[{"data":1,"prerenderedAt":827},["ShallowReactive",2],{"/en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering":3,"navigation-en-us":41,"banner-en-us":462,"footer-en-us":472,"blog-post-authors-en-us-Bill Staples":713,"blog-related-posts-en-us-gitlab-18-3-expanding-ai-orchestration-in-software-engineering":728,"blog-promotions-en-us":766,"next-steps-en-us":817},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":26,"description":17,"extension":27,"externalUrl":28,"featured":15,"heroImage":19,"isFeatured":15,"meta":29,"navigation":15,"path":30,"publishedDate":26,"rawbody":31,"seo":32,"slug":14,"stem":35,"tagSlugs":36,"tags":39,"template":13,"updatedDate":28,"__hash__":40},"blogPosts/en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering.md","GitLab 18.3: Expanding AI orchestration in software engineering",[7],"bill-staples",[9],"Bill Staples","Today, GitLab is a comprehensive DevSecOps platform, unifying every stage of the software lifecycle. Building on that foundation, we're on a journey toward becoming the world's first AI-native platform for software engineering. At GitLab, we believe the future of software engineering is an inherently human and AI collaboration, and we want to bring the very best AI capabilities to every GitLab user.\n\nThis transformation is happening at three distinct layers that go beyond what other AI dev tools are doing:\n\n![AI-native transformation slide visualizing what's laid out below](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755762266/iwuugge3cxweiyvi0yjk.png)\n\n**First, we are a system of record.** Our unified data platform holds your most valuable digital assets. This includes your source code and intellectual property, as well as a wealth of unstructured data spanning project plans, bug backlogs, CI/CD configurations, deployment histories, security reports, and compliance data. This creates a treasure trove of contextual data that remains securely within your GitLab environment, unavailable to generic agents or large language models.\n\n**Second, we act as your software control plane.** We orchestrate your most critical business processes through Git repositories, REST APIs, and webhook-based interfaces that power your end-to-end software delivery. Many of our customers consider this a tier-0 dependency that their critical business processes rely on daily.\n\n**Third, we deliver a powerful user experience.** We deliver an integrated interface that helps eliminate the costly context-switching that slows down most engineering teams. With complete lifecycle visibility and collaboration tools in one platform, over 50 million registered users and our vast community depend on GitLab to get their work done. This expertise positions GitLab uniquely to pioneer intuitive human-to-AI collaboration that amplifies team productivity while preserving the workflows that our users know and trust.\n\n**Extending our platform with AI natively integrated at every layer**\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) integrates and extends all three of these layers. It is designed for extensibility and interoperability, enabling customers and partners to build solutions that create even more value. Our open platform approach emphasizes seamless connectivity with external AI tools and systems while being deeply integrated into our existing stack at all three layers.\n\n* First, we're extending our unified data platform with a **Knowledge Graph,** which indexes and stitches together code with all of the rest of your unstructured data, specifically optimized for agentic access. AI thrives on context, and we believe this will not only accelerate reasoning and inference by agents but also deliver lower-cost and higher-quality agentic outcomes.\n* Second, we're adding an important **Orchestration Layer** to our existing Control Plane in three distinct parts: enabling agents and flows to register as subscribers for GitLab SDLC events, building a new orchestration engine that allows for purpose-built, multi-agent flows, and exposing GitLab tools, agents, and flows via MCP and standard protocols for unparalleled interoperability.\n* Finally, we're extending the **GitLab experience** to deliver first-class agents and agent flows across the entire software development lifecycle. You'll be able to assign async tasks to agents, @ mention them in comments, and create custom agents with context specific to your workflows — but more importantly, GitLab is shipping native agents for every stage of development while unlocking a rich ecosystem of third-party agents. This creates true human-to-AI collaboration where agents become as natural to work with as your human teammates.\n\nWatch this video to see what's coming in 18.3 and beyond, or read on.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111796316?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=\"GitLab_18.3 Release_081925_MP_v1\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## What's new in GitLab 18.3\n\nWith 18.2, we introduced specialized [AI agents](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=Agents%20that%20work%20out%20of%20the%20box) that work alongside developers across the software development lifecycle, plus our [Software Development Flow](\u003Chttps://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=we%20are%20building%3A-,Software%20Development%20Flow,-(now%20in%20beta>) — a powerful feature that gives users the ability to orchestrate multiple agents to plan, implement, and test code changes end-to-end.\n\nGitLab 18.3 introduces expanded integrations and interoperability, more Flows, and enhanced context awareness across the entire software development lifecycle.\n\n### Expanded integrations and interoperability\n\nWe're delivering comprehensive AI extensibility through both first-party GitLab agents and a rich ecosystem of third-party agents, all with full access to project context and data. This approach maintains native GitLab workflows and governance while providing the flexibility to choose preferred tools through highly integrated orchestration between these agents and GitLab's core platform. Teams gain enhanced AI functionality while preserving key integration, oversight, and user experience benefits.\n\n* **MCP server - Universal AI integration:** GitLab's MCP ([Model Context Protocol](https://about.gitlab.com/topics/ai/model-context-protocol/)) server enables AI systems to securely integrate directly with your GitLab projects and development processes. This standardized interface eliminates custom integration overhead and allows your AI tools — including [Cursor](https://docs.cursor.com/en/tools/mcp) — to work intelligently within your existing GitLab environment. See our [docs](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/) for a full list of tools included with 18.3. **This is only the start; additional tools are planned for 18.4.**\n> *“Bringing GitLab workflows directly into Cursor is a critical step in reducing friction for developers. By minimizing the need for context switching, teams can check issue status, review merge requests, and monitor pipeline results without ever leaving their coding environment. This integration is a natural fit for our shared customers, and we look forward to a long-term partnership with GitLab to continue enhancing developer productivity.”*\n>\n> \\- **Ricky Doar, VP of Field Engineering at Cursor**\n>\n> *“GitLab's MCP server and CLI agent support create powerful new ways for Amazon Q to integrate with development workflows. Amazon Q Developer can now connect directly through GitLab's remote MCP interface, while teams can delegate development tasks by simply @ mentioning Amazon Q CLI in issues and merge requests. The robust security and governance capabilities built into these integrations give enterprises the confidence to leverage AI coding tools while preserving their development standards. Our partnership with GitLab demonstrates AWS' ongoing commitment to expanding our AI ecosystem and making intelligent development tools accessible wherever developers work.\"*\n>\n> \\- **Deepak Singh, Vice President of Developer Agents and Experiences at AWS**\n\n* **CLI agent support for Claude Code, Codex, Amazon Q, Google Gemini, and opencode (Bring Your Own Key):** 18.3 introduces integrations that enable teams to delegate routine development work by @ mentioning their agents directly in issues or merge requests. When developers mention these AI assistants, they automatically read the surrounding context and repository code, then respond to the user's comment with either ready-to-review code changes or inline comments. These integrations require you to bring your own API key for the respective AI providers and keep all interactions natively within GitLab's interface while maintaining proper permissions and audit trails.\n\n**Note:** Third-party agents is a GitLab Premium Beta feature and only available to GitLab Duo Enterprise customers for evaluation.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111784124?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=\"Third Party Agents Flows Claude Code\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n> *“Bringing Claude Code directly into GitLab puts AI assistance where millions of developers already collaborate and ship code daily. The ability to mention Claude directly in issues and merge requests removes friction while maintaining quality with human oversight and review processes. This update brings Claude Code's capabilities to more places where teams work, making AI a natural part of their developer workflow.”*\n>\n> **\\- Cat Wu, Claude Code Product Lead, Anthropic**\n>\n> *“With GitLab's new agent integration in 18.3 you can use opencode within your existing workflows. You can @mention opencode in an issue or merge request and it'll run your agent right in your CI pipeline. This ability to configure and run opencode the way you want is the type of integration we know the open source community really values.”