[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/gitlab-duo-agent-platform-public-beta":3,"navigation-en-us":39,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Bill Staples":702,"blog-related-posts-en-us-gitlab-duo-agent-platform-public-beta":716,"blog-promotions-en-us":753,"next-steps-en-us":805},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":22,"description":25,"extension":26,"externalUrl":27,"featured":13,"heroImage":23,"isFeatured":13,"meta":28,"navigation":13,"path":29,"publishedDate":22,"rawbody":30,"seo":31,"slug":15,"stem":34,"tagSlugs":35,"tags":37,"template":14,"updatedDate":27,"__hash__":38},"blogPosts/en-us/blog/gitlab-duo-agent-platform-public-beta.yml","GitLab Duo Agent Platform Public Beta: Next-gen AI orchestration and more",[7],"bill-staples",[9],"Bill Staples","**We're building the future of software development.**\n\nAt GitLab, we are [reimagining the future of software engineering](https://about.gitlab.com/blog/gitlab-duo-agent-platform-what-is-next-for-intelligent-devsecops/) as a human and AI collaboration. Where developers focus on solving technical, complex problems and driving innovation, while AI agents handle the routine, repetitive tasks that slow down progress. Where developers are free to explore new ideas in code at much lower cost, bug backlogs are a thing of the past, and users of the software you build enjoy a more usable, reliable, and secure experience. This isn't a distant dream. We're building this reality today, and it is called the GitLab Duo Agent Platform.\n\n## What is GitLab Duo Agent Platform?\n\nGitLab Duo Agent Platform is our next-generation DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents. It will transform your development workflow from isolated linear processes into dynamic collaboration where specialized AI agents work alongside you and your team on every stage of the software development lifecycle; it will be like having an unlimited team of colleagues at your disposal.\n\nImagine delegating a complex refactoring task to a Software Developer Agent while simultaneously having a Security Analyst Agent scan for vulnerabilities and a Deep Research Agent analyze progress across your repository history. This all happens in parallel, orchestrated seamlessly within GitLab.\n\nToday, we are announcing the launch of the [first public beta of the GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) for GitLab.com and self-managed GitLab Premium and Ultimate customers. This is just the first in a series of updates that will improve how software gets planned, built, verified, and deployed as we amplify human ingenuity through intelligent automation.\n\nThis first beta focuses on unlocking the IDE experience through the GitLab VS Code extension and JetBrains IDEs plug-in; next month, we plan on bringing the Duo Agent Platform experience to the GitLab application and expand our IDE support. Let me share a bit more about our vision for the roadmap between now and general availability, planned for later this year. You can find details about the first beta down below.\n\nWatch this video or read on for what's available now and what's to come. Then, if you're ready to get started with Duo Agent Platform, [find out how with the public beta](#get-started-now).\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101993507?title=0&byline=0&portrait=0&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 Agent Platform Beta Launch_071625_MP_v2\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n## GitLab's unique position as an orchestration platform\n\nGitLab sits at the heart of the development lifecycle as the system of record for engineering teams, orchestrating the entire journey from concept to production for over 50 million registered users, including half of the Fortune 500 across geographies. This includes over 10,000 paying customers across all segments and verticals, including public institutions.\n\nThis gives GitLab something no competitor can match: a comprehensive understanding of everything it takes to deliver software. We bring together your project plans, code, test runs, security scans, compliance checks, and CI/CD configurations to not only power your team but also orchestrate collaboration with AI agents you control.\n\nAs an intelligent, unified DevSecOps platform, GitLab stores all of the context about your software engineering practice in one place. We will expose this unified data to AI agents via our knowledge graph. Every agent we build has automatic access to this SDLC-connected data set, providing rich context so agents can make informed recommendations and take actions that adhere to your organizational standards.\n\n**Here's an example of this advantage in action.