[{"data":1,"prerenderedAt":811},["ShallowReactive",2],{"/en-us/blog/gitlab-ux-2020-year-in-review":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Christie Lenneville":702,"blog-related-posts-en-us-gitlab-ux-2020-year-in-review":716,"blog-promotions-en-us":748,"next-steps-en-us":801},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/gitlab-ux-2020-year-in-review.yml","Gitlab Ux 2020 Year In Review",[7],"christie-lenneville",null,"product",{"slug":11,"featured":12,"template":13},"gitlab-ux-2020-year-in-review",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab UX 2020 Year in Review","2020 was a difficult but productive year. Let's take a look back.",[18],"Christie Lenneville","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664102/Blog/Hero%20Images/gitlab-values-cover.png","2020-11-20","\nA global pandemic and broad social unrest have made this year difficult for everyone. When times are as tough as 2020 has proven to be, it's easy to focus on the negative and forget about the many good things that happened along the way. But our product designers, user researchers, and technical writers spend every day doing great work, and we can't let that slip by unnoticed.\n\nIn this post, I want to be intentional about celebrating our successes during a year when many of us wanted to just curl up under a comfy blanket and wait for the turmoil to pass. So, let's take a moment to reflect on some of the things we can feel really proud to have achieved.\n\n## Usability is now a key consideration in our category maturity model\n\nHistorically, we rated the maturity of our product areas fairly subjectively and based almost entirely on feature availability. This year, that changed when we introduced [Category Maturity Scorecards](https://handbook.gitlab.com/handbook/product/ux/category-maturity/category-maturity-scorecards/) that are based on user research. Now, we start by considering the Job to be Done (JTBD) that our users need to accomplish, and we gather user feedback to rate the entire experience -- not just functionality, but usability, too.\n\nWe've learned some amazing things through this new approach, and those learnings have enabled us to make [valuable recommendations](https://gitlab.com/gitlab-org/gitlab/-/issues?label_name%5B%5D=cm-scorecard-rec) to improve our product experience in areas like Code Review, Logging, and Issue Management. We have several additional scorecard initiatives underway, which means that our focus on creating an exceptional experience will only continue to grow.\n\nSo often, UX departments complain that they have to fight for executives to acknowledge the importance of usability on business outcomes. In this case, refining category maturity started as an idea from [Sid](https://gitlab.com/sytses), our CEO. This is honestly amazing! It's the kind of user-centered focus that UX teams get really excited about.\n\nAs the person who leads UX at GitLab, it was awesome for me to watch our cross-functional team immediately get on board. Because measuring product maturity isn't an industry standard, through our value of [Iteration](https://handbook.gitlab.com/handbook/values/#iteration) it took us some time (and a false start) to determine the right approach. Fortunately, Product leadership was both enthusiastic and patient, UX Researchers were persistent in taking feedback and making methodological refinements, and Product Designers were courageous in trying something they've never done before. Even better: Technical Writing has been involved, too, as we've identified documentation improvements that will refine our product maturity.\n\nThis was truly a team effort, and I appreciate everyone who participated. 🤝\n\n## Our design system evolved from an idea into reality\n\nWhen I joined GitLab in early 2019, our design system, [Pajamas](https://design.gitlab.com/), was a scrappy project that the design team was working hard to get off the ground. We had designed a set of 28 single-source-of-truth components and were working hard to build them into [GitLab UI](https://gitlab.com/gitlab-org/gitlab-ui), our Vue-based component library.\nWe now have a robust design library that's implemented in Figma, and a large collection of SSOT Vue components are available to use in the product, too. Even more exciting: We're just finishing with implementing our 8 most impactful components across the entire product UI (buttons, alerts, dropdowns, modals, tabs, popovers, and tooltips), which will result in better performance and consistency when we're done. (We're so close!)\n\nMost amazing to me was watching product designers and technical writers jump in to do much of this component migration work themselves. This was no small feat, because frontend development is not something that many of us are deeply skilled at. But, apparently we're both tenacious and brave, because we did the work anyway (with lots of help from our Frontend Engineers and the awesome documentation that our UX Foundations team created). In the process, we've gotten to know both our product features (which are complex) and our code base (which is also complex) even better, which makes us more effective in our day-to-day jobs.