*\n>\n> **\\- Jay V., CEO, opencode**\n\n* **Agentic Chat support for Visual Studio IDE and GitLab UI available to all Premium and Ultimate customers:** With 18.3, you no longer need to context-switch between tools to access GitLab's full development lifecycle data. Our enhanced integrations bring the complete power of GitLab Duo into the GitLab UI as well as IDEs — expanding support from JetBrains and VS Code to now include Visual Studio. This helps developers stay in flow while accessing rich project context, deployment history, and team collaboration data directly within their preferred environment.\n* **Expanded AI model support:** GitLab Duo Self-Hosted now supports additional AI models, giving teams more flexibility in their AI-supported development workflows. You can now deploy open source OpenAI GPT models (20B and 120B parameters) through vLLM on your datacenter hardware, or through cloud services like Azure OpenAI and AWS Bedrock in your private cloud. Additionally, Anthropic's Claude 4 is available on AWS Bedrock\n\n### New automated development flows\n\nGitLab Flows coordinate multiple AI agents with pre-built instructions to autonomously handle those time-consuming, mundane tasks so developers can focus on the work that matters most.\n\nGitLab 18.3 comes with two new Flows:\n\n* **Issue to MR Flow enabling automated code generation from concept to completion in minutes:** This Flow automatically converts issues into actionable merge requests (MRs) by coordinating agents to analyze requirements, prepare comprehensive implementation plans, and generate production-grade code that's ready for review — helping you turn ideas into reviewable implementations in minutes, not hours.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111782058?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=\"Issue to MR\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n* **Convert CI File Flow built for seamless migration intelligence:** Our Convert CI File Flow streamlines migration workflows by having agents analyze existing CI/CD configurations and intelligently convert them to GitLab CI format with full pipeline compatibility. This helps eliminate the manual effort and potential errors of rewriting CI configurations from scratch, enabling teams to migrate entire deployment pipelines with confidence. 18.3 includes support for Jenkins migrations. Additional support is planned for future releases.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783724?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=\"Convert to CI Flow\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Intelligent code and search\n\nAI point solutions typically operate with limited visibility into isolated code snippets, but GitLab's Knowledge Graph provides agents with environment context to help inform faster and more intelligent responses.\n\n* **Knowledge Graph for real-time code intelligence:** With 18.3, GitLab's Knowledge Graph now delivers real-time code indexing to enable faster code searches, delivering more accurate and contextual results. By understanding the relationships between files, dependencies, and development patterns across your entire codebase, our agents are designed to provide insights that would take human developers hours to uncover — **and this is just the first step in unlocking the powerful capabilities that are planned for Knowledge Graph.**\n\n### Enterprise governance\n\nAI transparency and organizational control are critical challenges that can hold teams back from fully adopting AI-powered development tools, with [85% of executives agreeing that agentic AI will create unprecedented security challenges](https://about.gitlab.com/software-innovation-report/).\n\nThese new features in 18.3 help address concerns around data governance, compliance requirements, and the need for visibility into AI decision-making processes so organizations can integrate AI within their existing security and policy frameworks.\n\n* **Agent Insights for transparency through intelligence:** Our built-in agent tracking provides visibility into agent decision-making processes. Users can optimize workflows and follow best practices through transparent activity tracking.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783244?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=\"Agent Insights\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **GitLab Duo Code Review for Self-Hosted:** This brings the intelligence of GitLab Duo to organizations with strict data governance requirements by allowing teams to keep sensitive code in controlled environments.\n* **Hybrid model configurations for flexible AI deployment:** GitLab Duo Self-Hosted customers can now use hybrid model configurations, combining self-hosted AI models via their local AI gateway with GitLab's cloud models through GitLab's AI gateway, enabling access to various features.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783569?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=\"Self Hosted Models Code Review\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **Enhanced security with OAuth support:** Our MCP server now includes full OAuth 2.0 authentication support, enabling secure connections to protected resources and sensitive development environments. This implementation follows the draft OAuth specification for MCP, handling authorization flows, token management, and dynamic client registration.\n\n### Secure by Design platform: Governance that scales\n\nTrue platform security requires consistent application of governance principles across every layer of the development lifecycle. The same security fundamentals that make AI adoption safe — least-privilege access, centralized policy management, proactive monitoring, and granular permissions — must be embedded throughout the entire SDLC to create a cohesive, defense-in-depth approach.\n\nGitLab 18.3 strengthens the foundational controls that help protect your entire software supply chain with these new updates:\n\n* **Custom admin role:** Provides granular, purpose-built administrative permissions, replacing blanket admin access with precise, least-privilege controls. Instead of granting blanket administrative privileges that create security risks, organizations can now create specialized roles tailored to specific functions — platform teams managing runners and monitoring, support teams handling user management, and leadership accessing dashboards and usage statistics. With complete role lifecycle management through UI and API, audit logging, and auto-generated documentation, this feature enables true least-privilege administration while helping maintain operational efficiency and improve overall instance security.\n* **Instance-level compliance framework and security policy management**: Organizations can now designate a dedicated compliance group that has the authority to apply standardized frameworks and security policies directly to top-level groups, automatically cascading enforcement to all their subgroups and projects. This centralized approach eliminates the compliance adoption blocker of fragmented policy management while maintaining group autonomy for additional local policies.\n* **Enhanced violations reporting:** Teams now receive immediate notifications when unauthorized changes are made to MR approval rules, framework policies lack proper approvals, or time-based compliance controls are violated. By directly linking violations to specific compliance framework controls, teams get actionable insights that tell them exactly which requirement was breached, turning compliance from a reactive checkbox exercise into a proactive, integrated part of the development and security workflow.\n* **Fine-grained permissions for CI/CD job tokens:** Replaces broad token access with granular, explicit permissions that grant CI/CD jobs access only to specific API endpoints they actually need. Instead of allowing jobs blanket access to project resources, teams can now define precise permissions for deployments, packages, releases, environments, and other critical resources, reducing the attack surface and potential for privilege escalation.\n* **AWS Secrets Manager integration:** Teams using AWS Secrets Manager can now retrieve secrets directly in GitLab CI/CD jobs, simplifying the build and deploy processes. Secrets are accessed by a GitLab Runner using OpenID Connect protocol-based authentication, masked to prevent exposure in job logs, and destroyed after use. This approach eliminates the need to store secrets in variables and integrates cleanly into existing GitLab and AWS-based workflows. Developed in close collaboration with Deutsche Bahn and the AWS Secrets Manager team, this integration reflects our commitment to building solutions alongside customers to solve real-world challenges.\n\n### Artifact management: Securing your software supply chain\n\nWhen artifacts aren't properly governed, small changes can have big consequences. Mutable packages, overwritten container images, and inconsistent rules across tools can trigger production outages, introduce vulnerabilities, and create compliance gaps. For enterprise DevSecOps, secure, centralized artifact management is essential for keeping the software supply chain intact.\n\n#### Enterprise-grade artifact protection in 18.3\n\nBuilding on our comprehensive package protection capabilities, GitLab 18.3 adds important new features:\n\n* **Conan revisions support:** New in 18.3, [Conan revisions](https://docs.gitlab.com/user/packages/conan_2_repository/#conan-revisions) provide package immutability for C++ developers. When changes are made to a package without changing its version, Conan calculates unique identifiers to track these changes, enabling teams to maintain immutable packages while preserving version clarity.\n* **Enhanced Container Registry security:** Following the successful launch of [immutable container tags](https://docs.gitlab.com/user/packages/container_registry/immutable_container_tags/) in 18.2, we're seeing strong enterprise adoption. Once a tag is created that matches an immutable rule, no one — regardless of permission level — can modify that container image, preventing unintended changes to production dependencies.