[{"data":1,"prerenderedAt":808},["ShallowReactive",2],{"/en-us/blog/monitor-application-performance-with-distributed-tracing":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Sacha Guyon":701,"blog-related-posts-en-us-monitor-application-performance-with-distributed-tracing":715,"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":28,"isFeatured":12,"meta":29,"navigation":12,"path":30,"publishedDate":20,"seo":31,"stem":36,"tagSlugs":37,"__hash__":39},"blogPosts/en-us/blog/monitor-application-performance-with-distributed-tracing.yml","Monitor Application Performance With Distributed Tracing",[7],"sacha-guyon",null,"product",{"slug":11,"featured":12,"template":13},"monitor-application-performance-with-distributed-tracing",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Monitor application performance with Distributed Tracing","Learn how Distributed Tracing helps troubleshoot application performance issues by providing end-to-end visibility and seamless collaboration across your organization.",[18],"Sacha Guyon","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098000/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%288%29_5x6kH5vwjz8cwKgSBh1w11_1750098000511.png","2024-06-13","Downtime due to application defects or performance issues can have devastating financial consequences for businesses. An hour of downtime is estimated to cost firms $301,000 or more, according to [Information Technology Intelligence Consulting's 2022 Global Server Hardware and Server OS Reliability Survey](https://itic-corp.com/server-and-application-by-the-numbers-understanding-the-nines/). These issues often originate from human-introduced changes, such as code or configuration changes.\n\nResolving such incidents requires development and operations teams to collaborate closely, investigating the various components of the system to find the root cause change, and promptly restore the system back to normal operation. However, these teams commonly use separate tools to build, manage, and monitor their application services and infrastructure. This approach leads to siloed data, fragmented communication, and inefficient context switching, increasing the time spent to detect and resolve incidents.\n\nGitLab aims to address this challenge by combining software delivery and monitoring functionalities within the same platform. Last year, we released [Error Tracking](https://docs.gitlab.com/ee/operations/error_tracking.html) as a general availability feature in [GitLab 16.0](https://about.gitlab.com/releases/2023/05/22/gitlab-16-0-released/#error-tracking-is-now-generally-available). Now, we're excited to announce the [Beta release of Distributed Tracing](https://docs.gitlab.com/ee/operations/tracing), the next step toward a comprehensive observability offering seamlessly integrated into the GitLab DevSecOps platform.\n\n## A new era of efficiency: GitLab Observability\n\nGitLab Observability empowers development and operations teams to visualize and analyze errors, traces, logs, and metrics from their applications and infrastructure. By integrating application performance monitoring into existing software delivery workflows, context switching is minimized and productivity is increased, keeping teams focused and collaborative on a unified platform.\n\nAdditionally, GitLab Observability bridges the gap between development and operations by providing insights into application performance in production. This enhances transparency, information sharing, and communication between teams. Consequently, they can detect and resolve bugs and performance issues arising from new code or configuration changes sooner and more effectively, preventing those issues from escalating into major incidents that could negatively impact the business.\n\n## What is Distributed Tracing?\n\nWith Distributed Tracing, engineers can identify the source of application performance issues. A trace represents a single user request that moves through different services and systems. Engineers are able to analyze the timing of each operation and any errors as they occur.\n\nEach trace is composed of one or more spans, which represent individual operations or units of work. Spans contain metadata like the name, timestamps, status, and relevant tags or logs. By examining the relationships between spans, developers can understand the request flow, identify performance bottlenecks, and pinpoint issues.\n\nDistributed Tracing is especially valuable for [microservices architecture](https://about.gitlab.com/topics/microservices/), where a single request may involve numerous service calls across a complex system. Tracing provides visibility into this interaction, empowering teams to quickly diagnose and resolve problems.\n\n![tracing example](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750098009139.png)\n\nFor example, this trace illustrates a how a user request flows through difference services to fetch product recommendations on a e-commerce website:\n\n- `User Action`: This indicates the user's initial action, such as clicking a button to request product recommendations on a product page.\n-  `Web front-end`: The web front-end sends a request to the recommendation service to retrieve product recommendations.\n- `Recommendation service`: The request from the web front-end is handled by the recommendation service, which processes the request to generate a list of recommended products.\n- `Catalog service`: The recommendation service calls the catalog service to fetch details of the recommended products. An alert icon suggests an issue or delay at this stage, such as a slow response or error in fetching product details.\n- `Database`: The catalog service queries the database to retrieve the actual product details. This span shows the SQL query in the database.\n\nBy visualizing this end-to-end trace, developers can identify performance issues – here, an error in the Catalog service – and quickly diagnose and resolve issues across the distributed system.\n\n![End-to-end trace](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750098009140.png)\n\n## How Distributed Tracing works\n\nHere is a breakdown of how Distributed Tracing works.\n\n### Collect data from any application with OpenTelemetry\n\nTraces and spans can be collected using [OpenTelemetry](https://opentelemetry.io/docs/what-is-opentelemetry/), an open-source observability framework that supports a wide array of SDKs and libraries across [major programming languages and frameworks](https://opentelemetry.io/docs/languages/). This framework offers a vendor-neutral approach for collecting and exporting telemetry data, enabling developers to avoid vendor lock-in and choose the tools that best fit their needs.\n\nThis means that if you are already using OpenTelemetry with another vendor, you can send data to us simply by adding our endpoint to your configuration file, making it very easy to try out our features!