[{"data":1,"prerenderedAt":805},["ShallowReactive",2],{"/en-us/blog/gitlab-18-7-advancing-ai-automation":3,"navigation-en-us":38,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Bill Staples":698,"blog-related-posts-en-us-gitlab-18-7-advancing-ai-automation":712,"blog-promotions-en-us":743,"next-steps-en-us":795},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":11,"meta":27,"navigation":11,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/gitlab-18-7-advancing-ai-automation.yml","Gitlab 18 7 Advancing Ai Automation",[7],"bill-staples",null,"product",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-18-7-advancing-ai-automation",{"heroImage":15,"title":16,"description":17,"authors":18,"date":20,"body":21,"category":9,"tags":22},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1766061346/qky0m84chjftwmyk4kml.png","GitLab 18.7: Advancing AI automation, governance, and developer experience","GitLab 18.7 adds new automation, pipeline controls, and policy updates to help teams reduce manual work, simplify processes, and deliver safer releases.",[19],"Bill Staples","2025-12-18","\nGitLab 18.7 delivers development, operations, and security capabilities that strengthen control, improve consistency, and build confidence as teams integrate AI further into their workflows. These improvements arrive as GitLab approaches a major milestone. GitLab Duo Agent Platform will reach general availability in January 2026 with our 18.8 release, pending we continue to meet the exceptionally high quality standards we set for ourselves in service to our customers worldwide across all industries.\n\nGitLab Duo Agent Platform's GA is designed to introduce a unified, governed way for organizations to orchestrate agentic AI across their software lifecycle. With foundational agents, custom agents, and automated flows working together inside GitLab, teams will be able to adopt agentic workflows that help accelerate work while staying aligned to organizational standards. At GA, we also plan to include expanded AI Catalog functionality, stronger administrative controls, reliability enhancements, and a flexible usage-based billing model designed to provide flexibility for agentic AI usage across many roles and projects.\n\nThe [18.7 release](https://about.gitlab.com/releases/2025/12/18/gitlab-18-7-released/) adds important building blocks to support GitLab Duo Agent Platform’s upcoming GA. New automation features, stronger governance controls, and enhancements across security and pipeline authoring help teams streamline their work and lay the groundwork for an even more reliable agentic experience in 18.8 and beyond.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1143231947?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"18.7 First Look)\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n> On February 10, 2026, we will host [a global event](https://about.gitlab.com/events/transcend/virtual/) that brings our vision of GitLab as the intelligent orchestration platform to life, where software teams and their AI agents stay in flow. You will hear how customers are tackling the [AI paradox in software delivery](https://about.gitlab.com/developer-survey/), see intelligent orchestration in action across DevSecOps workflows, and get a jump start on what this next chapter means for your own modernization journey. [Reserve your spot](https://about.gitlab.com/events/transcend/virtual/) to see how GitLab’s next chapter comes together.\n\n**Here's what is new in 18.7:**\n\n## GitLab Duo Agent Platform\n\nAs more teams bring AI into their development and security workflows, GitLab continues to focus on making adoption powerful and predictable. The updates in 18.7 strengthen the foundation for guided, governed AI experiences that will become fully realized when GitLab Duo Agent Platform reaches GA, as planned for 18.8.\n\n[**Custom Flows**](https://docs.gitlab.com/user/duo_agent_platform/flows/custom/)\n\nCustom Flows introduce a new way for teams to automate multistep workflows using YAML-defined sequences that orchestrate agents to complete repetitive development tasks. Custom Flows help eliminate manual effort for scenarios that follow predictable patterns — such as diagnosing and fixing failed pipelines, updating dependencies, or running policy checks when reviewers are assigned. Instead of handling these tasks interactively, teams can define flows that automatically trigger from GitLab events like mentions and assignments. This capability supports developers who want tailored automations for their own projects, as well as administrators who need consistent, organization-wide workflows for compliance and operational efficiency.\n\n[**SAST False Positive Detection Flow**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/sast_false_positive_detection/)\n\nAI-powered false positive management for Static Application Security Testing (SAST) works to introduce a faster, more accurate way for teams to assess and act on potential false positives. GitLab now uses AI to help identify which findings may be false positives earlier in the review process, reducing the time developers and security teams spend triaging noise. Users can see an overview of how many vulnerabilities may warrant review, track their analysis progress, and dismiss false positives directly from the vulnerability report. Once dismissed, these findings stay dismissed across future pipelines and continue to reflect the correct dismissed status in merge request widgets. This assists with a consistent and reliable signal as code evolves and helps teams focus on real risks, streamline remediation, and cut down on unnecessary security review cycles.