\n\nThese enhancements complement our existing protection capabilities for npm, PyPI, Maven, NuGet, Helm charts, and generic packages, enabling platform teams to implement consistent governance across their entire software supply chain — a requirement for organizations building secure internal developer platforms.\n\nUnlike standalone artifact solutions, GitLab's integrated approach eliminates context switching between tools while providing end-to-end traceability from code to deployment, enabling platform teams to implement consistent governance across their entire software delivery pipeline.\n\n### Embedded views: Real-time visibility and reports\n\nAs GitLab projects grow in complexity, teams find themselves navigating between issues, merge requests, epics, and milestones to maintain visibility into work status. The challenge lies in consolidating this information efficiently while ensuring teams have real-time access to project progress without context switching or breaking their flow.\n**Launching real-time work status visibility in 18.3**\nGitLab 18.3's [embedded views, powered by our powerful GitLab Query Language](https://docs.gitlab.com/user/glql/#embedded-views) (GLQL), eliminate context switching by bringing live project data directly into your workflow:\n* **Dynamic views:** Insert live GLQL queries in Markdown code blocks throughout wiki pages, epics, issues, and merge requests that automatically refresh with current project states each time you load the page.\n* **Contextual personalization:** Views automatically adapt using functions like `currentUser()` and `today()` to show relevant information for whoever is viewing, without manual configuration.\n* **Powerful filtering:** Filter by 25+ fields, including assignee, author, label, milestone, health status, and creation date.\n* **Display flexibility:** Present data as tables, lists, or numbered lists with customizable field selection, item limits, and sort orders to keep your views focused and actionable\n\nUnlike fragmented project management approaches, we've designed embedded views to maintain your workflow continuity while providing real-time visibility, enabling teams to make informed decisions without losing focus or switching between multiple tools and interfaces.\n\n> Learn about the [newest features in GitLab 18.3](https://docs.gitlab.com/releases/18/gitlab-18-3-released/).\n## Get started today\nGitLab 18.3 is available now for GitLab Premium and Ultimate users on GitLab.com and self-managed environments.\n\nGitLab Dedicated customers are now upgraded to 18.2 and will be able to use the features released with GitLab 18.3 next month.\n\nReady to experience the future of software engineering?[ Enable beta and experimental features for GitLab Duo](https://docs.gitlab.com/user/gitlab_duo/turn_on_off/#turn-on-beta-and-experimental-features) and start collaborating with AI agents that understand your complete development context.\n\nNew to GitLab? [Start your free trial](https://gitlab.com/-/trials/new) today and discover why the future of software engineering is human and AI collaboration, orchestrated through the world's most comprehensive DevSecOps platform.\n\n\u003Cp>\u003Csmall>\u003Cem>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 the forward-looking statements contained in this blog post are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements.\u003C/em>\u003C/p>\n\u003Cp>\u003Cem>Further information on risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those included in or contemplated by the forward-looking statements contained in this blog post are included under the caption “Risk Factors” and elsewhere in the filings and reports we make with the Securities and Exchange Commission. We do not undertake any obligation to update or release any revisions to any forward-looking statement or to report any events or circumstances after the date of this blog post or to reflect the occurrence of unanticipated events, except as required by law.\u003C/em>\u003C/small>\u003C/p>","ai-ml",{"template":13,"slug":14,"featured":15},"BlogPost","gitlab-18-3-expanding-ai-orchestration-in-software-engineering",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":26,"body":10},"Learn how we're advancing human-AI collaboration with enhanced Flows, enterprise governance, and seamless tool integration.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1755711502/wuuadis1pza3zehqohcc.png",[21,22,23,24,25],"product","AI/ML","DevSecOps platform","features","security","2025-08-21","md",null,{},"/en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering","---\nseo:\n  config:\n    noIndex: false\n  title: 'GitLab 18.3: Expanding AI orchestration in software engineering'\n  description: Learn how we're advancing human-AI collaboration with enhanced\n    Flows, enterprise governance, and seamless tool integration.\ntitle: 'GitLab 18.3: Expanding AI orchestration in software engineering'\ndescription: Learn how we're advancing human-AI collaboration with enhanced Flows, enterprise governance, and seamless tool integration.\nauthors:\n  - Bill Staples\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1755711502/wuuadis1pza3zehqohcc.png\ntags:\n  - product\n  - AI/ML\n  - DevSecOps platform\n  - features\n  - security\ncategory: ai-ml\ndate: '2025-08-21'\nslug: gitlab-18-3-expanding-ai-orchestration-in-software-engineering\nfeatured: true\ntemplate: BlogPost\n---\n\nToday, GitLab is a comprehensive DevSecOps platform, unifying every stage of the software lifecycle. Building on that foundation, we're on a journey toward becoming the world's first AI-native platform for software engineering. At GitLab, we believe the future of software engineering is an inherently human and AI collaboration, and we want to bring the very best AI capabilities to every GitLab user.\n\nThis transformation is happening at three distinct layers that go beyond what other AI dev tools are doing:\n\n![AI-native transformation slide visualizing what's laid out below](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755762266/iwuugge3cxweiyvi0yjk.png)\n\n**First, we are a system of record.** Our unified data platform holds your most valuable digital assets. This includes your source code and intellectual property, as well as a wealth of unstructured data spanning project plans, bug backlogs, CI/CD configurations, deployment histories, security reports, and compliance data. This creates a treasure trove of contextual data that remains securely within your GitLab environment, unavailable to generic agents or large language models.\n\n**Second, we act as your software control plane.** We orchestrate your most critical business processes through Git repositories, REST APIs, and webhook-based interfaces that power your end-to-end software delivery. Many of our customers consider this a tier-0 dependency that their critical business processes rely on daily.\n\n**Third, we deliver a powerful user experience.** We deliver an integrated interface that helps eliminate the costly context-switching that slows down most engineering teams. With complete lifecycle visibility and collaboration tools in one platform, over 50 million registered users and our vast community depend on GitLab to get their work done. This expertise positions GitLab uniquely to pioneer intuitive human-to-AI collaboration that amplifies team productivity while preserving the workflows that our users know and trust.\n\n**Extending our platform with AI natively integrated at every layer**\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) integrates and extends all three of these layers. It is designed for extensibility and interoperability, enabling customers and partners to build solutions that create even more value. Our open platform approach emphasizes seamless connectivity with external AI tools and systems while being deeply integrated into our existing stack at all three layers.\n\n* First, we're extending our unified data platform with a **Knowledge Graph,** which indexes and stitches together code with all of the rest of your unstructured data, specifically optimized for agentic access. AI thrives on context, and we believe this will not only accelerate reasoning and inference by agents but also deliver lower-cost and higher-quality agentic outcomes.\n* Second, we're adding an important **Orchestration Layer** to our existing Control Plane in three distinct parts: enabling agents and flows to register as subscribers for GitLab SDLC events, building a new orchestration engine that allows for purpose-built, multi-agent flows, and exposing GitLab tools, agents, and flows via MCP and standard protocols for unparalleled interoperability.\n* Finally, we're extending the **GitLab experience** to deliver first-class agents and agent flows across the entire software development lifecycle. You'll be able to assign async tasks to agents, @ mention them in comments, and create custom agents with context specific to your workflows — but more importantly, GitLab is shipping native agents for every stage of development while unlocking a rich ecosystem of third-party agents. This creates true human-to-AI collaboration where agents become as natural to work with as your human teammates.\n\nWatch this video to see what's coming in 18.3 and beyond, or read on.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111796316?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=\"GitLab_18.3 Release_081925_MP_v1\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## What's new in GitLab 18.3\n\nWith 18.2, we introduced specialized [AI agents](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=Agents%20that%20work%20out%20of%20the%20box) that work alongside developers across the software development lifecycle, plus our [Software Development Flow](\u003Chttps://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=we%20are%20building%3A-,Software%20Development%20Flow,-(now%20in%20beta>) — a powerful feature that gives users the ability to orchestrate multiple agents to plan, implement, and test code changes end-to-end.