[{"data":1,"prerenderedAt":806},["ShallowReactive",2],{"/en-us/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch":3,"navigation-en-us":41,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Dov Hershkovitch":698,"blog-related-posts-en-us-ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch":712,"blog-promotions-en-us":743,"next-steps-en-us":796},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":40},"blogPosts/en-us/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch.yml","Ci Cd Catalog Goes Ga No More Building Pipelines From Scratch",[7],"dov-hershkovitch",null,"product",{"slug":11,"featured":12,"template":13},"ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"CI/CD Catalog goes GA: No more building pipelines from scratch","The CI/CD Catalog becomes generally available in GitLab 17.0. Get to know the capabilities for discovering and sharing pipeline building blocks to help standardize and scale pipelines.",[18],"Dov Hershkovitch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098794/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%289%29_DoeBNJVrhv9FpF3WCsHNc_1750098793762.png","2024-05-08","GitLab's [CI/CD Catalog](https://docs.gitlab.com/ee/ci/components/#cicd-catalog) becomes generally available in 17.0 (May 16, 2024), enabling all GitLab users to discover, reuse, and contribute CI/CD components easily. The CI/CD Catalog boosts collaboration and efficiency when creating pipeline configurations by allowing access to a treasure trove of pre-built components, ready to seamlessly integrate into DevSecOps workflows. Enterprises can use the CI/CD Catalog's centralized platform to standardize workflows across the whole organization.\n\nWith the CI/CD Catalog, GitLab is introducing several key capabilities that are also generally available.\n\n> Discover the future of AI-driven software development with our GitLab 17 virtual launch event. [Watch today!](https://about.gitlab.com/eighteen/)\n\n## Components and inputs\nThe [CI/CD Catalog](https://about.gitlab.com/blog/introducing-the-gitlab-ci-cd-catalog-beta/) draws its strength from two fundamental features: components and inputs. These capabilities form the backbone of the catalog, enabling developers and DevSecOps teams to streamline their pipeline development. Let’s dive into each of these features:\n\n### Components\n\n#### What are components?\nComponents are reusable, single-purpose building blocks that abstract away the complexity of pipeline configuration. Think of them as Lego pieces for your CI/CD workflows. By using components, you can assemble pipelines more efficiently without starting from scratch each time.\n\n#### Types of components\n- Template-type components: These components resemble CI templates and come with predefined input definitions. They are organized within a specific directory structure, which you can easily plug into your pipelines.\n- CI Steps (upcoming): This new type of component, which is available as an [experimental feature](https://docs.gitlab.com/ee/ci/steps/), will become a first-class object in the CI/CD Catalog, so stay tuned for this exciting addition.\n\n### Inputs\n\n#### What is Inputs Interpolation?\n\nInputs Interpolation is a powerful feature that allows you to define input parameters for includable configuration files. By using the [spec: inputs keyword](https://docs.gitlab.com/ee/ci/yaml/#specinputs) within your component configuration, you can dynamically replace almost any keywords within components with parameters. This flexibility extends to adjusting stages, scripts, or job names, supporting various data types making the component fully flexible to your needs.\n\n##### Scoped and effective\nImportantly, inputs are scoped exclusively to the included configuration. This prevents unintended effects on the rest of your pipeline. With Inputs Interpolation, you can declare and enforce constraints seamlessly, ensuring smooth integration of components.\n\nWhether you’re a seasoned DevOps pro or just starting out, the CI/CD Catalog, components, and Inputs Interpolation will transform your pipeline development experience.\n\n## How to access CI/CD Catalog components\nThe CI/CD Catalog is a powerful resource for developers and DevOps teams. It allows you to share and discover pre-built components, streamlining your pipeline development. Here’s how it works:\n\n1. Components are standalone building blocks that simplify pipeline configuration. You can create custom components tailored to your needs. But how do you make them available to others? That’s where the CI/CD Catalog comes in.\n\n2. How to publish to the CI/CD Catalog\n    - To share your components with the community, follow these steps:\n      - Use a simple CI job to publish your component and make it discoverable in the CI/CD Catalog.\n      - Whether it’s a reusable script, a deployment template, or any other pipeline element, the CI/CD Catalog is the perfect place to contribute.\nComponents released to the CI/CD Catalog should be tagged with a [semantic version](https://docs.gitlab.com/ee/ci/components/#semantic-versioning) using three digits.\n    - By sharing your components, you contribute to a growing library of resources that benefit the entire community.