[{"data":1,"prerenderedAt":808},["ShallowReactive",2],{"/en-us/blog/contributions-to-git-2-42-release":3,"navigation-en-us":39,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Christian Couder":700,"blog-related-posts-en-us-contributions-to-git-2-42-release":714,"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__":38},"blogPosts/en-us/blog/contributions-to-git-2-42-release.yml","Contributions To Git 2 42 Release",[7],"christian-couder",null,"product",{"slug":11,"featured":12,"template":13},"contributions-to-git-2-42-release",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Git 2.42 release: Here are four of our contributions in detail","Find out how GitLab's Git team helped improve Git 2.42.",[18],"Christian Couder","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667792/Blog/Hero%20Images/git-241.jpg","2023-10-12","[Git 2.42](https://gitlab.com/gitlab-org/git/-/raw/master/Documentation/RelNotes/2.42.0.txt)\nwas officially released on August 21, 2023, and included some\nimprovements from GitLab's Git team. Git is the foundation of\nrepository data at GitLab. GitLab's Git team works on new features, performance improvements, documentation improvements,\nand growing the Git community. Often our contributions to Git have a\nlot to do with the way we integrate Git into our services at\nGitLab.\n\nWe previously shared [some of our improvements that were included in the Git 2.41 release](https://about.gitlab.com/blog/contributions-to-latest-git-release/). Here are some highlights from the Git 2.42 release, and a\nwindow into how we use Git on the server side at GitLab.\n\n## 1. Prevent certain refs from being packed\n\n### Write-ahead logging\nIn [Gitaly](https://docs.gitlab.com/ee/administration/gitaly/), we\nwant to use a [write-ahead log](https://gitlab.com/groups/gitlab-org/-/epics/8911)\nto replicate Git operations on different machines.\n\nThis means that the Git objects and references that should be changed\nby a Git operation are first kept in a log entry. Then, when all the\nmachines have agreed that the operation should proceed, the log entry\nis applied so the corresponding Git objects and references are\nactually added to the repositories on all the machines.\n\n### Need for temporary references\nBetween the time when a specific log entry is first written and when\nit is applied, other log entries could be applied which could remove\nsome objects and references. It could happen that these objects and\nreferences are needed to apply the specific log entry though.\n\nSo when we log an entry, we have to make sure that all the objects and\nreferences that it needs to be properly applied will not be removed\nuntil that entry is either actually applied or discarded.\n\nThe best way to make sure things are kept in Git is to create new Git\nreferences pointing to these things. So we decided to use temporary\nreferences for that purpose. They would be created when a log entry is\nwritten, and then deleted when that entry is either applied or\ndiscarded.\n\n### Packed-refs performance\nGit can store references in \"loose\" files, with one reference per\nfile, or in the `packed-refs` file, which contains many of them. The\n`git pack-refs` command is used to pack some references from \"loose\"\nfiles into the `packed-refs` file.\n\nFor reading a lot of references, the `packed-refs` file is very\nefficient, but for writing or deleting a single reference, it is not\nso efficient as rewriting the whole `packed-refs` file is required.\n\nAs temporary references are to be created and then deleted soon after,\nstoring them in the `packed-refs` file would not be efficient. It\nwould be better to store them in \"loose\" files.\n\nThe `git pack-refs` command had no way to be told precisely which refs\nshould be packed or not though. By default it would repack all the\ntags (which are refs in `refs/tags/`) and all the refs that are\nalready packed. With the `--all` option one could tell it to repack\nall the refs except the hidden refs, broken refs, and symbolic refs,\nbut that was the only thing that could be controlled.\n\n### Improving `git pack-refs`\nWe decided to improve `git pack-refs` by adding two new options to it:\n  - `--include \u003Cpattern>` which can be used to specify which refs should be packed\n  - `--exclude \u003Cpattern>` which can be used to specify which refs should not be packed\n\n[John Cai](https://gitlab.com/jcaigitlab), Gitaly:Git team engineering manager, implemented these options.