\n\nGitLab 18.3 introduces expanded integrations and interoperability, more Flows, and enhanced context awareness across the entire software development lifecycle.\n\n### Expanded integrations and interoperability\n\nWe're delivering comprehensive AI extensibility through both first-party GitLab agents and a rich ecosystem of third-party agents, all with full access to project context and data. This approach maintains native GitLab workflows and governance while providing the flexibility to choose preferred tools through highly integrated orchestration between these agents and GitLab's core platform. Teams gain enhanced AI functionality while preserving key integration, oversight, and user experience benefits.\n\n* **MCP server - Universal AI integration:** GitLab's MCP ([Model Context Protocol](https://about.gitlab.com/topics/ai/model-context-protocol/)) server enables AI systems to securely integrate directly with your GitLab projects and development processes. This standardized interface eliminates custom integration overhead and allows your AI tools — including [Cursor](https://docs.cursor.com/en/tools/mcp) — to work intelligently within your existing GitLab environment. See our [docs](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/) for a full list of tools included with 18.3. **This is only the start; additional tools are planned for 18.4.**\n> *“Bringing GitLab workflows directly into Cursor is a critical step in reducing friction for developers. By minimizing the need for context switching, teams can check issue status, review merge requests, and monitor pipeline results without ever leaving their coding environment. This integration is a natural fit for our shared customers, and we look forward to a long-term partnership with GitLab to continue enhancing developer productivity.”*\n>\n> \\- **Ricky Doar, VP of Field Engineering at Cursor**\n>\n> *“GitLab's MCP server and CLI agent support create powerful new ways for Amazon Q to integrate with development workflows. Amazon Q Developer can now connect directly through GitLab's remote MCP interface, while teams can delegate development tasks by simply @ mentioning Amazon Q CLI in issues and merge requests. The robust security and governance capabilities built into these integrations give enterprises the confidence to leverage AI coding tools while preserving their development standards. Our partnership with GitLab demonstrates AWS' ongoing commitment to expanding our AI ecosystem and making intelligent development tools accessible wherever developers work.\"*\n>\n> \\- **Deepak Singh, Vice President of Developer Agents and Experiences at AWS**\n\n* **CLI agent support for Claude Code, Codex, Amazon Q, Google Gemini, and opencode (Bring Your Own Key):** 18.3 introduces integrations that enable teams to delegate routine development work by @ mentioning their agents directly in issues or merge requests. When developers mention these AI assistants, they automatically read the surrounding context and repository code, then respond to the user's comment with either ready-to-review code changes or inline comments. These integrations require you to bring your own API key for the respective AI providers and keep all interactions natively within GitLab's interface while maintaining proper permissions and audit trails.\n\n**Note:** Third-party agents is a GitLab Premium Beta feature and only available to GitLab Duo Enterprise customers for evaluation.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111784124?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=\"Third Party Agents Flows Claude Code\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n> *“Bringing Claude Code directly into GitLab puts AI assistance where millions of developers already collaborate and ship code daily. The ability to mention Claude directly in issues and merge requests removes friction while maintaining quality with human oversight and review processes. This update brings Claude Code's capabilities to more places where teams work, making AI a natural part of their developer workflow.”*\n>\n> **\\- Cat Wu, Claude Code Product Lead, Anthropic**\n>\n> *“With GitLab's new agent integration in 18.3 you can use opencode within your existing workflows. You can @mention opencode in an issue or merge request and it'll run your agent right in your CI pipeline. This ability to configure and run opencode the way you want is the type of integration we know the open source community really values.”*\n>\n> **\\- Jay V., CEO, opencode**\n\n* **Agentic Chat support for Visual Studio IDE and GitLab UI available to all Premium and Ultimate customers:** With 18.3, you no longer need to context-switch between tools to access GitLab's full development lifecycle data. Our enhanced integrations bring the complete power of GitLab Duo into the GitLab UI as well as IDEs — expanding support from JetBrains and VS Code to now include Visual Studio. This helps developers stay in flow while accessing rich project context, deployment history, and team collaboration data directly within their preferred environment.\n* **Expanded AI model support:** GitLab Duo Self-Hosted now supports additional AI models, giving teams more flexibility in their AI-supported development workflows. You can now deploy open source OpenAI GPT models (20B and 120B parameters) through vLLM on your datacenter hardware, or through cloud services like Azure OpenAI and AWS Bedrock in your private cloud. Additionally, Anthropic's Claude 4 is available on AWS Bedrock\n\n### New automated development flows\n\nGitLab Flows coordinate multiple AI agents with pre-built instructions to autonomously handle those time-consuming, mundane tasks so developers can focus on the work that matters most.\n\nGitLab 18.3 comes with two new Flows:\n\n* **Issue to MR Flow enabling automated code generation from concept to completion in minutes:** This Flow automatically converts issues into actionable merge requests (MRs) by coordinating agents to analyze requirements, prepare comprehensive implementation plans, and generate production-grade code that's ready for review — helping you turn ideas into reviewable implementations in minutes, not hours.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111782058?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=\"Issue to MR\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n* **Convert CI File Flow built for seamless migration intelligence:** Our Convert CI File Flow streamlines migration workflows by having agents analyze existing CI/CD configurations and intelligently convert them to GitLab CI format with full pipeline compatibility. This helps eliminate the manual effort and potential errors of rewriting CI configurations from scratch, enabling teams to migrate entire deployment pipelines with confidence. 18.3 includes support for Jenkins migrations. Additional support is planned for future releases.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783724?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=\"Convert to CI Flow\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Intelligent code and search\n\nAI point solutions typically operate with limited visibility into isolated code snippets, but GitLab's Knowledge Graph provides agents with environment context to help inform faster and more intelligent responses.\n\n* **Knowledge Graph for real-time code intelligence:** With 18.3, GitLab's Knowledge Graph now delivers real-time code indexing to enable faster code searches, delivering more accurate and contextual results. By understanding the relationships between files, dependencies, and development patterns across your entire codebase, our agents are designed to provide insights that would take human developers hours to uncover — **and this is just the first step in unlocking the powerful capabilities that are planned for Knowledge Graph.**\n\n### Enterprise governance\n\nAI transparency and organizational control are critical challenges that can hold teams back from fully adopting AI-powered development tools, with [85% of executives agreeing that agentic AI will create unprecedented security challenges](https://about.gitlab.com/software-innovation-report/).\n\nThese new features in 18.3 help address concerns around data governance, compliance requirements, and the need for visibility into AI decision-making processes so organizations can integrate AI within their existing security and policy frameworks.\n\n* **Agent Insights for transparency through intelligence:** Our built-in agent tracking provides visibility into agent decision-making processes. Users can optimize workflows and follow best practices through transparent activity tracking.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783244?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=\"Agent Insights\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **GitLab Duo Code Review for Self-Hosted:** This brings the intelligence of GitLab Duo to organizations with strict data governance requirements by allowing teams to keep sensitive code in controlled environments.\n* **Hybrid model configurations for flexible AI deployment:** GitLab Duo Self-Hosted customers can now use hybrid model configurations, combining self-hosted AI models via their local AI gateway with GitLab's cloud models through GitLab's AI gateway, enabling access to various features.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783569?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=\"Self Hosted Models Code Review\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **Enhanced security with OAuth support:** Our MCP server now includes full OAuth 2.0 authentication support, enabling secure connections to protected resources and sensitive development environments. This implementation follows the draft OAuth specification for MCP, handling authorization flows, token management, and dynamic client registration.