** Have you ever tried to figure out exactly how a project is going across dozens, if not hundreds, of stories and issues being worked on across all the developers involved? Our Deep Research Agent leverages the GitLab Knowledge Graph and semantic search capabilities to traverse your epic and all related issues, and explore the related codebase and surrounding context. It quickly correlates information across your repositories, merge requests, and deployment history. This delivers critical insights that standalone tools can't match and that would take human developers hours to uncover. \n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101998114?title=0&byline=0&portrait=0&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=\"Deep Research Demo_071625_MP_v1\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n## Our strategic evolution from AI features to agent orchestration\n\nGitLab Duo started as an add-on, bringing generative AI to developers through Duo Pro and Enterprise. With GitLab 18.0, it's now built into the platform. We've unlocked [Duo Agentic Chat](https://about.gitlab.com/blog/gitlab-duo-chat-gets-agentic-ai-makeover/) and Code Suggestions for all Premium and Ultimate users, and now we're providing immediate access to the Duo Agent Platform.\n\nWe've ramped up engineering investment and are accelerating delivery, with powerful new AI features landing every month. But we're not just building another coding assistant. GitLab Duo is becoming an agent orchestration platform, where you can create, customize, and deploy AI agents that work alongside you and interoperate easily with other systems, dramatically increasing productivity. \n\n> **“GitLab Duo Agent Platform enhances our development workflow with AI that truly understands our codebase and our organization. Having GitLab Duo AI agents embedded in our system of record for code, tests, CI/CD, and the entire software development lifecycle boosts productivity, velocity, and efficiency. The agents have become true collaborators to our teams, and their ability to understand intent, break down problems, and take action frees our developers to tackle the exciting, innovative work they love.”** - Bal Kang, Engineering Platform Lead at NatWest\n\n### Agents that work out of the box\n\nWe are introducing agents that mirror familiar team roles. These agents can search, read, create, and modify existing artifacts across GitLab. Think of these as agents you can interact with individually, that also act as building blocks that you can customize to create your own agents. Like your team members, agents have defined specializations, such as software development, testing, or technical writing. As specialists, they're tapping into the right context and tools to consistently accomplish the same types of tasks, wherever they're deployed.\n\nHere are some of the agents we're building today:\n\n- **Chat Agent (now in beta):** Takes natural language requests to provide information and context to the user. Can perform general development tasks, such as reading issues or code diffs. As an example, you can ask Chat to debug a failed job by providing the job URL.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1102616311?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=\"agentic-chat-in-web-ui-demo_Update V2\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\u003Cp>\u003C/p>\n\n\n- **Software Developer Agent (now in beta):** Works on assigned items by creating code changes in virtual development environments and opening merge requests for review.\n\n- **Product Planning Agent:** Prioritizes product backlogs, assigns work items to human and agentic team members, and provides project updates over specified timelines.\n\n- **Software Test Engineer Agent:** Tests new code contributions for bugs and validates if reported issues have been resolved. \n\n- **Code Reviewer Agent:** Performs code reviews following team standards, identifies quality and security issues, and can merge code when ready.\n\n- **Platform Engineer Agent:** Monitors GitLab deployments, including GitLab Runners, tracks CI/CD pipeline health, and reports performance issues to human platform engineering teams.\n\n- **Security Analyst Agent:** Finds vulnerabilities within codebases and deployed applications, and implements code and configuration changes to help resolve security weaknesses.\n\n- **Deployment Engineer Agent:** Deploys updates to production, monitors for unusual behavior, and rolls back changes that impact application performance or security.\n\n- **Deep Research Agent:** Conducts comprehensive, multi-source analysis across your entire development ecosystem.\n\nWhat makes these agents powerful is their native access to GitLab's comprehensive toolkit. Today, we have over 25 tools, from issues and epics to merge requests and documentation, with more to come. Unlike external AI tools that operate with limited context, our agents work as true team members with full platform privileges under your supervision.\n\nIn the coming months, you'll also be able to modify these agents to meet the needs of your organization. For example, you'll be able to specify that a Software Test Engineer Agent follows best practices for a particular framework or methodology, deepening its specialization and turning it into an even more valuable team member.\n\n## Flows orchestrate complex agent tasks\n\nOn top of individual agents, we are introducing agent Flows. Think of these as more complex workflows that can include multiple agents with pre-built instructions, steps, and actions for a given task that can run autonomously. \n\nWhile you can create Flows for basic tasks common to individuals, they truly excel when applied to complex, specialized tasks that would normally take hours of coordination and effort to complete. Flows will help you finish complex tasks faster and, in many cases, asynchronously without human intervention.