[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/gitlab-18-10-agentic-ai-now-open-to-even-more-teams-on-gitlab":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Talia Armato-Helle":695,"blog-related-posts-en-us-gitlab-18-10-agentic-ai-now-open-to-even-more-teams-on-gitlab":710,"assessment-promotions-en-us":741,"next-steps-en-us":780},{"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/gitlab-18-10-agentic-ai-now-open-to-even-more-teams-on-gitlab.yml","Gitlab 18 10 Agentic Ai Now Open To Even More Teams On Gitlab",[7],"talia-armato-helle",null,"product",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-18-10-agentic-ai-now-open-to-even-more-teams-on-gitlab",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab 18.10: Agentic AI now open to even more teams on GitLab","Free GitLab.com teams can purchase GitLab Credits and start using AI agents and workflows, including flat-rate automated code review.",[18],"Talia Armato-Helle","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773843921/rm35fx4gylrsu9alf2fx.png","2026-03-19","Agentic AI is changing how software gets built. But for many teams, especially small and midsize ones, the path to adopting it has felt like an all-or-nothing decision: commit to a full platform subscription, or don't use AI at all.\n\nThat changes with GitLab 18.10. Starting today, Free GitLab.com teams can purchase a monthly commitment of [GitLab Credits](https://docs.gitlab.com/subscriptions/gitlab_credits/) and start using [GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/) immediately. No subscription upgrade required. This is a full entry point to agentic AI for teams that aren’t ready to add a GitLab plan, but are ready to start building with AI.\n\nThe model is simple: pay for what AI does, not how many people use it. Your group owner purchases a monthly credit commitment of GitLab Credits through your group’s billing settings. Your entire team gets access to the same AI agents and flows that GitLab Premium and Ultimate customers use for planning, code generation, automated code review, and pipeline diagnosis, all drawing from a shared credit pool.\n\nThe [GitLab Credits dashboard](https://docs.gitlab.com/subscriptions/gitlab_credits/#gitlab-credits-dashboard) gives group owners visibility into which agents and flows are consuming credits, so you can connect AI spend directly to the work it's producing.\n\n![GitLab Credits dashboard showing a monthly committed pool of 50 credits with usage tracking, on-demand credit consumption, and per-user credit allocation for Duo Agent Platform.](https://res.cloudinary.com/about-gitlab-com/image/upload/v1773867549/jdrzquwptvjnbr7eqd56.png)\n\n## Day Zero with GitLab Duo Agent Platform\n\nOnce your group owner purchases credits, every member of the team can start using GitLab Duo Agent Platform right away.\n\nHere's how a typical workflow plays out:\n\nYou start with a feature request for your software. Open [Planner Agent](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/planner/) in Agentic Chat and describe what you need in plain language. The agent breaks it down into structured work items: issues with descriptions, labels, and relationships. They are all created directly in your project. What used to be an afternoon of manual issue grooming takes minutes.\n\nPick up one of those issues and assign [Developer Flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/developer/) to begin work. The agent reads the issue context, generates code aligned to the requirements, runs tests, and opens a merge request for review. You can also use [Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/) for more iterative work, whether you’re refactoring, extending, or explaining code within your project's context.\n\nWhen the merge request is ready, [Code Review Flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/) runs a multi-step automated review: scanning the changes, pulling in repository context, and posting structured inline feedback tied to the diff. Your human reviewers can skip the first-pass mechanics and focus on architecture and business logic.\n\nAnd if the pipeline breaks, [Fix CI/CD Pipeline Flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/fix_pipeline/) reads the failure logs, traces the error to its root cause, and proposes a fix. Instead of manually stepping through job logs, your team gets a starting point for resolution.\n\nGitLab’s Duo Agent Platform takes software development from iteration to deployment, powered by one credit pool.\n\nIt's simple to get started with agents and flows, from plan to deploy in under 3 minutes. Watch this demo to learn how:\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1175244743?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=\"18.10 Main Demo V2\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Flat-rate code review: Predictable costs as you scale\n\nOf all the workflows available through GitLab Duo Agent Platform, automated code review is where teams see value the fastest, and where predictable pricing matters most.\n\nCode Review Flow now costs a flat 0.25 GitLab Credits per review, regardless of merge request size, repository complexity, or how many steps the flow runs internally. Four reviews equal one credit. Whether your team merges 500 requests a month or 50,000, you can forecast costs directly in terms of reviews.\n\nThe math is worth a closer look. Manual code reviews don’t just cost money, they take time and add disruption in development with constant context-switching. The time saved with the Code Review Flow could mean substantial savings as review volume grows. Now you have the potential to run hundreds of reviews simultaneously rather than having them sit in a queue. That means the time savings quickly compound with the cost savings.\n\nFor teams on GitLab’s Free tier, this means you know exactly what portion of your monthly credit pool goes to code review and can plan accordingly.\n\n> Learn more about [how Code Review Flow works](https://about.gitlab.com/blog/code-review-without-the-bottlenecks-or-the-bill/) and what it means for scaling your engineering organization.\n\n## Why Premium multiplies the value\n\nGitLab Credits on the Free tier give your team a direct path to agentic AI. If your team relies on GitLab for more, Premium is where the economics and the capabilities come together.