\n\n### Secure by Design platform: Governance that scales\n\nTrue platform security requires consistent application of governance principles across every layer of the development lifecycle. The same security fundamentals that make AI adoption safe — least-privilege access, centralized policy management, proactive monitoring, and granular permissions — must be embedded throughout the entire SDLC to create a cohesive, defense-in-depth approach.\n\nGitLab 18.3 strengthens the foundational controls that help protect your entire software supply chain with these new updates:\n\n* **Custom admin role:** Provides granular, purpose-built administrative permissions, replacing blanket admin access with precise, least-privilege controls. Instead of granting blanket administrative privileges that create security risks, organizations can now create specialized roles tailored to specific functions — platform teams managing runners and monitoring, support teams handling user management, and leadership accessing dashboards and usage statistics. With complete role lifecycle management through UI and API, audit logging, and auto-generated documentation, this feature enables true least-privilege administration while helping maintain operational efficiency and improve overall instance security.\n* **Instance-level compliance framework and security policy management**: Organizations can now designate a dedicated compliance group that has the authority to apply standardized frameworks and security policies directly to top-level groups, automatically cascading enforcement to all their subgroups and projects. This centralized approach eliminates the compliance adoption blocker of fragmented policy management while maintaining group autonomy for additional local policies.\n* **Enhanced violations reporting:** Teams now receive immediate notifications when unauthorized changes are made to MR approval rules, framework policies lack proper approvals, or time-based compliance controls are violated. By directly linking violations to specific compliance framework controls, teams get actionable insights that tell them exactly which requirement was breached, turning compliance from a reactive checkbox exercise into a proactive, integrated part of the development and security workflow.\n* **Fine-grained permissions for CI/CD job tokens:** Replaces broad token access with granular, explicit permissions that grant CI/CD jobs access only to specific API endpoints they actually need. Instead of allowing jobs blanket access to project resources, teams can now define precise permissions for deployments, packages, releases, environments, and other critical resources, reducing the attack surface and potential for privilege escalation.\n* **AWS Secrets Manager integration:** Teams using AWS Secrets Manager can now retrieve secrets directly in GitLab CI/CD jobs, simplifying the build and deploy processes. Secrets are accessed by a GitLab Runner using OpenID Connect protocol-based authentication, masked to prevent exposure in job logs, and destroyed after use. This approach eliminates the need to store secrets in variables and integrates cleanly into existing GitLab and AWS-based workflows. Developed in close collaboration with Deutsche Bahn and the AWS Secrets Manager team, this integration reflects our commitment to building solutions alongside customers to solve real-world challenges.\n\n### Artifact management: Securing your software supply chain\n\nWhen artifacts aren't properly governed, small changes can have big consequences. Mutable packages, overwritten container images, and inconsistent rules across tools can trigger production outages, introduce vulnerabilities, and create compliance gaps. For enterprise DevSecOps, secure, centralized artifact management is essential for keeping the software supply chain intact.\n\n#### Enterprise-grade artifact protection in 18.3\n\nBuilding on our comprehensive package protection capabilities, GitLab 18.3 adds important new features:\n\n* **Conan revisions support:** New in 18.3, [Conan revisions](https://docs.gitlab.com/user/packages/conan_2_repository/#conan-revisions) provide package immutability for C++ developers. When changes are made to a package without changing its version, Conan calculates unique identifiers to track these changes, enabling teams to maintain immutable packages while preserving version clarity.\n* **Enhanced Container Registry security:** Following the successful launch of [immutable container tags](https://docs.gitlab.com/user/packages/container_registry/immutable_container_tags/) in 18.2, we're seeing strong enterprise adoption. Once a tag is created that matches an immutable rule, no one — regardless of permission level — can modify that container image, preventing unintended changes to production dependencies.\n\nThese enhancements complement our existing protection capabilities for npm, PyPI, Maven, NuGet, Helm charts, and generic packages, enabling platform teams to implement consistent governance across their entire software supply chain — a requirement for organizations building secure internal developer platforms.\n\nUnlike standalone artifact solutions, GitLab's integrated approach eliminates context switching between tools while providing end-to-end traceability from code to deployment, enabling platform teams to implement consistent governance across their entire software delivery pipeline.\n\n### Embedded views: Real-time visibility and reports\n\nAs GitLab projects grow in complexity, teams find themselves navigating between issues, merge requests, epics, and milestones to maintain visibility into work status. The challenge lies in consolidating this information efficiently while ensuring teams have real-time access to project progress without context switching or breaking their flow.\n**Launching real-time work status visibility in 18.3**\nGitLab 18.3's [embedded views, powered by our powerful GitLab Query Language](https://docs.gitlab.com/user/glql/#embedded-views) (GLQL), eliminate context switching by bringing live project data directly into your workflow:\n* **Dynamic views:** Insert live GLQL queries in Markdown code blocks throughout wiki pages, epics, issues, and merge requests that automatically refresh with current project states each time you load the page.\n* **Contextual personalization:** Views automatically adapt using functions like `currentUser()` and `today()` to show relevant information for whoever is viewing, without manual configuration.\n* **Powerful filtering:** Filter by 25+ fields, including assignee, author, label, milestone, health status, and creation date.\n* **Display flexibility:** Present data as tables, lists, or numbered lists with customizable field selection, item limits, and sort orders to keep your views focused and actionable\n\nUnlike fragmented project management approaches, we've designed embedded views to maintain your workflow continuity while providing real-time visibility, enabling teams to make informed decisions without losing focus or switching between multiple tools and interfaces.\n\n> Learn about the [newest features in GitLab 18.3](https://docs.gitlab.com/releases/18/gitlab-18-3-released/).\n## Get started today\nGitLab 18.3 is available now for GitLab Premium and Ultimate users on GitLab.com and self-managed environments.\n\nGitLab Dedicated customers are now upgraded to 18.2 and will be able to use the features released with GitLab 18.3 next month.\n\nReady to experience the future of software engineering?[ Enable beta and experimental features for GitLab Duo](https://docs.gitlab.com/user/gitlab_duo/turn_on_off/#turn-on-beta-and-experimental-features) and start collaborating with AI agents that understand your complete development context.\n\nNew to GitLab? [Start your free trial](https://gitlab.com/-/trials/new) today and discover why the future of software engineering is human and AI collaboration, orchestrated through the world's most comprehensive DevSecOps platform.\n\n\u003Cp>\u003Csmall>\u003Cem>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 the forward-looking statements contained in this blog post are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements.\u003C/em>\u003C/p>\n\u003Cp>\u003Cem>Further information on risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those included in or contemplated by the forward-looking statements contained in this blog post are included under the caption “Risk Factors” and elsewhere in the filings and reports we make with the Securities and Exchange Commission. We do not undertake any obligation to update or release any revisions to any forward-looking statement or to report any events or circumstances after the date of this blog post or to reflect the occurrence of unanticipated events, except as required by law.\u003C/em>\u003C/small>\u003C/p>\n",{"config":33,"title":5,"description":17},{"noIndex":34},false,"en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering",[21,37,38,24,25],"aiml","devsecops-platform",[21,22,23,24,25],"5WOmKLUQZAhLOuCbNCQcYRzxit2w4Fw7VnoYOpXSyu8",{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":382,"minimal":413,"duo":432,"switchNav":441,"pricingDeployment":452},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab logo","header",{"text":48,"config":49},"Get free trial",{"href":50,"dataGaName":51,"dataGaLocation":46},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free 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will train on your data: Opt out with GitLab","Learn why Atlassian's latest move is a threat to data governance and how GitLab's approach helps ensure your customers' data stays private and protected.",[734],"Jessica Hurwitz","2026-05-04","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773866173/vte9qh8rriznvyclhkes.png","Starting August 17, 2026, Atlassian will begin collecting customer metadata and in-app content from Jira, Confluence, and other cloud products to train its AI offerings, including Rovo and Rovo Dev. This announcement comes after [GitHub recently changed its Copilot data usage policy](https://about.gitlab.com/blog/github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call/). **Taken together, these changes suggest opt-out-by-default is becoming the industry norm. GitLab takes the opposite position: no data collection, no AI training on customer data, no matter what tier you're on.