\n\nFlows have specific triggers for execution. Each Flow contains a series of steps, and each step has detailed instructions that tell a specialized agent what to do. This granular approach allows  you to give precise instructions to agents in the Flow. By defining instructions in greater detail and establishing structured decision points, Flows can help solve for the inherent variability in AI responses while eliminating the need to repeatedly specify the same requirements, unlocking more consistent and predictable outcomes without user configuration.\n\nHere are some examples of out-of-the-box Flows that we are building:\n\n- **Software Development Flow (now in beta):** Orchestrates multiple agents to plan, implement, and test code changes end-to-end, helping transform how teams deliver features from concept to production.\n\n- **Issue-to-MR Flow:** Automatically converts issues into actionable merge requests by coordinating agents to analyze requirements, prepare comprehensive implementation plans, and generate code.\n\n- **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.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101941425?title=0&byline=0&portrait=0&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=\"jenkins-to-gitlab-cicd-for-blog\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n- **Search and Replace Flow:** Discovers and transforms code patterns across codebases by systematically analyzing project structures, identifying optimization opportunities, and executing precise replacements.\n\n- **Incident Response & Root Cause Analysis Flow:** Orchestrates incident response by correlating system data, coordinating specialized agents for root cause analysis, and executing approved remediation steps while keeping human stakeholders informed throughout the resolution process.\n\nThis is where GitLab Duo Agent Platform is taking a truly unique approach versus other AI solutions. We won't just give you pre-built agents. We'll also give you the power to create, customize, and share agent Flows that perfectly match your individual and organization's unique needs. And with Flows, you will then be able to give agents a specific execution plan for common and complex tasks.\n\nWe believe this approach is more powerful than building purpose-built agents like our competitors do, because every organization has different workflows, coding standards, security requirements, and business logic. Generic AI tools can't understand your specific context, but GitLab Duo Agent Platform will be able to be tailored to work exactly how your team works.\n\n## Why build agents and agent Flows in the GitLab Duo Agent Platform?\n\n**Build fast.** You can build agents and complex agent Flows in the Duo Agent Platform quickly and easily using a fast, declarative extensibility model and UI assistance.\n\n**Built-in compute.** With Duo Agent Platform, you no longer have to worry about the hassle of standing up your own infrastructure for agents: compute, network, and storage are all built-in.\n\n**SDLC events.** Your agents can be invoked automatically on common events: broken pipeline, failed deployment, issue created, etc.\n\n**Instant access.** You can interact with your agents everywhere in GitLab or our IDE plug-in: assign them issues, @mention them in comments, and chat with them everywhere Duo Chat is available.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1102029239?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=\"assigning an agent an issue\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script> \u003Cp>\u003C/p>\n\n\n**Built-in and custom models supported.** Your agents will have automatic access to all of the models we support, and users will be able to choose specific models for specific tasks. If you want to connect Duo Agent Platform to your own self-hosted model, you will be able to do that too!\n\n**Model Context Protocol (MCP) endpoints.** Every agent and Flow can be accessed or triggered via native MCP endpoints, allowing you to connect to and collaborate with your agents and Flows from anywhere, including popular tools like Claude Code, Cursor, Copilot, and Windsurf.\n\n**Observability and security.** Finally, we provide built-in observability and usage dashboards, so you can see exactly who, where, what, and when agents took actions on your behalf.\n\n## A community-driven future\n\nCommunity contributions have long fueled GitLab's innovation and software development. We're excited to partner with our community with the introduction of the AI Catalog. The AI Catalog will allow you to create and share agents and Flows within your organization and across the GitLab Ecosystem in our upcoming beta.\n\nWe believe that the most valuable AI applications are likely to emerge from you, our community, thanks to your daily application of GitLab Duo Agent Platform to solve numerous real-world use cases. By enabling seamless sharing of agents and Flows, we're creating a network effect where each contribution enhances the platform's collective intelligence and value. Over time, we believe that the most valuable use cases from Agent Platform will come from our thriving GitLab community. \n\n![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752685501/awdwx08udwrxgvcpmssb.png \"AI Catalog\")\n\n## Available today in the GitLab Duo Agent Platform in public beta\n\nThe GitLab Duo Agent Platform public beta is available now to Premium and Ultimate customers with these capabilities:\n\n**Software Development Flow:** Our first Flow orchestrates agents in gathering comprehensive context, clarifying ambiguities with human developers, and executing strategic plans to make precise changes to your codebase and repository. It leverages your entire project, including its structure, codebase, and history, along with additional context like GitLab issues or merge requests to amplify developer productivity.