Human-in-the-loop by default** in interactive mode, so no action is taken without approval.  \n* **Prompt injection detection** is built into the GitLab Duo Agent Platform, not bolted on.  \n* **Composite identity** limits what the agent can access and makes every AI-driven action auditable.\n\nGitLab Duo CLI also supports [custom instruction files](https://docs.gitlab.com/user/duo_agent_platform/customize/), e.g. `chat-rules.md`, `AGENTS.md`, and `SKILL.md`, that define which tasks, resources, context, knowledge, and actions your agents are permitted to take. **This is the principle of least privilege applied to AI: Your agent does exactly what you've authorized, and nothing more.**\n\nWatch GitLab Duo CLI in action:\n\u003Ciframe src=\"https://player.vimeo.com/video/1179964611?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=\"GitLab Duo CLI Beta Demo V1\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Use GitLab Duo CLI today\n\nYou can experience the benefits of GitLab Duo CLI by [starting a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/). \n\nIf you are already using GitLab in the free tier, you can sign up for GitLab Duo Agent Platform by [following a few simple steps](https://docs.gitlab.com/subscriptions/gitlab_credits/#for-the-free-tier-on-gitlabcom). \n\nAnd if you are an existing subscriber to GitLab Premium or Ultimate, you can take advantage of GitLab Duo CLI by simply [turning on Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/turn_on_off/) and start using the GitLab Credits [that are included](https://docs.gitlab.com/subscriptions/gitlab_credits/#included-credits) with your subscription.",[743,9,744],"AI/ML","features",{"featured":11,"template":12,"slug":746},"gitlab-duo-cli",{"promotions":748},[749,763,774],{"id":750,"categories":751,"header":753,"text":754,"button":755,"image":760},"ai-modernization",[752],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":756,"config":757},"Get your AI maturity score",{"href":758,"dataGaName":759,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":761},{"src":762},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":764,"categories":765,"header":766,"text":754,"button":767,"image":771},"devops-modernization",[9,563],"Are you just managing tools or shipping innovation?",{"text":768,"config":769},"Get your DevOps maturity score",{"href":770,"dataGaName":759,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":772},{"src":773},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":775,"categories":776,"header":778,"text":754,"button":779,"image":783},"security-modernization",[777],"security","Are you trading speed for security?",{"text":780,"config":781},"Get your security maturity score",{"href":782,"dataGaName":759,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":784},{"src":785},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":787,"blurb":788,"button":789,"secondaryButton":794},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":790,"config":791},"Get your free trial",{"href":792,"dataGaName":44,"dataGaLocation":793},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":795},{"href":48,"dataGaName":49,"dataGaLocation":793},1777309981400]