\n\n![Distributed tracing workflow diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750098009141.png)\n\n### Ingest and retain data at scale with fast, real-time queries\n\nObservability requires the storage and querying of vast amounts of data while maintaining low latency for real-time analytics. To meet these needs, we developed a horizontally scalable, long-term storage solution using ClickHouse and Kubernetes, based on our [acquisition of Opstrace](https://about.gitlab.com/press/releases/2021-12-14-gitlab-acquires-opstrace-to-expand-its-devops-platform-with-open-source-observability-solution/). This [open-source platform](https://gitlab.com/gitlab-org/opstrace/opstrace) ensures rapid query performance and enterprise-grade scalability, all while minimizing costs.\n\n### Explore and analyze traces effortlessly\nAn advanced, native-level user interface is crucial for effective data exploration. We built such an interface from the ground up, starting with our Trace Explorer, which allows users to examine traces and understand their application's performance:\n- __Advanced filtering:__ Filter by services, operation names, status, and time range. Autocomplete helps simplify querying.\n- __Error highlighting:__ Easily identify error spans in search results.\n- __RED metrics:__ Visualize the Requests rate, Errors rate, and average Duration as a time-series chart for any search in real-time.\n- __Timeline view:__ Individual traces are displayed as a waterfall diagram, providing a complete view of a request distributed across different services and operations.\n- __Historical data:__ Users can query traces up to 30 days in the past.\n\n![Distributed Tracing - image 5](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750098009141.png)\n\n## How we use Distributed Tracing at GitLab\n[Dogfooding](https://handbook.gitlab.com/handbook/values/#dogfooding) is a core value and practice at GitLab. We've been already using early versions of Distributed Tracing for our engineering and operations needs. Here are a couple example use cases from our teams:\n\n### 1. Debug errors and performance Issues in GitLab Agent for Kubernetes\n\nThe [Environments group](https://handbook.gitlab.com/handbook/engineering/development/ops/deploy/environments/) has been using Distributed Tracing to troubleshoot and resolve issues with the [GitLab Agent for Kubernetes](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent), such as timeouts or high latency issues. The Trace List and Trace Timeline views offer valuable insights for the team to address these concerns efficiently. These traces are shared and discussed in the [related GitLab issues](https://gitlab.com/gitlab-org/cluster-integration/gitlab-agent/-/issues/386#note_1576431796), where the team collaborates on resolution.\n\n\u003Ccenter>\u003Ci>\"The Distributed Tracing feature has been invaluable in pinpointing where latency issues are occurring, allowing us to focus on the root cause and resolve it faster.\" - Mikhail, GitLab Engineer\u003C/i>\u003C/center>\u003Cp>\n\n### 2. Optimize GitLab’s build pipeline duration by identifying performance bottlenecks\n\nSlow deployments of GitLab source code can significantly impact the productivity of the whole company, as well as our compute spending. Our main repository runs [over 100,000 pipelines every month](https://gitlab.com/gitlab-org/gitlab/-/pipelines/charts). If the time it takes for these pipelines to run changes by just one minute, it can add or remove more than 2,000 hours of work time. That's 87 extra days!\n\nTo optimize pipeline execution time, GitLab's [platform engineering teams](https://handbook.gitlab.com/handbook/engineering/infrastructure/) utilize a [custom-built tool](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace) that converts GitLab deployment pipelines into traces.\n\nThe Trace Timeline view allows them to visualize the detailed execution timeline of complex pipelines and pinpoint which jobs are part of the critical path and slowing down the entire process. By identifying these bottlenecks, they can optimize job execution – for example, making the job fail faster, or running more jobs in parallel – to improve overall pipeline efficiency.\n\n![Distributed Tracing - image 6](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098009/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750098009143.gif)\n\n[The script is freely available](https://gitlab.com/gitlab-com/gl-infra/gitlab-pipeline-trace), so you can adapt it for your own pipelines.\n\n\u003Ccenter>\u003Ci>\"Using Distributed Tracing for our deployment pipelines has been a game-changer. It's helped us quickly identify and eliminate bottlenecks, significantly reducing our deployment times.\"- Reuben, GitLab Engineer\u003C/i>\u003C/center>\u003Cp>\n\n## What's coming next?\n\nThis release is just the start: In the next few months, we'll continue to expand our observability and monitoring features with the upcoming Metrics and Logging releases. Check out [our Observability direction page](https://docs.gitlab.com/operations/) for more info, and keep an eye out for updates!\n\n## Join the private Beta\n\nInterested in being part of this exciting journey? [Sign up to enroll in the private Beta](https://docs.gitlab.com/operations/observability/) and try out our features. Your contribution can help shape the future of observability within GitLab, ensuring our tools are perfectly aligned with your needs and challenges.\n\n> Help shape the future of GitLab Observability. 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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.",[721],"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/).",[9,726,25],"AI/ML",{"featured":32,"template":13,"slug":728},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":730,"config":733},{"title":731,"heroImage":722,"description":732,"date":723,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":32,"template":13,"externalUrl":734},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":736,"config":743},{"title":737,"description":738,"authors":739,"heroImage":722,"date":723,"body":741,"category":9,"tags":742},"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.",[740],"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.",[726,24,9],{"featured":12,"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":244},"/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",[9,569],"Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":757,"dataGaLocation":244},"/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":244},"/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":244},"/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":506,"config":807},{"href":55,"dataGaName":56,"dataGaLocation":805},1776443058371]