\n3. Catalog index page\n    - The main page of the CI/CD Catalog (also known as the index page) provides an overview of available projects with published components. Anyone can access the catalog and search for a component that suits their needs.\n    - The index page features two tabs:\n      - All: Displays all component projects that have been published and visible to you.\n      - Your groups: Shows components published within a namespace you’re part of.\n\n![CI/CD Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098805/Blog/Content%20Images/Blog/Content%20Images/catalog_index_aHR0cHM6_1750098804807.png)\n\n4.  Catalog details page\n\n- Upon clicking on one of the projects in the CI/CD Catalog, you will be redirected to the details page where you can view the available components in that project.     - Note that there could be multiple components in a single project.\n\n- The details page features two tabs:\n\u003Ccenter>\u003Ci>Readme: Displays the readme.md of the project that was previously configured by the user.\u003C/i>\u003C/center>\n\n![readme tab](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098805/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750098804808.png)\n\n\u003Ccenter>\u003Ci>Components: Displays the detailed information for each component such as inputs table syntax to use and more. This information is generated and displayed automatically to help keep it up to date.\u003C/i>\u003C/center>\n\n![components tab](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098805/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750098804809.png)\n\n## Using a component\n\nTo use a component from the CI/CD Catalog, simply copy the suggested snippet to your pipeline configuration. For example:\n```yaml\n\ninclude:   - component:   gitlab.com/google-gitlab-components/cloud-run/deploy-cloud-run@0.1.0\n\n```\n\nNote that the snippet contains the fully qualified domain name of the component, so if you moved or clone the component to a different location, you should make sure the FQDN is accurate. You can use the $CI_SERVER_FQDN variable instead of hardcoding the FQDN in your pipeline configuration.\n\nA component can be referenced using the following:\n\n- a commit SHA, for example, e3262fdd0914fa823210cdb79a8c421e2cef79d. We highly recommend using this with $CI_COMMIT_SHA variable in your `.gitlab.ci.yml` file to test a component before publishing it to the CI/CD Catalog.\n- a branch name, for example, main\n- a tag, for example 1.0.0\n- shorthand abbreviation 1.0, which will provide you the latest patched 1.0.x version or 1, which will provide you the latest 1.x.x minor version. This is why it is recommended to use the best practices of semantic versioning and always reference a specific version (minor, major, or a specific patch).\n- ~latest, which always points to the latest semantic version published in the CI/CD Catalog. Use ~latest only if you want to use the absolute latest version at all times, which could include breaking changes., so please use it with caution.\n\n## Understanding the CI/CD Catalog across GitLab deployments\nThe CI/CD Catalog and components offer different flavors to cater to various needs and use cases.\n\n### Private and public components\n\n#### Public components\n\n- Public components are hosted in public repositories and are accessible to everyone.\n- When a public component is published from GitLab.com to the main catalog, it becomes discoverable and available for consumption by all users.\n- We encourage users to contribute their best components to the public catalog, helping us build a thriving community.\n\n#### Private components\n\n- Private components are hosted in private repositories.\n- Visibility based on permissions: Users who access the catalog can also see and search for private components if they have permission to view the repository where the component is hosted.\n    - Private catalog option: In GitLab.com, organizations can publish private components to the main catalog in GitLab.com, thereby creating a “private catalog” with content accessible only to authorized users.\n### GitLab.com vs. Self-managed\n- The “public” catalog in GitLab.com: The main catalog is the one that is hosted on GitLab.com and can be accessible to anyone by going to [gitlab.com/explore/catalog](http://gitlab.com/explore/catalog). The CI/CD Catalog is:\n    - Open access: The catalog hosted on GitLab.com is available for anyone to view.\n    - Contribute and grow: By sharing components, users around the world contribute to a growing library of resources that benefits the entire community.\n\n- Self-managed customers: The CI/CD Catalog is also available for self-managed customers however it has several differences:     - Empty catalog: For self-managed customers, the catalog initially appears empty since it doesn't contain any available components.\n    - Organizational catalog: Each organization is responsible for its own catalog, where it can create and maintain its own library of components within this flavor.\n    - Using a component from GitLab.com: If you want to use a component from the main catalog in GitLab.com, clone the project locally and publish it to your organizational catalog. Keep in mind that upstream updates will require mirroring to receive the latest changes. You can learn more about how to do that in our [CI/CD Components documentation](https://docs.gitlab.com/ee/ci/components/#use-a-gitlabcom-component-in-a-self-managed-instance).