[{"data":1,"prerenderedAt":826},["ShallowReactive",2],{"/en-us/blog/4-ways-to-accelerate-embedded-development-with-gitlab":3,"navigation-en-us":42,"banner-en-us":452,"footer-en-us":462,"blog-post-authors-en-us-Matt DeLaney|Darwin Sanoy":703,"blog-related-posts-en-us-4-ways-to-accelerate-embedded-development-with-gitlab":732,"blog-promotions-en-us":763,"next-steps-en-us":816},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":29,"isFeatured":13,"meta":30,"navigation":31,"path":32,"publishedDate":22,"seo":33,"stem":37,"tagSlugs":38,"__hash__":41},"blogPosts/en-us/blog/4-ways-to-accelerate-embedded-development-with-gitlab.yml","4 Ways To Accelerate Embedded Development With Gitlab",[7,8],"matt-delaney","darwin-sanoy",null,"product",{"slug":12,"featured":13,"template":14},"4-ways-to-accelerate-embedded-development-with-gitlab",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"4 ways to accelerate embedded development with GitLab","Learn how automated hardware testing, standard builds, collaborative workflows, and integrated compliance eliminate bottlenecks in firmware development.",[19,20],"Matt DeLaney","Darwin Sanoy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659756/Blog/Hero%20Images/REFERENCE_-_display_preview_for_blog_images.png","2025-06-05","Software in embedded systems is no longer just a part number — it's a critical differentiator. This shift has led to enormous complexity in the firmware running in our cars, airplanes, and industrial machines. The number of lines of code in the average car is expected to reach [650 million](https://www.statista.com/statistics/1370978/automotive-software-average-lines-of-codes-per-vehicle-globally/) by the end of 2025, up from 200 million just five years ago. In aerospace systems, the complexity of embedded software has nearly [doubled every four years](https://www.mckinsey.com/industries/aerospace-and-defense/our-insights/debugging-the-software-talent-gap-in-aerospace-and-defense) for the last several decades. \n\nTraditional embedded development approaches cannot effectively handle the software challenges of modern machines. This shortcoming slows engineers down, in part, by exacerbating challenges such as: \n\n* [Hardware testing bottlenecks](#challenge-1-hardware-testing-bottlenecks) \n* [Inconsistent build environments](#challenge-2-inconsistent-build-environments)\n* [Siloed development practices](#challenge-3-siloed-development-practices)\n* [Manual functional safety compliance processes](#challenge-4-manual-functional-safety-compliance-processes)\n\nEmbedded developers need a new approach to deal with the rapid increase in code. In this article, we’ll explain four ways you can use the GitLab AI-native DevSecOps platform to shorten feedback loops, work collaboratively and iteratively, and streamline compliance.\n\n## Challenge 1: Hardware testing bottlenecks\n\nUnlike enterprise software that can run on virtually any cloud server, embedded automotive software must be tested on specialized hardware that precisely matches production environments. Traditional hardware-in-the-loop (HIL) testing processes often follow this pattern:\n\n1. Developers write code for an embedded system (e.g., an electronic control unit)  \n2. They request access to limited, expensive hardware test benches (costing $500,000-$10M each)  \n3. They wait days or weeks for their scheduled access window  \n4. They manually deploy and test their code on physical hardware at their desks  \n5. They document results, pass the hardware to the next developer, and go to the back of the hardware testing queue\n\nThis process is extremely inefficient. Embedded developers may finish writing their code today and wait weeks to test it on a hardware target. By then, they've moved on to other tasks. This context switching drains productivity. Not only that, developers may wait weeks to learn they had a simple math error in their code. \n\n### Solution: Automated hardware allocation and continuous integration\n\nYou can streamline hardware testing through automation using the [GitLab On-Premises Device Cloud](https://gitlab.com/guided-explorations/embedded/ci-components/device-cloud), a CI/CD component. This lets you automate the orchestration of scarce hardware resources, turning a manual, time-intensive process into a streamlined, continuous workflow.\n\nThe On-Premises Device Cloud:\n\n1. Creates pools of shared hardware resources  \n2. Automatically — and exclusively — allocates hardware to a developer’s hardware testing pipeline tasks based on availability  \n3. Deploys and executes tests without manual intervention  \n4. Collects and reports results through integrated pipelines  \n5. Automatically deallocates hardware back into the “available” pool\n\nAfter submitting code, you’ll receive results in hours instead of days, often without ever physically touching the test hardware.