**\n\n[Atlassian's change](https://www.atlassian.com/trust/ai/data-contribution) is enabled by default for all cloud customers and affects roughly 300,000 organizations. For customers on the Free, Standard, and Premium tiers, metadata collection is mandatory and cannot be turned off. Only Enterprise-tier customers have the option to opt out. This policy change deserves a close read if your engineering, IT, and program management teams run on Atlassian because they are most exposed by this change — and least likely to have been consulted before it happened.\n\nAlthough the underlying governance questions are the same for both Atlassian and GitHub's changes, the data at risk is different. Where GitHub's change concerned source code and developer interactions, Atlassian's reaches into project plans, internal documentation, workflow configurations, and operational metadata across Jira, Confluence, and the broader Atlassian stack. **For organizations that rely on these tools as their system of record for how work gets planned and delivered, the implications run deep.**\n\n## What changed and what it means for your data\n\nAtlassian will collect two categories of information: \n\n- **Metadata:** de-identified operational signals like story points, sprint dates, and SLA values, including data from its Teamwork Graph and connected third-party apps  \n- **In-app content:** user-generated material such as Confluence page content, Jira issue titles, descriptions, and comments\n\nAtlassian says it will apply de-identification and aggregation before training. Collected data may be retained for up to seven years, with in-app data removed within 30 days of opt-out and models retrained within 90 days.\n\nThere are some exclusions: Customers using customer-managed encryption keys, Atlassian Government Cloud, Isolated Cloud, or those with HIPAA requirements are carved out from collection. But for the vast majority of Atlassian's cloud customer base, data collection will start unless you pay for the Enterprise tier and actively flip the switch.\n\nThis reverses Atlassian's prior stated position that customer data would not be used to train or improve AI services. Organizations that adopted Jira and Confluence to manage their most sensitive planning workflows, sprint boards, security tickets, incident postmortems, and internal documentation will soon be contributing that content to Atlassian's AI training pipeline, without ever being asked.\n\n## The governance gap in \"opt-out by default\"\n\nOpt-out-by-default data collection for AI training is an emerging pattern across the software industry. It raises the same set of questions every time: How does this interact with existing data processing agreements? Does the vendor's definition of \"metadata\" match what your legal and security teams would consider non-sensitive data?\n\n**For many organizations, the answer to these questions is \"we don't know.\"** \n\nWhen a vendor changes its data practices through a terms-of-service update, the burden falls on the customer to notice, evaluate the implications, and act within the window the vendor provides. \n\nThe mandatory nature of metadata collection on Free, Standard, and Premium tiers makes this more acute. The only exit is upgrading to Enterprise, which requires a minimum of 801 users and custom pricing that would represent a significant cost jump for teams that aren't there yet. Data protection, in other words, is now a purchasing decision.\n\nThe tiered structure also introduces a subtler problem. Metadata like story points, sprint velocity, SLA metrics, and task classifications may seem innocuous in isolation, but in aggregate they reveal project structure, team performance patterns, and delivery cadence. For organizations in competitive industries, that operational intelligence has real value, and \"de-identified\" does not necessarily mean \"non-sensitive\" once patterns are reconstructable at scale.\n\n## Why this matters more for Atlassian-stack organizations\n\nIn Atlassian-based organizations, Jira has been the center how teams plan, track, and deliver work. It’s the source of truth for sprint planning, bug tracking, release management, portfolio coordination, and cross-functional project execution. \n\nIn regulated industries like financial services, public sector and manufacturing, Jira and Confluence together hold sensitive operational data that may be subject to compliance requirements. The risk compounds for organizations that have expanded beyond Jira into the broader Atlassian ecosystem.\n\nWhen you run Jira, Confluence, Bitbucket, and Bamboo together, the surface area of data now feeding into AI training spans your project plans, internal documentation, source code metadata, and CI/CD configurations — each of which security and compliance teams would want to review before sharing with a vendor's training pipeline.\n\nAtlassian’s Teamwork Graph connectors add another dimension for customers who have integrated third-party tools, such as Slack, Figma, Google Drive, Salesforce, and ServiceNow, into their environment. Teamwork Graph connectors index relationship and activity signals from these connected apps, which means the metadata Atlassian collects will not be limited to what lives inside Atlassian products. For security and compliance teams accustomed to evaluating data flows on a per-vendor basis, this cross-platform reach complicates the assessment considerably.\n\nOrganizations that are already navigating [Atlassian's push from Data Center](https://about.gitlab.com/blog/atlassian-ending-data-center-as-gitlab-maintains-deployment-choice/) and Server editions to the cloud face a compounding challenge. Adding default AI data collection to that migration path raises the stakes further: **The question is no longer just \"do we move to Atlassian Cloud?\" but \"do we move to Atlassian Cloud knowing our data will feed AI training unless we're on the most expensive tier?\"**\n\n## What regulated industries should be evaluating now\n\nThe compliance implications vary by sector, but the obligation to reassess is consistent.\n\nIn financial services, frameworks like [SR 11-7](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) and [DORA](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) require documented, auditable oversight of third-party technology providers, including how those providers handle data. In the public sector, [NIST 800-53](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and [FISMA](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) make controlling where sensitive data flows a foundational requirement. In healthcare, [HIPAA](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties. \n\nAcross the board, a material change in a vendor's data practices, such as Atlassian moving from \"we don't train on your data\" to \"we do, by default,\" triggers a documentation and risk reassessment obligation. \n\nInstitutions operating under the [EU AI Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng) face an additional dimension: opt-out framing aligns with U.S. norms, while European regulators generally expect opt-in consent for data processing of this nature.\n\nIf your model risk or vendor management team documented Atlassian's data handling controls before this announcement, the question isn't whether this change triggers a reassessment obligation. It does. The question is whether your team can take action before August 17.\n\n## What to look for in your platform vendors\n\nCTOs and CISOs across regulated industries need to adopt AI in a way they can explain to regulators, boards, and customers. Because of this, GitLab operates within the following set of principles:\n\n**Unconditional data commitments, not tier-dependent protections.** Regulated organizations need to know, with specificity, what happens to their data. A commitment that varies by plan tier, or that requires action before a deadline, introduces exactly the kind of uncontrolled variable that keeps CISOs up at night.\n\n**Transparency and auditability.** Model risk management frameworks require organizations to understand the AI systems they deploy, including the training data and third parties involved. Vendors who cannot answer these questions clearly create documentation risk.\n\n**Separation between customer data and vendor AI training.** When a platform vendor trains models on customer usage data, workflows and operational patterns become inputs to a system that also serves competitors. For organizations where project structure or delivery cadence represents competitive advantage, that exposure matters.\n\n## How GitLab's approach differs\n\nGitLab doesn't train on customer data — at any tier, full stop. AI vendors powering GitLab Duo features are contractually prohibited from using customer inputs or outputs for their own purposes, [a commitment GitLab CEO Bill Staples](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf) has consistently reiterated.\n\n[GitLab's AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) documents exactly which models power which features, how data is handled, and what vendor commitments are in place. [GitLab's AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including any material changes to how AI vendors treat customer data. For institutions managing third-party AI risk under DORA or similar frameworks, vendor continuity and concentration are active governance concerns, and having a documented plan for both is part of what responsible AI tooling looks like.\n\nFor organizations that require AI processing to stay within their own infrastructure, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) is available with GitLab Self-Managed deployments, including support for integration with self-hosted AI models. This means prompts and code never leave the customer's environment. GitLab also provides IP indemnification for Duo-generated output, with no filters required and no activation steps needed. Where your data lives remains your choice, no matter your deployment model or subscription tier.