\n\n## What’s next?\n\nThe CI/CD Catalog is only the first step in revolutionizing the way you build and display your available pipelines. Here is a glimpse of what we plan to offer to our users in the upcoming milestones.\n\n### CI Steps\n\nSteps are reusable and composable pieces of a job that can be referenced in your pipeline configuration. Each step defines structured inputs and outputs that can be consumed by other steps. Steps can come from local files, GitLab.com repositories, or any other Git source.\n\nIn GitLab, we think of steps as another type of component. We are going to make sure CI Steps will become a first-class object in the CI/CD Catalog, where users can publish, unpublish, search, and consume steps in the same way as they are using components today.\n\n### Securing your catalog workflows\n\nWe aim to empower central administrators to manage component creation, usage, and publication within their organizational catalog. We are committed to ensuring the publishing process seamlessly integrates with the organization's standards and existing workflow. We want to enable the platform administrators with the capabilities to secure and govern the CI/CD Catalog and component workflows. More information can be found in [this epic](https://gitlab.com/groups/gitlab-org/-/epics/12713).\n\n### Analytics\n\nOur goal is to empower users with seamless control over component management across pipelines, ensuring optimal version control and project alignment. This addresses the challenge of users currently lacking visibility into component usage across various project pipelines. Our objective is to provide users with the capability to swiftly identify outdated versions and take prompt corrective actions as needed. This enhancement will foster an environment where users can efficiently manage and update components, promoting both version control precision and project alignment. Read more in [this issue](https://gitlab.com/gitlab-org/gitlab/-/issues/393326).\n\n## Get started with the CI/CD Catalog\n\nThe introduction of the CI/CD Catalog revolutionizes pipeline development by offering a vast array of pre-built components. Users don't have to start building pipelines from scratch because the CI/CD Catalog provides an access point to search components and pipeline configurations. The CI/CD Catalog's availability makes accessing and sharing components effortless, fostering collaboration and community growth. Whether utilizing public or private repositories, users can leverage these resources to enhance their pipeline development experience. Moreover, while GitLab.com users benefit from an open-access catalog, self-managed customers can establish organizational catalogs tailored to their needs.\n\n> [Get to know the CI/CD Catalog](https://about.gitlab.com/free-trial/devsecops/) with a free trial of GitLab Ultimate.\n\n> Learn more about the CI/CD Catalog and components:\n> > - [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n>\n> - [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n>\n> - [Documentation: CI/CD components and CI/CD Catalog](https://docs.gitlab.com/ee/ci/components/)\n> > - [Introducing CI/CD components and how to use them in GitLab](https://about.gitlab.com/blog/introducing-ci-components/)\n> \n",[23,24,25,26],"CI/CD","DevSecOps","DevSecOps 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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.",[718],"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/).",[9,723,724],"AI/ML","news",{"featured":31,"template":13,"slug":726},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":728,"config":731},{"title":729,"heroImage":719,"description":730,"date":720,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":31,"template":13,"externalUrl":732},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":734,"config":741},{"title":735,"description":736,"authors":737,"heroImage":719,"date":720,"body":739,"category":9,"tags":740},"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.",[738],"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.",[723,26,9],{"featured":12,"template":13,"slug":742},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":744},[745,759,770,782],{"id":746,"categories":747,"header":749,"text":750,"button":751,"image":756},"ai-modernization",[748],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":752,"config":753},"Get your AI maturity score",{"href":754,"dataGaName":755,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":757},{"src":758},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":760,"categories":761,"header":762,"text":750,"button":763,"image":767},"devops-modernization",[9,38],"Are you just managing tools or shipping innovation?",{"text":764,"config":765},"Get your DevOps maturity score",{"href":766,"dataGaName":755,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":771,"categories":772,"header":774,"text":750,"button":775,"image":779},"security-modernization",[773],"security","Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":755,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":783,"paths":784,"header":787,"text":788,"button":789,"image":794},"github-azure-migration",[785,786],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. 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