\n\nWhat this video for an introduction to the GitLab On-Premises Device Cloud CI/CD Component to orchestrate the remote allocation of shared hardware for HIL:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ltr2CIM9Zag?si=NOij3t1YYz4zKajC\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nYou can also adopt multi-pronged testing strategies that balance speed and quality. Bring the following embedded test patterns and environments into automated GitLab CI pipelines:\n\n* **Software-in-the-loop (SIL):** Testing on virtual hardware simulators for quicker initial feedback  \n* **Processor-in-the-loop (PIL):** Testing on representative processor hardware for faster feedback at a lower cost  \n* **Hardware-in-the-loop (HIL):** Testing on full production-equivalent hardware and test benches for late-stage verification\n\nBy automating the orchestration of these tests within CI pipelines, you’ll be able to identify issues earlier, iterate faster, and accelerate time to market.\n\n## Challenge 2: Inconsistent build environments\n\nAnother significant challenge in embedded development is build environment inconsistency. Embedded developers often manually execute builds on their local machines with varying configurations, compiler versions, and dependencies. Then they’ll paste the binaries from their local build to a shared codebase.\n\nThis approach creates several problems:\n\n* **Inconsistent outputs:** Builds for the same source code produce different results on different machines  \n* **\"Works on my machine\" syndrome:** Code that builds locally fails in shared environments  \n* **Poor traceability:** Limited audit trail of who built what and when  \n* **Knowledge silos:** Build expertise becomes concentrated in a few individuals\n\nThis approach can lead to errors, bottlenecks, and costly delays. \n\n### Solution: Standardized build automation\n\nYou can address these challenges by implementing standardized build automation within CI/CD pipelines in GitLab. This approach creates consistent, repeatable, container-based build environments that eliminate machine-specific variations. Through the use of special Embedded Gateway Runner provisioning scripts, containers can interface with hardware for flashing and port monitoring for automated testing.\n\nKey elements of this solution include:\n\n* **Lifecycle managed environments:** Define complex embedded simulation environments as code; automatically deploy environments for testing and destroy them afterward  \n* **Containerization:** Use Docker containers to ensure identical build environments  \n* **Automated dependency management:** Control and version all dependencies  \n* **Central build execution:** Run builds on shared infrastructure rather than local machines\n\n> Follow this tutorial to learn [how to automate embedded software builds within a GitLab CI pipeline](https://gitlab.com/guided-explorations/embedded/workshops/embedded-devops-workshop-refactoring-to-ci/-/blob/main/TUTORIAL2.md%20).\n\nBy standardizing and automating the build process, you can ensure that every build follows the same steps with the same dependencies, producing consistent outputs regardless of who initiated it. This not only improves quality but also democratizes the build process, enabling more team members to participate without specialized knowledge.\n\n## Challenge 3: Siloed development practices\n\nEnterprise development teams have widely adopted collaborative practices such as DevOps, underpinned by shared source code management (SCM) and continuous integration/continuous delivery (CI/CD) systems. Embedded developers, on the other hand, have historically worked alone at their desks. There are valid technical reasons for this. \n\nFor example, consider hardware virtualization, which is a key enabler of DevOps automation. The industry has been slower to virtualize the massive range of specialized processors and boards used in embedded systems. This is due in large part to the difficulties of virtualizing production real-time systems and the associated lack of economic incentives. Compare that to cloud virtualization which has been commoditized and benefited enterprise SaaS development for over a decade.