\n\nFor example, if the refs managed by the write-ahead log are in\n`refs/wal/`, it's now possible the exclude them from being moved into\nthe `packed-refs` file by using:\n\n```shell\n$ git pack-refs --exclude \"refs/wal/*\"\n```\n\nDetails of the patch series, including discussions, can be found\n[here](https://lore.kernel.org/git/pull.1501.git.git.1683215331910.gitgitgadget@gmail.com/).\n\n## 2. Get machine-readable output from `git cat-file --batch`\n\n### Efficiently retrieving Git object information\nIn GitLab, we often retrieve Git object information. For example, when a\nuser navigates into the files and directories in a repository, we need\nto get the content of the corresponding Git blobs and trees so that\nwe can show it.\n\nIn Gitaly, we use `git cat-file` to retrieve Git object information\nfrom a Git repository. As it's a frequent operation, it needs to be\nperformed efficiently, so we use the batch modes of `git cat-file`\navailable through the `--batch`, `--batch-check` and `--batch-command`\noptions.\n\nIn these modes, a pointer to a Git object can be repeatedly sent to\nthe standard input, called 'stdin', of a `git cat-file` command, while\nthe corresponding object information is read from the standard ouput,\ncalled 'stdout' of the command. This way we don't need to launch a\nnew `git cat-file` command for each object.\n\nGitLab can keep, for example, a `git cat-file --batch-command` process\nrunning in the background while feeding it commands like\n`info \u003Cobject>` or `contents \u003Cobject>` through its stdin to\nget either information about an object or its content.\n\n### Newlines in stdin, stdout, and filenames\nThe commands or pointers to Git objects that are sent through stdin\nshould be delimited using newline characters, and in the same way `git\ncat-file` will use newline characters to delimit the information from\ndifferent Git objects in its output. This is a common shell practice\nto make it easy to chain commands together. For example, one can\neasily get the size (in bytes) of the last three commits on the current\nbranch using the following:\n\n```shell\n$ git log -3 --format='%H' | git cat-file --batch-check='%(objectsize)'\n285\n646\n428\n```\n\nSometimes, though, the pointer to a Git object can contain a filename\nor a directory name, as such a pointer is allowed to be in the form\n`\u003Cbranch>:\u003Cpath>`. For example `HEAD:Documentation` is a valid\npointer to the blob or the tree corresponding to the `Documentation`\npath on the current branch.\n\nThis used to be an issue because on some systems newline characters\nare allowed in file or directory names. So the `-z` option was\nintroduced last year in Git 2.38 to allow users to change the input\ndelimiter in batch modes to the NUL character.\n\n### Error output\nWhen the `-z` option was introduced, it wasn't considered useful to\nchange the output delimiter to be also the NUL character. This is\nbecause only tree objects can contain paths and the internal format\nof tree objects already uses NUL characters to delimit paths.\n\nUnfortunately, it was overlooked that in case of an error the pointer\nto the object is displayed in the error message:\n\n```shell\n$ echo 'HEAD:does-not-exist' | git cat-file --batch\nHEAD:does-not-exist missing\n```\n\nAs the error messages are printed along with the regular ouput of the\ncommand on stdout, passing in an invalid pointer with a number of\nnewline characters in it could make it very difficult to parse the\noutput.\n\n### -Z comes to the rescue\n[Toon Claes](https://gitlab.com/toon), Gitaly senior engineer, initially worked on a\npatch to just quote the pointer in the error message, but it was\ndecided in the Git mailing list discussions related to the patch that\nit would be better to just create a new `-Z` option. This option would\nchange both the input and the output delimiter to the NUL character,\nwhile the old `-z` option would be deprecated over time.\n\nSo [Patrick Steinhardt](https://gitlab.com/pks-gitlab), Gitaly staff engineer, implemented that new `-Z` option.\n\nDetails of the patch series, including discussions, can be found\n[here](https://lore.kernel.org/git/20221209150048.2400648-1-toon@iotcl.com/)\nand [here](https://lore.kernel.org/git/cover.1685710884.git.ps@pks.im/).