\n\nAt $29 per user per month, [GitLab Premium](https://about.gitlab.com/pricing/) includes 12 GitLab Credits per user as a promotional offer. For a team of 20, that's 240 credits per month before you spend anything extra, enough to cover roughly 960 automated code reviews, or a mix of code review, planning, development flows, and pipeline fixes.\n\nGitLab Duo Agent Platform is only a part of what Premium gives you. You also get advanced CI/CD for high-volume pipelines, merge approvals and code owners for governance, and AI that runs within a single data layer with unified context across your projects.\n\nIf your team is spending credits on Free and finding that AI is becoming central to your workflow, Premium is the natural next step with the included promotional credits. It offers more platform capability, and a foundation that scales with you.\n\n## Get started today\n\nGitLab 18.10 is available now. Whether your team needs agentic AI to move faster or the full platform to support how you already work, there's a clear path for accelerating your software development process.\n\n* **Free GitLab.com teams:** [Purchase a monthly commitment of GitLab Credits](https://docs.gitlab.com/subscriptions/gitlab_credits/#for-the-free-tier-on-gitlabcom) through your group billing settings and start using GitLab Duo Agent Platform today.\n\n* **Teams ready for the full platform:** [Find the right GitLab subscription for your team](https://docs.gitlab.com/subscriptions/choosing_subscription/), or [start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n\nGetting credits set up for your team is fast and easy. Watch this demo to learn how:\n\u003Ciframe src=\"https://player.vimeo.com/video/1175238100?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 Credits Purchase Flow\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n---\n\n## FAQ\n\n**What is a monthly commitment of GitLab Credits?**\n\nA monthly commitment is a usage-based purchase option where your group owner selects a set number of credits that apply as a shared pool across the group. Credits are consumed when your team uses GitLab Duo Agent Platform capabilities. Learn more in the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/).\n\n**Who can purchase GitLab Credits today?**\n\nGitLab Premium and Ultimate customers already receive promotional credits included with their subscription. Starting in 18.10, Free GitLab.com top-level group namespaces can also purchase a monthly commitment of credits through self-serve group billing. For the latest on eligibility, see the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/).\n\n**What AI capabilities do credits unlock on Free?**\n\nTeams with credits get access to the same agentic AI capabilities and models available to Premium and Ultimate customers, including Planner Agent, Developer Flow, Code Review Flow, Fix CI/CD Pipeline Flow, Agentic Chat, Code Suggestions, custom agents and flows, and more. For the full list, see the [Duo Agent Platform documentation](https://docs.gitlab.com/user/duo_agent_platform/).\n\n**How much does automated code review cost?**\n\nCode Review Flow charges a flat rate of 0.25 GitLab Credits per review, regardless of merge request size or complexity. For current pricing details, see the [Code Review Flow documentation](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/).\n\n**Can I upgrade from Free with credits to GitLab Premium?**\n\nIn GitLab 18.10, upgrading from a Free namespace with a monthly credit commitment to Premium is available through a sales-assisted path. Contact the [GitLab sales team](https://about.gitlab.com/contact-sales/) to explore your options.\n\n\n",[23,9],"features","yml",{},"/en-us/blog/gitlab-18-10-agentic-ai-now-open-to-even-more-teams-on-gitlab",{"config":28,"title":15,"description":16},{"noIndex":29},false,"en-us/blog/gitlab-18-10-agentic-ai-now-open-to-even-more-teams-on-gitlab",[23,9],"pPTh9vz1pA3cT_oaH0G6yaAUD6X8T1K4_QG6AaZ6B_s",{"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 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Helle",{"template":700,"gitlabHandle":701},"BlogAuthor","thelle",{"name":18,"config":703},{"headshot":704},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772021433/wop3fdef9erx2srfow52.jpg",{},"/en-us/blog/authors/talia-armato-helle",{},"en-us/blog/authors/talia-armato-helle","ny-XdkWLfw_DdxxdRMKKeqHEnL2O9C66pk14uBQivFE",[711,725,731],{"content":712,"config":723},{"title":713,"description":714,"authors":715,"heroImage":717,"date":718,"body":719,"category":9,"tags":720},"GitLab 18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[716],"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/).",[9,721,722],"AI/ML","news",{"featured":29,"template":12,"slug":724},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":726,"config":729},{"title":727,"heroImage":717,"description":728,"date":718,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":29,"template":12,"externalUrl":730},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":732,"config":739},{"title":733,"description":734,"authors":735,"heroImage":717,"date":718,"body":737,"category":9,"tags":738},"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.",[736],"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.",[721,23,9],{"featured":11,"template":12,"slug":740},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":742},[743,757,768],{"id":744,"categories":745,"header":747,"text":748,"button":749,"image":754},"ai-modernization",[746],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":750,"config":751},"Get your AI maturity score",{"href":752,"dataGaName":753,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":755},{"src":756},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":758,"categories":759,"header":760,"text":748,"button":761,"image":765},"devops-modernization",[9,563],"Are you just managing tools or shipping innovation?",{"text":762,"config":763},"Get your DevOps maturity score",{"href":764,"dataGaName":753,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":769,"categories":770,"header":772,"text":748,"button":773,"image":777},"security-modernization",[771],"security","Are you trading speed for security?",{"text":774,"config":775},"Get your security maturity score",{"href":776,"dataGaName":753,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":781,"blurb":782,"button":783,"secondaryButton":788},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":784,"config":785},"Get your free trial",{"href":786,"dataGaName":44,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":789},{"href":48,"dataGaName":49,"dataGaLocation":787},1776438088647]