\n\nSpeaking of our UX Foundations team, this is another related success. At the beginning of 2020, we got the budgetary support to create a team that is dedicated solely to maintaining our design system and tooling. The team may be small, but its impact certainly isn't. They've already made some big improvements to things like:\n\n* **Improving tooling for designers:** The move to Figma allows for greater collaboration, as well as community contributions. Sketch is only available on Mac platforms and there are no real-time collaboration features. Figma allows us to provide a UI Kit that is available across platforms, while being available for community contributors to use for free. It also promotes collaboration through its use of real-time editing capabilities and version history. We were able to streamline developer handoff by simply linking to the design file, reducing the need for additional plugins such as Sketch Measure.\n* **Making our color palette consistent and accessible:** We addressed color contrast for accessibility and normalized the palette across hues, so that we can better systematize variable use throughout the UI.\n* **Improving consistency in our icons:** With the creation of our own [SVG Library](http://gitlab-org.gitlab.io/gitlab-svgs/), we've been working to [deprecate our use of Font Awesome](https://gitlab.com/groups/gitlab-org/-/epics/2331) throughout the year. With the help of the Frontend department, we've closed out 156 out of 168 issues related to this effort.\n* **Moving towards more accessible workflows:** Near the end of the year, we've started focusing more on building accessibility standards into our workflows. We are currently auditing and updating our [voluntary product accessibility template](https://design.gitlab.com/accessibility/vpat), as well as [incorporating accessibility audit guides](https://gitlab.com/gitlab-org/gitlab-services/design.gitlab.com/-/merge_requests/2158) into Pajamas.\n\n## Actionable insights\n\nUser research is so incredibly valuable... when you take action on it. But it can be a challenge for research teams to condense their powerful findings into small but compelling insights and then track those insights to determine whether they actually make it into the product.\n\nIn the second half of this year, our user research team made two big strides in this area. First, we started using [Dovetail](https://dovetailapp.com/) to help us more easily analyze research data to find meangingful insights and share it collaboratively with Product Managers and Product Designers (and anyone else who may be interested). But, they took this a step farther by also beginning to [track actionable insights](https://handbook.gitlab.com/handbook/product/ux/performance-indicators/#actionable-insights) as a performance indicator.\n\nThe considerable effort it took to get both of these programs in place will be worth it as we watch our research efforts result in an even better product.\n\n## Beautifying our docs\n\nComplex products like GitLab require high-quality documentation. Some things you just can't (and shouldn't) communicate through the UI, so users rely on great docs to get their daily jobs done.\n\nOur Technical Writing team (many of whom have been with GitLab less than a year) worked hard to improve our docs site during 2020, including:\n\n- Several UX research projects to discover - and fix! - problems users encounter when using the docs site.\n- A \"Beautification\" effort that focused on an updated visual design. Our 2020 GitLab Contribute event included many rapid improvements to the docs site, and we made many more afterward. (Did you notice?)\n- Ongoing content improvements, including making our docs more consistent, findable, detailed, and easier to read.\n- Adding (a lot of) metadata information to product docs to help connect content contributors with Technical Writers.\n- Coding innovations for automation, such as grammar checking with Vale, a linter, to automatically catch errors before they’re merged.\n\nWe’ve also completed work on a Docs Strategy roadmap to drive even more improvements in the upcoming months.\n\n## And so much more...\n\n* GitLab Design Talks: In this fun video series, watch designers, technical writers, researchers, and product managers talk about [Iteration](https://www.youtube.com/playlist?list=PL05JrBw4t0KpgzLWbRCXf8o7iap-uoe7o) and [Collaboration](https://www.youtube.com/playlist?list=PL05JrBw4t0KrER807JktsL-addVZa4N0-) at GitLab. (Special thanks to host [Nick Post](https://gitlab.com/npost)!)\n* UX Showcase: See [100+ videos](https://www.youtube.com/playlist?list=PL05JrBw4t0Kq89nFXtkVviaIfYQPptwJz) highlighting exciting UX work happening across GitLab. I learn something new everytime I watch one of these.