\n\n**New Agent tools available:** Agents now have access to multiple tools to do their work, including:\n\n  - File System (Read, Create, Edit, Find Files, List, Grep)\n  - Execute Command Line*\n  - Issues (List, Get, Get Comments, Edit*, Create*, Add/Update Comments*)\n  - Epics (Get, Get Comments)\n  - MR (Get, Get Comments, Get Diff, Create, Update)\n  - Pipeline (Job Logs, Pipeline Errors)\n  - Project (Get, Get File)\n  - Commits (Get, List, Get Comments, Get Diff)\n  - Search (Issue Search)\n  - Secure (List Vulnerabilities)\n  - Documentation Search\n\n  \n*=Requires user approval\n\n**GitLab Duo Agentic Chat in the IDE:** Duo Agentic Chat transforms the chat experience from a passive Q&A tool into an active development partner directly in your IDE.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1103237126?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=\"agentic-ai-launch-video_NEW\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\u003Cp>\u003C/p>\n\n- **Iterative feedback and chat history:** Duo Agentic Chat now supports chat history and iterative feedback, transforming the agent into a stateful, conversational partner. This fosters trust, enabling developers to delegate more complex tasks and offer corrective guidance.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743173?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"agentic-chat-history\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n- **Streamlined delegation with slash commands:** Expanded, more powerful slash commands, such as /explain, /tests, and /include, create a “delegation language” for quick and precise intent. The /include command allows the explicit injection of context from specific files, open issues, merge requests, or dependencies directly into the agent's working memory, making the agent more powerful and teaching users how to provide optimal context for high-quality responses.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743187?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"include-agentic-chat-jc-voiceover\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n- **Personalization through custom rules:** New Custom Rules enables developers to tailor agent behavior to individual and team preferences using natural language, for example, development style guides. This foundational mechanism shapes the agent's persona into a personalized assistant, evolving toward specialized agents based on user-defined preferences and organizational policies.\n    \n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743179?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"custom-rules-with-jc-voiceover\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\u003Cp>\u003C/p>\n\n- **Support for GitLab Duo Agentic Chat in JetBrains IDE:** To help meet developers where they work, we have expanded Duo Agentic Chat support to the JetBrains family of IDEs, including IntelliJ, PyCharm, GoLand, and Webstorm. This adds to our existing support for VS Code. Existing users get agentic capabilities automatically, while new users can install the plugin from the JetBrains Marketplace.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743193?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"jetbrains-support-jc-voiceover\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n    \n- **MCP client support:** Duo Agentic Chat can now act as an MCP client, connecting to remote and locally running MCP servers. This capability unlocks the agent's ability to connect to systems beyond GitLab like Jira, ServiceNow, and ZenDesk to gather context or take actions. Any service that exposes itself via MCP can now become part of the agent's skill set. The official GitLab MCP Server is coming soon!\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743202?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"McpDemo\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\u003Cp>\u003C/p>\n    \n- **GitLab Duo Agentic Chat in GitLab Web UI.** Duo Agentic Chat is also now available directly within the GitLab Web UI. This pivotal step evolves the agent from a coding assistant to a true DevSecOps agent, as it gains access to rich non-code context, such as issues and merge request discussions, allowing it to understand the \"why\" behind the work. Beyond understanding context, the agent can make changes directly from the WebUI, such as automatically updating issue statuses or editing merge request descriptions.