\n\n[**Custom Agent Versioning**](https://docs.gitlab.com/user/duo_agent_platform/ai_catalog/#agent-and-flow-versions)\n\nCustom Agent Versioning gives teams control over which version of an AI Catalog agent or flow they use in their projects. Instead of automatically inheriting updates from the creator, GitLab now pins each project to the exact version of the agent and flow enabled for the team. This helps prevent breaking changes, security risks, and workflow disruptions, especially in production pipelines or security-sensitive environments. Teams can upgrade when they choose, test new versions in staging before promoting them, and clearly see which version is running to avoid confusion. It also enables safer customization by letting users fork an agent at a specific version and evolve it independently. The result is a more predictable, stable, and secure way to adopt custom agents across development and CI/CD workflows.\n\n[**New Settings for Foundational Agents**](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/#turn-foundational-agents-on-or-off)\n\nAdmins now have the ability to turn [foundational agents](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/) on or off, giving teams greater control over how AI is used across their organization. With this update, admins can enable or disable these agents at the instance or group level, choose default availability, and control how new agents are introduced while still providing access to the core agent. The result is more flexible AI adoption with the governance, consistency, and control enterprise teams need.\n\n[**Data Analyst Agent**](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/data_analyst/)\n\nThe Data Analyst Agent gives teams a simple way to explore GitLab data using natural language, automatically generating [GitLab Query Language](https://docs.gitlab.com/user/glql/) (GLQL)  queries, retrieving relevant information, and presenting clear insights without requiring dashboards or manual query writing. Users can analyze work volume, understand team activity, identify development trends, monitor issue and merge request status, and quickly discover work items by labels, authors, milestones, or other criteria. It also creates reusable GLQL queries that can be embedded anywhere GitLab Flavored Markdown is supported, making it easier to share findings and answer everyday questions about project activity directly within GitLab.\n\n## Core DevOps\n\nInnovations with GitLab Duo Agent Platform are most effective when the underlying DevOps experience is equally streamlined and dependable. The improvements in 18.7 to core GitLab workflows help ensure that automation, pipelines, and reusable components operate with highest levels of clarity and consistency.\n\n[**Dynamic Input Selection in GitLab Pipelines**](https://docs.gitlab.com/ci/inputs/#define-conditional-input-options-with-specinputsrules)\n\nDynamic Input Selection in GitLab Pipelines introduces a more intuitive way to trigger pipelines through dynamic, cascading dropdown fields in the GitLab UI. This allows cross-functional teams to run pipelines without editing YAML or relying on developers, while ensuring that only valid, context-aware options are shown as they make selections. The feature supports complex workflows, assists with reducing misconfigured runs, and removes a key blocker for teams migrating from Jenkins Active Choice, helping organizations standardize their CI/CD processes entirely on GitLab.\n\n[**CI/CD Catalog Publication Guardrails**](https://docs.gitlab.com/ee/ci/components/)\n\nAdministrators of GitLab Self-Managed and GitLab Dedicated can now control which projects are allowed to publish components to the CI/CD Catalog. This new setting helps organizations maintain a curated, trusted ecosystem by ensuring only approved sources can add components. It strengthens governance for enterprise customers who want to preserve control over their CI/CD landscape while still enabling teams to discover and reuse sanctioned components.\n\n## Platform Security\n\nAs automation and pipeline workflows become more efficient, it remains essential that teams maintain strong visibility and control over how code changes meet organizational standards. The Platform Security update in 18.7 reinforces this balance by giving teams a more flexible way to introduce and refine policy guidance without interrupting delivery.\n\n[**Warn Mode for MR Approval Policies**](https://docs.gitlab.com/user/application_security/policies/merge_request_approval_policies/#warn-mode)\n\nWarn Mode for MR Approval Policies allows violations to be surfaced without blocking merges, giving teams a lower-friction way to introduce or adjust policies while assessing their impact before full enforcement. It also supports a guidance-based approach, where developers can review or dismiss violations with all actions audited to help AppSec refine policy effectiveness. Beyond merge requests, violations already present or introduced into the default branch now appear with a visual badge in the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/), making it easier to identify and prioritize issues that break policy.\n\n## Elevating how teams build, secure, and deliver software\n\nThe 18.7 release is about strengthening the foundation for reliable, flexible automation across your GitLab environment.