\n\nMany providers are now embracing virtualization-first for the sake of speeding up embedded development. If teams fail to adopt virtual testing options, however, their silos will remain and negatively impact the business through: \n\n* **Knowledge fragmentation**: Critical insights remain scattered across individuals and teams  \n* **Redundant development**: Multiple teams solve identical problems, creating inconsistencies  \n* **Late-stage discovery during big-bang integrations**: Problems are found late in the process when multiple developers integrate their code at once, when errors are more costly to fix  \n* **Stifled innovation**: Solutions from one domain rarely influence others, hampering the development of new product ideas\n\n### Solution: Collaborative engineering through a unified platform\n\nAn important step in breaking down these silos is to standardize embedded development around GitLab’s unified DevSecOps platform. In this regard, GitLab is aligned with the shift of embedded systems toward more consolidated, shared platforms on embedded devices. GitLab enables:\n\n* **Shared visibility:** Make all code, Issues, and documentation visible across teams  \n* **Collaborative workflows:** Enable peer review and knowledge sharing through merge requests  \n* **Centralized knowledge:** Maintain a single source of truth for all development artifacts  \n* **Asynchronous collaboration:** Allow teams to work together across different locations and time zones\n\nHuman-AI agent collaboration is a fundamental ingredient to fueling the customer-facing innovations that digital natives and established embedded brands desire. GitLab enables human-AI collaboration as well. By creating transparency across the development lifecycle, GitLab changes embedded development from an isolated activity to a collaborative practice. Engineers can see each other's work in progress, learn from collective experiences, and build upon shared solutions.\n\nWatch this presentation from Embedded World Germany 2025, which explains the power of embedded developers collaborating and sharing “work in progress”. The demo portion from 24:42 to 36:51 shows how to integrate HIL into a GitLab CI pipeline and enable collaborative development.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/F_rlOyq0hzc?si=eF4alDY6HK98uZPj\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nPerhaps most importantly, by achieving greater collaboration through DevSecOps, teams can unlock embedded systems innovations that would otherwise remain hidden. Indeed, collaboration fuels innovation. [One study](https://www.sciencedirect.com/science/article/abs/pii/S0749597800928887), for example, found that group brainstorming, when properly structured, can lead to more innovative and creative outcomes than individuals working alone. Collaborative development is crucial in the race to develop software-defined products. \n\n## Challenge 4: Manual functional safety compliance processes\n\nEmbedded systems in the automotive and aerospace industries must comply with rigorous functional safety standards, including ISO 26262, MISRA C/C++, DO-178C, and DO-254. Traditional compliance approaches involve manual reviews, extensive documentation, and separate verification activities that occur late in the development cycle. This often creates security review bottlenecks. When specialized embedded security and code quality scanners detect vulnerabilities in a developer’s code, the scan issue gets added to a pile of other issues that haven’t been resolved. Developers can’t integrate their code, and security personnel need to wade through a backlog of code violations. This creates delays and makes compliance more difficult. \n\nSome of the challenges can best be summed up as: \n\n* **Late-stage compliance issues**: Problems discovered after development is complete  \n* **Documentation burden**: Extensive manual effort to create and maintain compliance evidence  \n* **Process bottlenecks**: Serial compliance activities that block development progress  \n* **Expertise dependence**: Reliance on limited specialists for compliance activities\n\nAs a result, teams often need to choose between velocity and compliance — a precarious trade-off in safety-critical systems.\n\n### Solution: Automated functional safety compliance workflow building blocks\n\nRather than treating security and compliance as post-development verification activities, you can codify compliance requirements and enforce them automatically through [customizable frameworks in GitLab](https://about.gitlab.com/blog/introducing-custom-compliance-frameworks-in-gitlab/). To do this for functional safety standards, in particular, you can integrate GitLab with specialized embedded tools, which provide the depth of firmware scanning required by functional safety standards. Meanwhile, GitLab provides automated compliance checks, full audit trails, and merge request gating — all features needed to support a robust continuous compliance program. \n\nThis integrated approach includes:\n\n* **Compliance-as-code:** Define compliance requirements as automated checks  \n* **Integrated specialized tools:** Connect tools like CodeSonar into the DevSecOps platform for automotive-specific compliance  \n* **Continuous compliance verification:** Verify requirements throughout development  \n* **Automated evidence collection:** Gather compliance artifacts as a by-product of development\n\nWatch this video to learn how to use Custom Compliance Frameworks in GitLab to create your own compliance policies. You can create compliance policies related to any standard (e.g., ISO 26262) and automatically enforce those policies in GitLab.