[{"data":1,"prerenderedAt":808},["ShallowReactive",2],{"/en-us/blog/automating-agile-workflows-with-the-gitlab-triage-gem":3,"navigation-en-us":40,"banner-en-us":449,"footer-en-us":459,"blog-post-authors-en-us-GitLab":699,"blog-related-posts-en-us-automating-agile-workflows-with-the-gitlab-triage-gem":713,"blog-promotions-en-us":745,"next-steps-en-us":798},{"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/automating-agile-workflows-with-the-gitlab-triage-gem.yml","Automating Agile Workflows With The Gitlab Triage Gem",[7],"gitlab",null,"product",{"slug":11,"featured":12,"template":13},"automating-agile-workflows-with-the-gitlab-triage-gem",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Automating Agile workflows with the gitlab-triage gem","Learn how to automate repetitive tasks like triaging issues and merge requests to free up valuable developer time in our \"Getting Started with GitLab\" series.",[18],"GitLab","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659525/Blog/Hero%20Images/blog-getting-started-with-gitlab-banner-0497-option4-fy25.png","2025-03-13","*Welcome to our \"Getting started with GitLab\" series, where we help newcomers get familiar with the GitLab DevSecOps platform.*\n\nThis post dives into the [`gitlab-triage`](https://gitlab.com/gitlab-org/ruby/gems/gitlab-triage) gem, a powerful tool that lets you create bots to automate your Agile workflow. Say goodbye to manual tasks and hello to streamlined efficiency.\n\n## Why automate your workflow?\n\nEfficiency is key in software development. Automating repetitive tasks like triaging issues and merge requests frees up valuable time for your team to focus on what matters most: building amazing software.\n\nWith `gitlab-triage`, you can:\n\n* **Ensure consistency:** Apply labels and assign issues automatically based on predefined rules.  \n* **Improve response times:** Get immediate feedback on new issues and merge requests.  \n* **Reduce manual effort:** Eliminate the need for manual triage and updates.  \n* **Boost productivity:** Free up your team to focus on coding and innovation.\n\n## Introducing the `gitlab-triage` gem\n\nThe `gitlab-triage` gem is a Ruby library that allows you to create bots that interact with your GitLab projects. These bots can automatically perform a wide range of actions, including:\n\n* **Labeling:** Automatically categorize issues and merge requests.  \n* **Commenting:** Provide updates, request information, or give feedback.  \n* **Assigning:** Assign issues and merge requests to the appropriate team members.  \n* **Closing:** Close stale or resolved issues and merge requests.  \n* **Creating:** Generate new issues based on specific events or conditions.  \n* **And much more!**\n\nCheck out the [`gitlab-triage` gem repository](https://gitlab.com/gitlab-org/ruby/gems/gitlab-triage). \n\n## Setting up your triage bot\n\nLet's get your first triage bot up and running!\n\n1. Install the gem. (Note: The gem command is available with Ruby programming language installed.)\n\n```bash\ngem install gitlab-triage\n```\n\n2. Get your GitLab API token.\n\n* Go to your GitLab [profile settings](https://gitlab.com/-/profile/preferences).  \n* Navigate to **Access Tokens**.  \n* Create a new token with the `api` scope.  \n* **Keep your token secure and set an expiration date for it based on when you will be done with this walkthrough!**\n\n3. Define your triage policies.\n\nCreate a file named `.triage-policies.yml` in your project's root directory. This file will contain the rules that govern your bot's behavior. Here's a simple example:\n\n```yaml\n---\n- name: \"Apply 'WIP' label\"\n  condition:\n    draft: true\n  action:\n    labels:\n      - status::wip\n\n- name: \"Request more information on old issue\"\n  condition:\n   date:\n    attribute: updated_at\n    condition: older_than\n    interval_type: months\n    interval: 12\n  action:\n    comment: |\n      {{author}} This issue has been open for more than 12 months, is this still an issue?\n\n```\n\nThis configuration defines two policies:\n\n* The first policy applies the `status::wip` label to any issue that is in draft.  \n* The second policy adds a comment to an issue that the issue has not been updated in 12 months.\n\n4. Run your bot.\n\nYou can run your bot manually using the following command:\n\n```bash\ngitlab-triage -t \u003Cyour_api_token> -p \u003Cyour_project_id>\n```\n\nReplace `\u003Cyour_api_token>` with your GitLab API token and `\u003Cyour_project_id>` with the [ID of your GitLab project](https://docs.gitlab.com/user/project/working_with_projects/#access-a-project-by-using-the-project-id). If you would like to see the impact of actions before they are taken, you can add the `-n` or `--dry-run` to test out the policies first.