\n\n## Coming soon to GitLab Duo Agent Platform\n\nOver the coming weeks, we'll release new capabilities to Duo Agent Platform, including more out-of-the-box agents and Flows. These will bring the platform into the GitLab experience you love today and enable even greater customization and extensibility, amplifying productivity for our customers:\n\n![GitLab Duo Agent Platform public beta roadmap](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752685275/hjbe9iiu2ydp9slibsc2.png \"GitLab Duo Agent Platform public beta roadmap\")\n\n\n- **Integrated GitLab experience:** Building on the IDE extensions available in 18.2, we're expanding agents and Flows within the GitLab platform. This deeper integration will expand the ways you can collaborate synchronously and asynchronously with agents. You will be able to assign issues directly to agents, @mention them within GitLab Duo Chat, and seamlessly invoke them from anywhere in the application while maintaining MCP connectivity from your developer tool of choice. This native integration transforms agents into true development team members, accessible across GitLab.\n\n- **Agent observability:** As agents become more autonomous, we're building comprehensive visibility into their activity as they progress through Flows, enabling you to monitor their decision-making processes, track execution steps, and understand how they're interpreting and acting on your development challenges. This transparency into agent behavior builds trust and confidence while allowing you to optimize workflows and identify bottlenecks, and helps ensure agents are performing exactly as intended.\n\n- **AI Catalog:** Recognizing that great solutions come from community innovation, we will soon introduce the public beta of our AI Catalog — a marketplace which will allow you to extend Duo Agent Platform with specialized Agents and Flows sourced from GitLab, and over time, the broader community.  You'll be able to quickly deploy these solutions in GitLab, leveraging context across your projects and codebase.\n\n- **Knowledge Graph:** Leveraging GitLab's unique advantage as the system of record for source code and its surrounding context, we're building a comprehensive Knowledge Graph that not only maps files and dependencies across the codebase but also makes that map navigable for users while accelerating AI query times and helping increase accuracy. This foundation enables GitLab Duo agents to quickly understand relationships across your entire development environment, from code dependencies to deployment patterns, unlocking faster and more precise responses to complex questions.\n\n![GitLab Duo Agent Platform Knowledge Graph](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752685367/n0tvfgorchuhrronic3j.png \"GitLab Duo Agent Platform Knowledge Graph\")\n\n- **Create and edit agents and Flows:** Understanding that every organization has unique workflows and requirements, we're developing powerful agent and Flow creation and editing capabilities that will be introduced as the AI Catalog matures. You'll be able to create and modify agents and Flows to operate precisely the way your organization works, delivering deep customization across the Duo Agent Platform that enables higher quality results and increased productivity. \n\n![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752684938/fruwqcqvvrx8gmkz5u0v.png \"AI Catalog\")\n\n- **Official GitLab MCP Server:** Recognizing that developers work across multiple tools and environments, we're building an official GitLab MCP server that will enable you to access all of your agents and Flows via MCP. You'll be able to connect to and collaborate with your agents and Flows from anywhere MCP is supported, including popular tools like Claude Code, Cursor, Copilot, and Windsurf, unlocking seamless AI collaboration regardless of your preferred development environment.\n\n- **GitLab Duo Agent Platform CLI:** Our upcoming CLI will allow you to invoke agents and trigger Flows on the command line, leveraging GitLab's rich context across the entire software development lifecycle—from code repositories and merge requests to CI/CD pipelines and issue tracking. \n\n## Get started now\n\n- **GitLab Premium and Ultimate customers** in GitLab.com and self-managed environments using GitLab 18.2 can use Duo Agent Platform immediately (beta and experimental features for GitLab Duo [must be enabled](https://docs.gitlab.com/user/gitlab_duo/turn_on_off/#turn-on-beta-and-experimental-features)). GitLab Dedicated customers will be able to use the Duo Agent Platform with the release of GitLab 18.2 for Dedicated next month.\n\n- Users should download the [VS Code extension](https://marketplace.visualstudio.com/items?itemName=GitLab.gitlab-workflow) or the [JetBrains IDEs plugin](https://plugins.jetbrains.com/plugin/22857-gitlab) and follow our [guide to using GitLab Duo Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/#use-agentic-chat), including Duo Chat [slash commands](https://docs.gitlab.com/user/gitlab_duo_chat/examples/#gitlab-duo-chat-slash-commands). \n\n**New to GitLab?** See GitLab Duo Agent Platform in action at our Technical Demo, offered in two timezone-friendly sessions: [Americas and EMEA](https://page.gitlab.com/webcasts-jul16-gitlab-duo-agentic-ai-emea-amer.html) and [Asia-Pacific](https://page.gitlab.com/webcasts-jul24-gitlab-duo-agentic-ai-apac.html). To get hands-on with GitLab Duo Agent Platform yourself, sign up for a [free trial](https://gitlab.com/-/trials/new?glm_content=default-saas-trial&glm_source=about.gitlab.com%2Fsales%2F) today.