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1147756347?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"18.7 Demo V3\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\nGitLab Premium and Ultimate users can start using these capabilities today on [GitLab.com](https://GitLab.com) and self-managed environments, with availability for GitLab Dedicated customers planned for next month.\n\nGitLab Duo Agent Platform is currently in **beta** — enable beta and experimental features to experience how full-context AI can transform the way your teams build software. New to GitLab? [Start your free trial](https://about.gitlab.com/free-trial/devsecops/) and see why the future of development is AI-powered, secure, and orchestrated through the world’s most comprehensive DevSecOps platform.\n\n***Note:** Platform capabilities that are in beta are available as part of the GitLab Beta program. They are free to use during the beta period, and when generally available, they will be made available with a paid add-on option for GitLab Duo Agent Platform.*\n\n### Stay up to date with GitLab\n\nTo make sure you’re getting the latest features, security updates, and performance improvements, we recommend keeping your GitLab instance up to date. The following resources can help you plan and complete your upgrade:\n\n* [Upgrade Path Tool](https://gitlab-com.gitlab.io/support/toolbox/upgrade-path/) – enter your current version and see the exact upgrade steps for your instance\n* [Upgrade Documentation](https://docs.gitlab.com/update/upgrade_paths/) – detailed guides for each supported version, including requirements, step-by-step instructions, and best practices\n\nBy upgrading regularly, you’ll ensure your team benefits from the newest GitLab capabilities and remains secure and supported.\n\nFor organizations that want a hands-off approach, consider [GitLab’s Managed Maintenance service](https://content.gitlab.com/viewer/d1fe944dddb06394e6187f0028f010ad#1). With Managed Maintenance, your team stays focused on innovation while GitLab experts keep your Self-Managed instance reliably upgraded, secure, and ready to lead in DevSecOps. Ask your account manager for more information.\n\n*This blog post contains \"forward‑looking statements\" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in these statements are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to differ materially. Further information on these risks and other factors is included under the caption \"Risk Factors\" in our filings with the SEC. We do not undertake any obligation to update or revise these statements after the date of this blog post, except as required by law.*\n\n\n",[9,23,24,25],"AI/ML","CI/CD","security","yml",{},"/en-us/blog/gitlab-18-7-advancing-ai-automation",{"config":30,"title":32,"ogImage":15,"description":17},{"noIndex":31},false,"GitLab 18.7: Advancing AI automation and developer experience","en-us/blog/gitlab-18-7-advancing-ai-automation",[9,35,36,25],"aiml","cicd","Iy3aDiZ75MQKyZac8AfePunFdhVEeBmmJ5m5QzoNGCM",{"data":39},{"logo":40,"freeTrial":45,"sales":50,"login":55,"items":60,"search":367,"minimal":398,"duo":417,"switchNav":426,"pricingDeployment":437},{"config":41},{"href":42,"dataGaName":43,"dataGaLocation":44},"/","gitlab logo","header",{"text":46,"config":47},"Get free trial",{"href":48,"dataGaName":49,"dataGaLocation":44},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":51,"config":52},"Talk to 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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.",[718],"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/).",[9,23,723],"news",{"featured":31,"template":12,"slug":725},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":727,"config":730},{"title":728,"heroImage":719,"description":729,"date":720,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":31,"template":12,"externalUrl":731},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":733,"config":741},{"title":734,"description":735,"authors":736,"heroImage":719,"date":720,"body":738,"category":9,"tags":739},"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.",[737],"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.",[23,740,9],"features",{"featured":11,"template":12,"slug":742},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":744},[745,759,770,781],{"id":746,"categories":747,"header":749,"text":750,"button":751,"image":756},"ai-modernization",[748],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":752,"config":753},"Get your AI maturity score",{"href":754,"dataGaName":755,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":757},{"src":758},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":760,"categories":761,"header":762,"text":750,"button":763,"image":767},"devops-modernization",[9,566],"Are you just managing tools or shipping innovation?",{"text":764,"config":765},"Get your DevOps maturity score",{"href":766,"dataGaName":755,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":771,"categories":772,"header":773,"text":750,"button":774,"image":778},"security-modernization",[25],"Are you trading speed for security?",{"text":775,"config":776},"Get your security maturity score",{"href":777,"dataGaName":755,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":782,"paths":783,"header":786,"text":787,"button":788,"image":793},"github-azure-migration",[784,785],"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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