\n\n## 3. Pass pseudo-options to `git rev-list --stdin`\n\n### Computing sizes\nIn GitLab, we need to have different ways to compute the size of Git\nrelated content. For example, we need to know:\n  - how much disk space a repository is using\n  - how big a specific Git object is\n  - how much additional space on a repository is required by a\n    specific set of revisions (and the objects they reference)\n\nKnowing \"how much disk space a repository is using\" is useful to\nenforce repository-related quotas and is easy to get using regular\nshell and OS features.\n\nSize information about a specific Git object is useful to enforce\nquotas related to maximum file size. It can be obtained using, for\nexample, `git cat-file -s \u003Cobject>` or\n`echo \u003Cobject> | git cat-file --batch-check='%(objectsize)'`\nas already seen above.\n\nComputing the space required by a set of revisions is useful, too, as\nforks can share Git content in what we call\n\"[pool repositories](https://docs.gitlab.com/ee/development/git_object_deduplication.html),\"\nand we want to discriminate how much content belongs to each forked\nrepository. Fortunately, `git rev-list` has a `--disk-usage` option\nfor this purpose.\n\n### Passing arguments to `git rev-list`\n`git rev-list` can take a number of different arguments and has a lot\nof different options. It's a fundamental command to traverse commit\ngraphs, and it should be flexible enough to fulfill a lot of different\nuser needs.\n\nWhen repositories grow, they often store a lot of references and a lot\nof files and directories, so there is often the need to pass a big\nnumber of references or paths as arguments to the\ncommand. References and paths can be quite long though.\n\nTo avoid hitting platform limits related to command line length, long\nago, a `--stdin` mode was added that allowed users to pass revisions\nand paths through stdin, instead of as command line\narguments. However, when that was implemented, it was not considered\nnecessary to allow options or pseudo-options, like `--not`,\n`--glob=...`, or `--all` to be passed through stdin.\n\nThis appeared to be a problem for GitLab, as for computing sizes for\nforked repositories we needed some of the pseudo-options, and it would\nhave been intricate and possibly buggy to pass some of them and their\narguments as arguments on the command line while others were passed\nthrough stdin.\n\n### Allowing pseudo-options\nTo fix this issue, Patrick Steinhardt implemented a small patch series to\nallow pseudo-options through stdin.\n\nWith it, in Git 2.42, one can now pass pseudo-options, like `--not`,\n`--glob=...`, or `--all` through stdin when the `--stdin` mode is used.\n\nDetails of the patch series, including discussions, can be found\n[here](https://lore.kernel.org/git/cover.1686744685.git.ps@pks.im/).\n\n## 4. Code and test improvements\nWhile looking at some Git code, we are often tempted to modify nearby\ncode, either to change only its style when the code is ancient and it\nwould look better using Git's current code style, or to refactor it to\nmake it cleaner. This is why we sometimes send small patch series that\ndon't have a real GitLab related purpose.\n\nIn Git 2.42, examples of style code improvements we made are the\n[part1](https://lore.kernel.org/git/pull.1513.git.git.1684440205.gitgitgadget@gmail.com/)\nand\n[part2](https://lore.kernel.org/git/pull.1514.git.git.1684599239.gitgitgadget@gmail.com/)\ntest code modernization patches from John Cai.\n\nAnd [here](https://lore.kernel.org/git/cover.1684324059.git.ps@pks.im/) is\nan example of a refactoring to cleanup some code by Patrick Steinhardt.\n",[23,24,25,26],"git","news","open 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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.",[720],"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/).",[9,725,24],"AI/ML",{"featured":12,"template":13,"slug":727},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":729,"config":732},{"title":730,"heroImage":721,"description":731,"date":722,"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":721,"date":722,"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.",[725,742,9],"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",[9,568],"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. 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