\n\n\n\u003Csmall>*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.*\n\n*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/small>","ai-ml",{"featured":13,"template":14,"slug":15},true,"BlogPost","gitlab-duo-agent-platform-public-beta",{"tags":17,"category":11,"date":22,"heroImage":23,"authors":24,"description":25,"title":5,"body":10},[18,19,20,21],"AI/ML","product","features","news","2025-07-17","https://res.cloudinary.com/about-gitlab-com/image/upload/v1752678395/impw8no5tbskr6k2afgu.jpg",[9],"Introducing the DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents.","yml",null,{},"/en-us/blog/gitlab-duo-agent-platform-public-beta","seo:\n  noIndex: false\n  title: GitLab Duo Agent Platform goes public beta\n  description: Introducing the DevSecOps orchestration platform designed to unlock\n    asynchronous collaboration between developers and AI agents.\n  ogImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1752678395/impw8no5tbskr6k2afgu.jpg\ncontent:\n  tags:\n    - AI/ML\n    - product\n    - features\n    - news\n  category: ai-ml\n  date: 2025-07-17\n  heroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1752678395/impw8no5tbskr6k2afgu.jpg\n  authors:\n    - Bill Staples\n  description: Introducing the DevSecOps orchestration platform designed to unlock\n    asynchronous collaboration between developers and AI agents.\n  title: 'GitLab Duo Agent Platform Public Beta: Next-gen AI orchestration and more'\n  body: >-\n    **We're building the future of software development.**\n\n\n    At GitLab, we are [reimagining the future of software engineering](https://about.gitlab.com/blog/gitlab-duo-agent-platform-what-is-next-for-intelligent-devsecops/) as a human and AI collaboration. Where developers focus on solving technical, complex problems and driving innovation, while AI agents handle the routine, repetitive tasks that slow down progress. Where developers are free to explore new ideas in code at much lower cost, bug backlogs are a thing of the past, and users of the software you build enjoy a more usable, reliable, and secure experience. This isn't a distant dream. We're building this reality today, and it is called the GitLab Duo Agent Platform.\n\n\n    ## What is GitLab Duo Agent Platform?\n\n\n    GitLab Duo Agent Platform is our next-generation DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents. It will transform your development workflow from isolated linear processes into dynamic collaboration where specialized AI agents work alongside you and your team on every stage of the software development lifecycle; it will be like having an unlimited team of colleagues at your disposal.\n\n\n    Imagine delegating a complex refactoring task to a Software Developer Agent while simultaneously having a Security Analyst Agent scan for vulnerabilities and a Deep Research Agent analyze progress across your repository history. This all happens in parallel, orchestrated seamlessly within GitLab.\n\n\n    Today, we are announcing the launch of the [first public beta of the GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) for GitLab.com and self-managed GitLab Premium and Ultimate customers. This is just the first in a series of updates that will improve how software gets planned, built, verified, and deployed as we amplify human ingenuity through intelligent automation.\n\n\n    This first beta focuses on unlocking the IDE experience through the GitLab VS Code extension and JetBrains IDEs plug-in; next month, we plan on bringing the Duo Agent Platform experience to the GitLab application and expand our IDE support. Let me share a bit more about our vision for the roadmap between now and general availability, planned for later this year. You can find details about the first beta down below.\n\n\n    Watch this video or read on for what's available now and what's to come. Then, if you're ready to get started with Duo Agent Platform, [find out how with the public beta](#get-started-now).\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101993507?title=0&byline=0&portrait=0&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 Agent Platform Beta Launch_071625_MP_v2\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n\n    ## GitLab's unique position as an orchestration platform\n\n\n    GitLab sits at the heart of the development lifecycle as the system of record for engineering teams, orchestrating the entire journey from concept to production for over 50 million registered users, including half of the Fortune 500 across geographies. This includes over 10,000 paying customers across all segments and verticals, including public institutions.\n\n\n    This gives GitLab something no competitor can match: a comprehensive understanding of everything it takes to deliver software. We bring together your project plans, code, test runs, security scans, compliance checks, and CI/CD configurations to not only power your team but also orchestrate collaboration with AI agents you control.\n\n\n    As an intelligent, unified DevSecOps platform, GitLab stores all of the context about your software engineering practice in one place. We will expose this unified data to AI agents via our knowledge graph. Every agent we build has automatic access to this SDLC-connected data set, providing rich context so agents can make informed recommendations and take actions that adhere to your organizational standards.