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/S-FQjzSyVJw?si=0UdtGNuugLPG0SLL\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nBy shifting compliance left and embedding it within normal development workflows, you can maintain safety standards without sacrificing velocity. Automated checks catch issues early when they're easier and less expensive to fix, while continuous evidence collection reduces the documentation burden.\n\n## Realizing the power of embedded DevOps\n\nEmbedded development is changing fast. Teams that remain stuck in manual processes and isolated workflows will find themselves increasingly left behind, while those that embrace automated, collaborative practices will define the future of software-defined smart systems.\n\nExplore our [Embedded DevOps Workshop](https://gitlab.com/guided-explorations/embedded/workshops/embedded-devops-workshop-refactoring-to-ci) to start automating embedded development workflows with GitLab, or [watch this presentation from GitLab's Field Chief Cloud Architect](https://content.gitlab.com/viewer/0a35252831bd130f879b0725738f70ed) to learn how leading organizations are bringing hardware-in-the-loop testing into continuous integration workflows to accelerate embedded development.\n\n## Learn more\n\n- [Why GitLab Premium with Duo for embedded systems development?](https://content.gitlab.com/viewer/438451cba726dd017da7b95fd0fb1b59)\n- [Why GitLab Ultimate with Duo for embedded systems development?](https://content.gitlab.com/viewer/87f5104c26720e2c0d73a6b377522a44)\n- [More embedded development systems presentations from GitLab](https://content.gitlab.com/viewer/e59c40099d5e3c8f9307afb27c4a923f)",[25,26,27,28],"DevSecOps platform","tutorial","features","embedded 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Architect",{"headshot":724,"linkedin":725,"ctfId":726},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659751/Blog/Author%20Headshots/Darwin-Sanoy-headshot-395-square-gitlab-teampage-avatar.png","https://linkedin.com/in/darwinsanoy","DarwinJS",{},"/en-us/blog/authors/darwin-sanoy",{},"en-us/blog/authors/darwin-sanoy","UkMMwmU5o2e6Y-wBltA9E_z96LvHuB-bG6VW9DsLzIY",[733,747,753],{"content":734,"config":745},{"title":735,"description":736,"authors":737,"heroImage":739,"date":740,"body":741,"category":10,"tags":742},"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.",[738],"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/).",[10,743,744],"AI/ML","news",{"featured":13,"template":14,"slug":746},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":748,"config":751},{"title":749,"heroImage":739,"description":750,"date":740,"category":10},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":13,"template":14,"externalUrl":752},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":754,"config":761},{"title":755,"description":756,"authors":757,"heroImage":739,"date":740,"body":759,"category":10,"tags":760},"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.",[758],"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.",[743,27,10],{"featured":31,"template":14,"slug":762},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":764},[765,779,790,802],{"id":766,"categories":767,"header":769,"text":770,"button":771,"image":776},"ai-modernization",[768],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":772,"config":773},"Get your AI maturity score",{"href":774,"dataGaName":775,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":780,"categories":781,"header":782,"text":770,"button":783,"image":787},"devops-modernization",[10,571],"Are you just managing tools or shipping innovation?",{"text":784,"config":785},"Get your DevOps maturity score",{"href":786,"dataGaName":775,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":791,"categories":792,"header":794,"text":770,"button":795,"image":799},"security-modernization",[793],"security","Are you trading speed for security?",{"text":796,"config":797},"Get your security maturity score",{"href":798,"dataGaName":775,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":800},{"src":801},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":803,"paths":804,"header":807,"text":808,"button":809,"image":814},"github-azure-migration",[805,806],"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":810,"config":811},"See how GitLab compares to GitHub",{"href":812,"dataGaName":813,"dataGaLocation":246},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":815},{"src":789},{"header":817,"blurb":818,"button":819,"secondaryButton":824},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":820,"config":821},"Get your free trial",{"href":822,"dataGaName":53,"dataGaLocation":823},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":508,"config":825},{"href":57,"dataGaName":58,"dataGaLocation":823},1776442962250]