\n\n> Whether your organization stays on Atlassian or begins evaluating alternatives, the conversation about who controls your data and how it gets used should be happening now. **The August 17 deadline is approaching, but you still have time to [try GitLab Ultimate with Duo Agent Platform for free today](https://gitlab.com/-/trials/new).**",[22,21],{"featured":15,"template":13,"slug":740},"atlassian-will-train-on-your-data-opt-out-with-gitlab",{"content":742,"config":751},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"body":749,"category":11,"tags":750},"GitLab and Anthropic: Governed AI for enterprise development","GitLab deepens its Anthropic Claude integration, bringing governed AI, access to new models, and cloud flexibility to enterprise software development.",[746],"Stuart Moncada","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776457632/llddiylsgwuze0u1rjks.png","2026-04-28","For enterprise and public sector leaders, the tension is familiar: Software teams need to move faster with AI, while security, compliance, and regulatory expectations only get more stringent. GitLab deepens its Anthropic Claude integration so organizations get access to newly released Claude models inside GitLab’s intelligent orchestration platform where governance, compliance, and auditability already run.\n\nClaude powers capabilities across GitLab Duo Agent Platform as the default model out of the box, across a variety of use cases from code generation and review to agentic chat and vulnerability resolution. If you've used GitLab Duo, you've already experienced how Duo agents automate workflows across the entire software development lifecycle (SDLC).\n\nThis accelerates the integration of Claude’s capabilities into GitLab, broadens how enterprises can deploy them, and reinforces what makes GitLab fundamentally different as a platform for software development and engineering: governance, compliance, and auditability built into every AI interaction.\n\n> \"GitLab Duo has accelerated how our teams plan, build, and ship software. The combination of Anthropic's Claude and GitLab's platform means we're getting more capable AI without changing how we work or how it is governed.\"\n>\n> – Mans Booijink, Operations Manager, Cube\n\n## The real differentiator: Governed AI\n\nWith GitLab, governance controls and auditing are built into the SDLC. When Claude suggests a code change through the GitLab Duo Agent Platform, that suggestion flows through the same merge request process, the same approval rules, the same security scanning, and the same audit trail as every other change. AI doesn't get a shortcut around your controls. It operates within them.\n\nAs GitLab moves deeper into agentic software development, where AI autonomously handles well-defined tasks, the governance layer becomes more important. An AI agent that can open a merge request, help resolve a vulnerability, or refactor a service needs to be auditable, attributable, and subject to the same policy enforcement as a human developer. That requirement is an architectural decision GitLab made from the start, and one that grows more consequential as AI agents take on broader responsibilities.\n\n## Enterprise deployment flexibility\n\nThis also expands how organizations access the latest Claude models through GitLab. Claude is available within GitLab through Google Cloud's Vertex AI and Amazon Bedrock, which means enterprises can route AI workloads through the hyperscaler commitments and cloud governance frameworks they already have in place. No separate vendor contract. No new data residency questions. Your existing Google Cloud or AWS relationship is the on-ramp. \n\nGitLab is now also available in the [Claude Marketplace](https://claude.com/platform/marketplace), allowing customers to purchase GitLab Credits and apply them toward existing Anthropic spending commitments – consolidating AI spend and simplifying how teams discover and procure GitLab alongside their Anthropic investments.\n\n## Advancing an agentic future\n\nGitLab's vision for agentic software development, where AI handles defined tasks autonomously across planning, coding, testing, securing, and deploying, requires models with strong reasoning, reliability, and safety characteristics. It also requires a platform where those autonomous actions are fully governed.\n\nAgentic workflows demand models with strong reasoning, reliability, and safety characteristics, criteria that guide how GitLab selects and integrates AI model partners. And GitLab's governance framework helps ensure that as AI agents assume more advanced development work, enterprises maintain full visibility and control over what those agents do, when they do it, and how changes are tracked.\n\n## What this means for GitLab customers\n\nIf you're already using GitLab Duo Agent Platform, you'll get access to Claude models and deeper AI assistance across your software development lifecycle, all within the governance framework you already rely on.\n\nIf you're evaluating AI-powered software development platforms, you shouldn't have to choose between advanced AI capabilities and enterprise control. This strategic integration is built to deliver both.\n\n> Want to learn more about GitLab Duo Agent Platform? [Get a demo or start a free trial today](https://about.gitlab.com/gitlab-duo-agent-platform/).",[22,21,289],{"featured":15,"template":13,"slug":752},"gitlab-and-anthropic-governed-ai-for-enterprise-development",{"content":754,"config":764},{"title":755,"description":756,"authors":757,"heroImage":759,"date":760,"body":761,"category":11,"tags":762},"Give your AI agent direct, structured GitLab access with glab CLI","The GitLab CLI (glab) provides AI agents structured, reliable access to projects via the MCP, eliminating friction. This tutorial shows how you can speed up code review and issue triage.",[758],"Kai Armstrong","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-27","\nWhen teams use GitLab Duo, Claude, Cursor, and other AI assistants, more of the development workflow runs through an AI agent acting on your behalf — reading issues, reviewing merge requests, running pipelines, and helping you ship faster. Most developers are already using the GitLab CLI (`glab`) from the terminal to interact with GitLab. Combining the two is a natural next step.\n\n\nThe problem is that without the right tools, AI agents are essentially guessing when it comes to your GitLab projects. They might hallucinate the details of an issue they've never seen, summarize a merge request based on stale training data rather than its actual state, or require you to manually copy context from a browser tab and paste it into a chat window just to get started. Every one of those workarounds is friction: it slows you down, introduces the possibility of error, and puts a hard ceiling on what your agent can actually do on your behalf. `glab` changes that by giving agents a direct, reliable interface to your projects.\n\n\nWith `glab`, your agent fetches what it needs directly from GitLab, acts on it, and reports back — so you spend less time relaying information and more time on the work that matters.\n\n\nIn this tutorial, you'll learn how to use `glab` to give AI agents structured, reliable access to your GitLab projects. You'll also discover how that unlocks a faster, more capable development workflow.\n\n\n## How to connect your AI agent to GitLab through MCP\n\n\nThe most direct way to supercharge your AI workflow is to give your AI agent native access to `glab` through Model Context Protocol ([MCP](https://about.gitlab.com/topics/ai/model-context-protocol/)).\n\n\n MCP is an open standard that lets AI tools discover and use external capabilities at runtime. Once connected, your AI assistant can read issues, comment on merge requests, check pipeline status, and write back to GitLab, all without copying anything from the UI or writing a single API call yourself.\n\n\n To get started, run:\n\n\n ```shell\n # Start the glab MCP server\n glab mcp serve\n ```\n\n\n Once your MCP client is configured, your AI can answer questions like *\"What's the status of my open MRs?\"* or *\"Are there any failing pipelines on main?\"* by querying GitLab directly, not scraping the web UI, not relying on stale training data. See the [full setup docs](https://docs.gitlab.com/cli/) for configuration steps for Claude Code, Cursor, and other editors.\n\n\n One detail worth knowing: `glab` automatically adds `--output json` when invoked through MCP, for any command that supports it. Your agent gets clean, structured data without you needing to think about output formats. And because `glab` uses the official MCP SDK, it stays compatible as the\n protocol evolves.\n\n\n We've also been deliberate about *which* commands are exposed through MCP. Commands that require interactive terminal input are intentionally\n excluded, so your agent never gets stuck waiting for input that will never come. What's exposed is what actually works reliably in an agent context.\n\n\n ## Let your AI participate in code review\n\n\n Most developers have a backlog of MRs waiting for review. It's one of the most time-consuming parts of the job and one of the best places to put\n AI to work. With `glab`, your agent doesn't just observe your review queue, it can work through it with you.\n\n\n ### See exactly what still needs addressing\n\n\n Start with this:\n\n\n ```shell\n glab mr view 2677 --comments --unresolved --output json\n ```\n\n\n This input returns the full MR: metadata, description, and every\n unresolved discussion, as a single structured JSON payload. Hand that to\n your AI and it has everything it needs: which threads are open, what the\n reviewer asked for, and in what context. No tab-switching, no copy-pasting\n individual comments.