\n\n\n    **Here's an example of this advantage in action.** Have you ever tried to figure out exactly how a project is going across dozens, if not hundreds, of stories and issues being worked on across all the developers involved? Our Deep Research Agent leverages the GitLab Knowledge Graph and semantic search capabilities to traverse your epic and all related issues, and explore the related codebase and surrounding context. It quickly correlates information across your repositories, merge requests, and deployment history. This delivers critical insights that standalone tools can't match and that would take human developers hours to uncover. \n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101998114?title=0&byline=0&portrait=0&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=\"Deep Research Demo_071625_MP_v1\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n\n    ## Our strategic evolution from AI features to agent orchestration\n\n\n    GitLab Duo started as an add-on, bringing generative AI to developers through Duo Pro and Enterprise. With GitLab 18.0, it's now built into the platform. We've unlocked [Duo Agentic Chat](https://about.gitlab.com/blog/gitlab-duo-chat-gets-agentic-ai-makeover/) and Code Suggestions for all Premium and Ultimate users, and now we're providing immediate access to the Duo Agent Platform.\n\n\n    We've ramped up engineering investment and are accelerating delivery, with powerful new AI features landing every month. But we're not just building another coding assistant. GitLab Duo is becoming an agent orchestration platform, where you can create, customize, and deploy AI agents that work alongside you and interoperate easily with other systems, dramatically increasing productivity. \n\n\n    > **“GitLab Duo Agent Platform enhances our development workflow with AI that truly understands our codebase and our organization. Having GitLab Duo AI agents embedded in our system of record for code, tests, CI/CD, and the entire software development lifecycle boosts productivity, velocity, and efficiency. The agents have become true collaborators to our teams, and their ability to understand intent, break down problems, and take action frees our developers to tackle the exciting, innovative work they love.”** - Bal Kang, Engineering Platform Lead at NatWest\n\n\n    ### Agents that work out of the box\n\n\n    We are introducing agents that mirror familiar team roles. These agents can search, read, create, and modify existing artifacts across GitLab. Think of these as agents you can interact with individually, that also act as building blocks that you can customize to create your own agents. Like your team members, agents have defined specializations, such as software development, testing, or technical writing. As specialists, they're tapping into the right context and tools to consistently accomplish the same types of tasks, wherever they're deployed.\n\n\n    Here are some of the agents we're building today:\n\n\n    - **Chat Agent (now in beta):** Takes natural language requests to provide information and context to the user. Can perform general development tasks, such as reading issues or code diffs. As an example, you can ask Chat to debug a failed job by providing the job URL.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1102616311?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=\"agentic-chat-in-web-ui-demo_Update V2\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\u003Cp>\u003C/p>\n\n\n\n    - **Software Developer Agent (now in beta):** Works on assigned items by creating code changes in virtual development environments and opening merge requests for review.\n\n\n    - **Product Planning Agent:** Prioritizes product backlogs, assigns work items to human and agentic team members, and provides project updates over specified timelines.\n\n\n    - **Software Test Engineer Agent:** Tests new code contributions for bugs and validates if reported issues have been resolved. \n\n\n    - **Code Reviewer Agent:** Performs code reviews following team standards, identifies quality and security issues, and can merge code when ready.\n\n\n    - **Platform Engineer Agent:** Monitors GitLab deployments, including GitLab Runners, tracks CI/CD pipeline health, and reports performance issues to human platform engineering teams.\n\n\n    - **Security Analyst Agent:** Finds vulnerabilities within codebases and deployed applications, and implements code and configuration changes to help resolve security weaknesses.\n\n\n    - **Deployment Engineer Agent:** Deploys updates to production, monitors for unusual behavior, and rolls back changes that impact application performance or security.\n\n\n    - **Deep Research Agent:** Conducts comprehensive, multi-source analysis across your entire development ecosystem.\n\n\n    What makes these agents powerful is their native access to GitLab's comprehensive toolkit. Today, we have over 25 tools, from issues and epics to merge requests and documentation, with more to come. Unlike external AI tools that operate with limited context, our agents work as true team members with full platform privileges under your supervision.