\n* Blog posts: Read about a variety of topics we were thinking about in 2020, including:\n    * [Designing in an all-remote company](https://about.gitlab.com/blog/designing-in-an-all-remote-company/)\n    * [Running an asynchronous sketching workshop for UX](https://about.gitlab.com/blog/async-sketching/)\n    * [Synchronous collaboration as a remote designer at GitLab](https://about.gitlab.com/blog/synchronous-collaboration-as-a-remote-designer-at-gitlab/)\n    * [A tale of two file editors](https://about.gitlab.com/blog/a-tale-of-two-editors/)\n    * [How holistic UX design increased GitLab.com free trial signups](https://about.gitlab.com/blog/how-holistic-ux-design-increased-gitlab-free-trial-signups/)\n    * [Improving iteration and collaboration with user stories](https://about.gitlab.com/blog/how-we-utilize-user-stories-as-a-collaborative-design-tool/)\n    * [Designing incident management from scratch](https://about.gitlab.com/blog/designing-alerts-and-incidents/)\n    * [Why GitLab is the right design collaboration tool for the entire team ](https://about.gitlab.com/blog/why-gitlab-is-the-right-design-collaboration-tool-for-the-whole-team/)\n\nAgain, the GitLab UX team does amazing work every single day, and there is no way to capture all of that effort in a single blog post. As this year wraps up, I hope you personally take time to think about your own successes and the impact they had on our fast-moving company.\n\nI also hope you know that we value every one of you. You are appreciated. 💜\n\n{::options parse_block_html=\"true\" /}\n\n\u003Cdiv class=\"panel panel-gitlab-purple\">\n  \u003Cp class=\"panel-heading\">\u003Cstrong>One more thing...\u003C/strong>\u003C/p>\n\u003Cdiv class=\"panel-body\">\n\n\u003Cp>The final 2020 highlight I wanted to ensure is here was Christie Lenneville's own promotion to be GitLab's first \u003Cstrong>Vice President of User Experience (UX)\u003C/strong>. I knew that as both the author of this article, and as a humble (and great) leader she'd be hesitant to add this herself. But it's not only a recognition of her achievements and her potential. VP-level leadership of UX at GitLab should \u003Ci>also\u003C/i> be a signal of how important UX is to our organization and to our community. And it should indicate that usability is an important differentiator for GitLab, and a critical part of our company's strategy. Congratulations again, Christie!\u003C/p>\n\n— Eric Johnson, Chief Technology Officer\n\n\u003C/div>\n\u003C/div>\n\n{::options parse_block_html=\"false\" /}\n",[23,24,25,26],"UX","design","inside GitLab","research","yml",{},true,"/en-us/blog/gitlab-ux-2020-year-in-review",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"https://about.gitlab.com/blog/gitlab-ux-2020-year-in-review","https://about.gitlab.com","article","en-us/blog/gitlab-ux-2020-year-in-review",[37,24,38,26],"ux","inside-gitlab","l0rzx_PhvzIb3GXitwFM7VEiMqZAMpO_IwhNXelEhXg",{"data":41},{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":370,"minimal":401,"duo":420,"switchNav":429,"pricingDeployment":440},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab logo","header",{"text":48,"config":49},"Get free 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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.",[722],"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/).",[9,727,728],"AI/ML","news",{"featured":12,"template":13,"slug":730},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":732,"config":735},{"title":733,"heroImage":723,"description":734,"date":724,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":12,"template":13,"externalUrl":736},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":738,"config":746},{"title":739,"description":740,"authors":741,"heroImage":723,"date":724,"body":743,"category":9,"tags":744},"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.",[742],"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.",[727,745,9],"features",{"featured":29,"template":13,"slug":747},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":749},[750,764,775,787],{"id":751,"categories":752,"header":754,"text":755,"button":756,"image":761},"ai-modernization",[753],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":757,"config":758},"Get your AI maturity score",{"href":759,"dataGaName":760,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":762},{"src":763},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":765,"categories":766,"header":767,"text":755,"button":768,"image":772},"devops-modernization",[9,570],"Are you just managing tools or shipping innovation?",{"text":769,"config":770},"Get your DevOps maturity score",{"href":771,"dataGaName":760,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":776,"categories":777,"header":779,"text":755,"button":780,"image":784},"security-modernization",[778],"security","Are you trading speed for security?",{"text":781,"config":782},"Get your security maturity score",{"href":783,"dataGaName":760,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":785},{"src":786},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":788,"paths":789,"header":792,"text":793,"button":794,"image":799},"github-azure-migration",[790,791],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":795,"config":796},"See how GitLab compares to GitHub",{"href":797,"dataGaName":798,"dataGaLocation":244},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":800},{"src":774},{"header":802,"blurb":803,"button":804,"secondaryButton":809},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":805,"config":806},"Get your free trial",{"href":807,"dataGaName":51,"dataGaLocation":808},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":810},{"href":55,"dataGaName":56,"dataGaLocation":808},1776452966763]