\n\n## Automating with GitLab CI/CD\n\nTo automate the execution of your triage bot, integrate it with [GitLab CI/CD](https://about.gitlab.com/blog/ultimate-guide-to-ci-cd-fundamentals-to-advanced-implementation/). Here's an example `.gitlab-ci.yml` configuration:\n\n```yaml\ntriage:\n  script:\n    - gem install gitlab-triage\n    - gitlab-triage -t $GITLAB_TOKEN -p $CI_PROJECT_ID\n  only:\n    - schedules\n\n```\n\nThis configuration defines a job named \"triage\" that installs the `gitlab-triage` gem and runs the bot using the `$GITLAB_TOKEN` (a predefined [CI/CD variable](https://docs.gitlab.com/ci/variables/)) and the `$CI_PROJECT_ID` variable. The `only: schedules` clause ensures that the job runs only on a schedule.\n\nTo create a [schedule](https://docs.gitlab.com/ee/ci/pipelines/schedules.html), go to your project's **CI/CD** settings and navigate to **Schedules**. Create a new schedule and define the frequency at which you want your bot to run (e.g., daily, hourly).\n\n## Advanced triage policies\n\n`gitlab-triage` offers a range of advanced features for creating more complex triage policies:\n\n* **Regular expressions:** Use regular expressions for more powerful pattern matching.  \n* **Summary policies:** Consolidate related issues into a single summary issue.  \n* **Custom actions:** Define custom actions using [Ruby code blocks](https://gitlab.com/gitlab-org/ruby/gems/gitlab-triage#can-i-customize) to perform more complex operations using the GitLab API.\n\nHere are two advanced real-world examples from the triage bot used by the Developer Advocacy team at GitLab. You can view the full policies in [this file](https://gitlab.com/gitlab-da/projects/devrel-bot/-/blob/master/.triage-policies.yml?ref_type=heads).\n\n```yaml\n- name: Issues where DA team member is an assignee outside DA-Meta project i.e. DevRel-Influenced\n  conditions:\n    assignee_member:\n      source: group\n      condition: member_of\n      source_id: 1008\n    state: opened\n    ruby: get_project_id != 18 \n    forbidden_labels:\n      - developer-advocacy\n  actions:   \n    labels:\n      - developer-advocacy\n      - DevRel-Influenced\n      - DA-Bot::Skip\n\n```\n\nThis example for issues across a group, excluding those in the project with the ID of 18, have assignees who are members of the group with ID of 1008 and do not have the label `developer-advocacy` on them. This policy helps the Developer Advocacy team at GitLab to find issues members of the team are assigned to but are not in their team’s project. This helps the team identify and keep track of contributions made outside of the team by adding the teams’ labels.\n\n```text\n- name: Missing Due Dates\n  conditions:\n    ruby: missing_due_date\n    state: opened\n    labels:\n      - developer-advocacy\n    forbidden_labels:\n      - DA-Due::N/A\n      - DA-Bot::Skip\n      - DA-Status::FYI\n      - DA-Status::OnHold\n      - CFP\n      - DA-Bot::Triage\n  actions:\n    labels:\n      - DA-Bot-Auto-Due-Date\n    comment: |\n      /due #{get_current_quarter_last_date}\n\n```\n\nThis second example checks for all issues with the `developer-advocacy` label, which do not include labels in the forbidden labels list and when their due dates have passed. It updates the due dates automatically by commenting on the issue with a slash command and a date that is generated using Ruby.\n\nThe Ruby scripts used in the policies are defined in a separate file as shown below. This feature allows you to be flexible in working with your filters and actions. You can see functions are created for different Ruby commands that we used in our policies. \n\n```text\nrequire 'json'\nrequire 'date'\nrequire \"faraday\"\nrequire 'dotenv/load'\n\nmodule DATriagePlugin\n  def last_comment_at\n    conn = Faraday.new(\n      url: notes_url+\"?sort=desc&order_by=created_at&pagination=keyset&per_page=1\",\n      headers: {'PRIVATE-TOKEN' => ENV.fetch(\"PRIV_KEY\"), 'Content-Type' => 'application/json' }\n    )\n\n    response = conn.get()\n    if response.status == 200\n      jsonData = JSON.parse(response.body)\n      if jsonData.length > 0\n        Date.parse(jsonData[0]['created_at'])\n      else\n        Date.parse(resource[:created_at])\n      end\n    else\n      Date.parse(resource[:created_at])\n    end\n  end\n\n  def notes_url\n    resource[:_links][:notes]\n  end\n\n  def get_project_id\n    resource[:project_id]\n  end\n\n  def get_current_quarter_last_date()\n    yr = Time.now.year\n    case Time.now.month\n    when 2..4\n      lm = 4\n    when 5..7\n      lm = 7\n    when 8..10\n      lm = 10\n    when 11..12\n      lm = 1\n      yr = yr + 1\n    else\n      lm = 1    \n    end\n\n    return Date.new(yr, lm, -1) \n  end\n\n  def one_week_to_due_date\n    if(resource[:due_date] == nil)\n      false\n    else\n      days_to_due = (Date.parse(resource[:due_date]) - Date.today).to_i\n      if(days_to_due > 0 && days_to_due \u003C 7)\n        true\n      else\n        false\n      end\n    end\n  end\n\n  def due_date_past\n    if(resource[:due_date] == nil)\n      false\n    else\n      Date.today > Date.parse(resource[:due_date])\n    end\n  end\n\n  def missing_due_date\n    if(resource[:due_date] == nil)\n      true\n    else\n      false\n    end\n  end\n\nend\n\nGitlab::Triage::Resource::Context.include DATriagePlugin\n```\nThe triage bot is executed using the command:\n\n```text\n`gitlab-triage -r ./triage_bot/issue_triage_plugin.rb --debug --token $PRIV_KEY --source-id gitlab-com --source groups`  \n```\n\n- `-r`: Passes in a  file of requirements for the performing triage. In this case we are passing in our Ruby functions.  \n- `--debug`: Prints debugging information as part of the output.  \n- `--token`: Is used to pass in a valid GitLab API token.  \n- `--source`: Specifies if the sources of the issues it will search is within a group or a project.  \n- `--source-id`: Takes in the ID of the selected source type – in this case, a group.\n\nThe GitLab [triage-ops](https://gitlab.com/gitlab-org/quality/triage-ops) project is another real-world example that is more complex and you can learn how to build your own triage bot.\n\n## Best practices\n\n* **Start simple:** Begin with basic policies and gradually increase complexity as needed. \n* **Test thoroughly:** Test your policies in a staging environment before deploying them to production.  \n* **Monitor regularly:** Monitor your bot's activity to ensure it's behaving as expected. \n* **Use descriptive names:** Give your policies clear and descriptive names for easy maintenance. \n* **Be mindful of the scope of your filters:** You might be tempted to filter issues across groups where thousands of issues exist. However, this can slow down the triage and also make the process fail due to rate limitations against the GitLab API.  \n* **Prioritize using labels for triages:** To avoid spamming other users, labels are a good way to perform triages without cluttering comments and issues.\n\n## Take control of your workflow\n\nWith the `gitlab-triage` gem, you can automate your GitLab workflow and unlock new levels of efficiency. Start by creating simple triage bots and gradually explore the more advanced features. You'll be amazed at how much time and effort you can save\\!\n\n> #### Want to take your learning to the next level? [Sign up for GitLab University courses](https://university.gitlab.com/). Or you can get going right away with a [free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n\n## \"Getting started with GitLab\" series\nRead more articles in our \"Getting started with GitLab\" series:\n\n- [How to manage users](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-manage-users/)\n- [How to import your projects to GitLab](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-import-your-projects-to-gitlab/)  \n- [Mastering project management](https://about.gitlab.com/blog/getting-started-with-gitlab-mastering-project-management/)\n- [Understanding CI/CD](https://about.gitlab.com/blog/getting-started-with-gitlab-understanding-ci-cd/)\n- [Working with CI/CD variables](https://about.gitlab.com/blog/getting-started-with-gitlab-working-with-ci-cd-variables/)\n",[23,24,9,25,26],"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.",[719],"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/).",[9,724,725],"AI/ML","news",{"featured":12,"template":13,"slug":727},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":729,"config":732},{"title":730,"heroImage":720,"description":731,"date":721,"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":733},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":735,"config":743},{"title":736,"description":737,"authors":738,"heroImage":720,"date":721,"body":740,"category":9,"tags":741},"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.",[739],"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.",[724,742,9],"features",{"featured":29,"template":13,"slug":744},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":746},[747,761,772,784],{"id":748,"categories":749,"header":751,"text":752,"button":753,"image":758},"ai-modernization",[750],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":754,"config":755},"Get your AI maturity score",{"href":756,"dataGaName":757,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":759},{"src":760},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":762,"categories":763,"header":764,"text":752,"button":765,"image":769},"devops-modernization",[9,567],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":776,"text":752,"button":777,"image":781},"security-modernization",[775],"security","Are you trading speed for security?",{"text":778,"config":779},"Get your security maturity score",{"href":780,"dataGaName":757,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":782},{"src":783},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":785,"paths":786,"header":789,"text":790,"button":791,"image":796},"github-azure-migration",[787,788],"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":792,"config":793},"See how GitLab compares to GitHub",{"href":794,"dataGaName":795,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":797},{"src":771},{"header":799,"blurb":800,"button":801,"secondaryButton":806},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":802,"config":803},"Get your free trial",{"href":804,"dataGaName":51,"dataGaLocation":805},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":505,"config":807},{"href":55,"dataGaName":56,"dataGaLocation":805},1776444473438]