\n\n\n \n ```json\n {\n   \"id\": 2677,\n   \"title\": \"feat: add OAuth2 support\",\n   \"state\": \"opened\",\n   \"author\": { \"username\": \"jdwick\" },\n   \"labels\": [\"backend\", \"needs-review\"],\n   \"blocking_discussions_resolved\": false,\n   \"discussions\": [\n     {\n       \"id\": \"3107030349\",\n       \"resolved\": false,\n       \"notes\": [\n         {\n           \"author\": { \"username\": \"dmurphy\" },\n           \"body\": \"This error handling will swallow panics — consider wrapping with recover()\",\n           \"created_at\": \"2026-03-14T09:23:11.000Z\"\n         }\n       ]\n     },\n     {\n       \"id\": \"3107030412\",\n       \"resolved\": false,\n       \"notes\": [\n         {\n           \"author\": { \"username\": \"sreeves\" },\n           \"body\": \"Token refresh logic needs a test for the expired token case\",\n           \"created_at\": \"2026-03-14T10:05:44.000Z\"\n         }\n       ]\n     }\n   ]\n }\n ```\n\n\n Instead of reading through every thread yourself, you ask your agent  *\"what do I still need to fix in MR 2677?\"* and get back a prioritized summary with suggested changes. This all happens from a single command.\n\n\n ### Close the loop programmatically\n\n\n Once your AI has helped you address the feedback, it can resolve\n discussions:\n\n\n ```shell\n # List all discussions — structured, ready for the agent to process\n glab mr note list 456 --output json\n\n # Resolve a discussion once the feedback is addressed\n glab mr note resolve 456 3107030349\n\n # Reopen if something needs another look\n glab mr note reopen 456 3107030349\n ```\n\n\n\n ```json\n [\n   {\n     \"id\": 3107030349,\n     \"body\": \"This error handling will swallow panics — consider wrapping with recover()\",\n     \"author\": { \"username\": \"dmurphy\" },\n     \"resolved\": false,\n     \"resolvable\": true\n   },\n   {\n     \"id\": 3107030412,\n     \"body\": \"Token refresh logic needs a test for the expired token case\",\n     \"author\": { \"username\": \"sreeves\" },\n     \"resolved\": false,\n     \"resolvable\": true\n   }\n ]\n ```\n\n\n\n Note IDs are visible directly in the GitLab UI and API, no extra lookup needed. Your agent can work through the full list, verify each fix, and\n resolve as it goes.\n\n\n ## Talk to your AI about your code more effectively\n\n\n Even if you're not running an MCP server, there's a simpler shift that makes a huge difference: using `glab` to feed your AI better information.\n\n\n Think about the last time you asked an AI assistant to help triage issues or debug a failing pipeline. You probably copied some text from the GitLab UI and pasted it into the chat. Here's what your agent is actually\n working with when you do that:\n\n\n ```text\n open issues: 12 • milestone: 17.10 • label: bug, needs-triage ...\n ```\n\n\n Compare that to what it gets with `glab`:\n\n\n \n ```json\n [\n   {\n     \"iid\": 902,\n     \"title\": \"Pipeline fails on merge to main\",\n     \"labels\": [\"bug\", \"needs-triage\"],\n     \"milestone\": { \"title\": \"17.10\" },\n     \"assignees\": []\n   },\n   ...\n ]\n ```\n\n\n Structured, typed, complete; no ambiguity, no parsing guesswork. That's the difference between an agent that can act and one that has to ask\n follow-up questions.\n\n\n If you're using the MCP server, you get this automatically: `glab` adds `--output json` for any command that supports it. If you're working directly\n from the terminal, just add the flag yourself:\n\n\n ```shell\n # Pull open issues for triage\n glab issue list --label \"needs-triage\" --output json\n\n # Check pipeline status\n glab ci status --output json\n\n # Get full MR details\n glab mr view 456 --output json\n ```\n\n\n We've significantly expanded JSON output support in recent releases. It now covers CI status, milestones, labels, releases, schedules, cluster agents, work items, MR approvers, repo contributors, and more. If `glab` can\n retrieve it, your AI can consume it cleanly.\n\n\n ### A real workflow\n\n\n ```shell\n $ glab issue list --label \"needs-triage\" --milestone \"17.10\"\n --output json\n ```\n\n\n ```text\n Agent: I found 2 unassigned bugs in the 17.10 milestone that need triage:\n 1. #902 — Pipeline fails on merge to main (opened 5 days ago)\n 2. #903 — Auth token not refreshing on expiry (opened 4 days ago)\n Both are unassigned. Want me to draft triage notes and suggest assignees based on recent commit history?\n ```\n\n\n ## Your agent is never limited to built-in commands\n\n\n `glab`'s first-class commands cover the most common workflows, but your agent is never limited to them. Through `glab api`, it has authenticated access to the full GitLab REST and GraphQL API surface, using the same session, with no extra credentials or configuration required.\n\n\n This is a meaningful differentiator. Most CLI tools stop at what their commands expose. With `glab`, if GitLab's API supports it, your agent can do it. It's always working from a trusted, authenticated context.\n\n\n A practical example: fetching just the list of changed files in an MR before deciding which diffs to pull in full:\n\n\n ```shell\n # Get changed file paths — lightweight, no diff content yet\n glab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n | jq '.[].new_path'\n\n# Then fetch only the specific file your agent needs\nglab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n| jq '.[] | select(.new_path == \"path/to/file.go\")'\n ```\n\n\n ```text\n \"internal/auth/token.go\"\n \"internal/auth/token_test.go\"\n \"internal/oauth/refresh.go\"\n ```\n\n\n For anything the REST API doesn't cover (epics, certain work item queries, complex cross-project data),  `glab api graphql` gives you the full\n GraphQL interface:\n\n\n ```shell\n   glab api graphql -f query='\n {\n   project(fullPath: \"gitlab-org/gitlab\") {\n     mergeRequest(iid: \"12345\") {\n       title\n       reviewers { nodes { username } }\n     }\n   }\n }'\n ```\n\n ```json\n{\n   \"data\": {\n     \"project\": {\n       \"mergeRequest\": {\n         \"title\": \"feat: add OAuth2 support\",\n         \"reviewers\": {\n           \"nodes\": [\n             { \"username\": \"dmurphy\" },\n             { \"username\": \"sreeves\" }\n           ]\n         }\n       }\n     }\n   }\n }\n\n ```\n\n\n Your agent has a single, authenticated entry point to everything GitLab exposes without the token juggling, separate API clients, or configuration\n overhead.\n\n\n ## What's coming and your feedback\n\n\n Two improvements we're actively working on will make `glab` even more useful for agent workflows:\n\n\n **Agent-aware help text.** Today, `--help` output is written for humansvat a terminal. We're updating it to surface the non-interactive alternative\n for every interactive command, flag which commands support `--output json`, and generally make help a useful resource for agents discovering\n capabilities at runtime — not just humans.\n\n\n **Better machine-readable errors.** When something goes wrong today, agents get the same human-readable error messages as terminal users. We're\n changing that so errors in JSON mode return structured output, giving your agent the information it needs to handle failures gracefully, retry intelligently, or surface the right context back to you.\n\n\n Both of these are in active development. If you're already using `glab` with an AI tool, you're exactly the audience we want feedback from.\n\n\n * **What friction are you hitting?** Commands that don't behave well in agent contexts, error messages that aren't actionable, gaps in JSON output\n coverage. We want to know.\n\n * **What workflows have you unlocked?** Real usage patterns help us prioritize what to build next.\n\n\n Join the discussion in [our feedback issue](https://gitlab.com/gitlab-org/cli/-/issues/8177) — that's where we're shaping the roadmap for agent-friendliness, and where your input will have the most direct impact. If you've found a specific gap, [open an issue](https://gitlab.com/gitlab-org/cli/-/issues/new). If you've got a fix in mind, contributions are welcome. Visit [CONTRIBUTING.md](https://gitlab.com/gitlab-org/cli/-/blob/main/CONTRIBUTING.md) to get started.\n\n\n The GitLab CLI has always been about giving developers more control over their workflow. As AI becomes a bigger part of how we all work, that means making `glab` the best possible interface between your AI tools and your GitLab projects. We're just getting started and we'd love to build the next part with you.\n",[22,21,763],"tutorial",{"featured":15,"template":13,"slug":765},"give-your-ai-agent-direct-structured-gitlab-access-with-glab-cli",{"promotions":767},[768,781,792,803],{"id":769,"categories":770,"header":771,"text":772,"button":773,"image":778},"ai-modernization",[11],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":774,"config":775},"Get your AI maturity score",{"href":776,"dataGaName":777,"dataGaLocation":252},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":782,"categories":783,"header":784,"text":772,"button":785,"image":789},"devops-modernization",[21,581],"Are you just managing tools or shipping innovation?",{"text":786,"config":787},"Get your DevOps maturity score",{"href":788,"dataGaName":777,"dataGaLocation":252},"/assessments/devops-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":793,"categories":794,"header":795,"text":772,"button":796,"image":800},"security-modernization",[25],"Are you trading speed for security?",{"text":797,"config":798},"Get your security maturity score",{"href":799,"dataGaName":777,"dataGaLocation":252},"/assessments/security-modernization-assessment/",{"config":801},{"src":802},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":804,"paths":805,"header":808,"text":809,"button":810,"image":815},"github-azure-migration",[806,807],"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":811,"config":812},"See how GitLab compares to GitHub",{"href":813,"dataGaName":814,"dataGaLocation":252},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":816},{"src":791},{"header":818,"blurb":819,"button":820,"secondaryButton":825},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":821,"config":822},"Get your free trial",{"href":823,"dataGaName":51,"dataGaLocation":824},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":518,"config":826},{"href":55,"dataGaName":56,"dataGaLocation":824},1777934796830]