\n\n\n    In the coming months, you'll also be able to modify these agents to meet the needs of your organization. For example, you'll be able to specify that a Software Test Engineer Agent follows best practices for a particular framework or methodology, deepening its specialization and turning it into an even more valuable team member.\n\n\n    ## Flows orchestrate complex agent tasks\n\n\n    On top of individual agents, we are introducing agent Flows. Think of these as more complex workflows that can include multiple agents with pre-built instructions, steps, and actions for a given task that can run autonomously. \n\n\n    While you can create Flows for basic tasks common to individuals, they truly excel when applied to complex, specialized tasks that would normally take hours of coordination and effort to complete. Flows will help you finish complex tasks faster and, in many cases, asynchronously without human intervention.\n\n\n    Flows have specific triggers for execution. Each Flow contains a series of steps, and each step has detailed instructions that tell a specialized agent what to do. This granular approach allows  you to give precise instructions to agents in the Flow. By defining instructions in greater detail and establishing structured decision points, Flows can help solve for the inherent variability in AI responses while eliminating the need to repeatedly specify the same requirements, unlocking more consistent and predictable outcomes without user configuration.\n\n\n    Here are some examples of out-of-the-box Flows that we are building:\n\n\n    - **Software Development Flow (now in beta):** Orchestrates multiple agents to plan, implement, and test code changes end-to-end, helping transform how teams deliver features from concept to production.\n\n\n    - **Issue-to-MR Flow:** Automatically converts issues into actionable merge requests by coordinating agents to analyze requirements, prepare comprehensive implementation plans, and generate code.\n\n\n    - **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.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101941425?title=0&byline=0&portrait=0&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=\"jenkins-to-gitlab-cicd-for-blog\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n    \u003Cp>\u003C/p>\n\n\n    - **Search and Replace Flow:** Discovers and transforms code patterns across codebases by systematically analyzing project structures, identifying optimization opportunities, and executing precise replacements.\n\n\n    - **Incident Response & Root Cause Analysis Flow:** Orchestrates incident response by correlating system data, coordinating specialized agents for root cause analysis, and executing approved remediation steps while keeping human stakeholders informed throughout the resolution process.\n\n\n    This is where GitLab Duo Agent Platform is taking a truly unique approach versus other AI solutions. We won't just give you pre-built agents. We'll also give you the power to create, customize, and share agent Flows that perfectly match your individual and organization's unique needs. And with Flows, you will then be able to give agents a specific execution plan for common and complex tasks.\n\n\n    We believe this approach is more powerful than building purpose-built agents like our competitors do, because every organization has different workflows, coding standards, security requirements, and business logic. Generic AI tools can't understand your specific context, but GitLab Duo Agent Platform will be able to be tailored to work exactly how your team works.\n\n\n    ## Why build agents and agent Flows in the GitLab Duo Agent Platform?\n\n\n    **Build fast.** You can build agents and complex agent Flows in the Duo Agent Platform quickly and easily using a fast, declarative extensibility model and UI assistance.\n\n\n    **Built-in compute.** With Duo Agent Platform, you no longer have to worry about the hassle of standing up your own infrastructure for agents: compute, network, and storage are all built-in.\n\n\n    **SDLC events.** Your agents can be invoked automatically on common events: broken pipeline, failed deployment, issue created, etc.\n\n\n    **Instant access.** You can interact with your agents everywhere in GitLab or our IDE plug-in: assign them issues, @mention them in comments, and chat with them everywhere Duo Chat is available.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1102029239?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=\"assigning an agent an issue\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script> \u003Cp>\u003C/p>\n\n\n\n    **Built-in and custom models supported.** Your agents will have automatic access to all of the models we support, and users will be able to choose specific models for specific tasks. If you want to connect Duo Agent Platform to your own self-hosted model, you will be able to do that too!\n\n\n    **Model Context Protocol (MCP) endpoints.** Every agent and Flow can be accessed or triggered via native MCP endpoints, allowing you to connect to and collaborate with your agents and Flows from anywhere, including popular tools like Claude Code, Cursor, Copilot, and Windsurf.\n\n\n    **Observability and security.** Finally, we provide built-in observability and usage dashboards, so you can see exactly who, where, what, and when agents took actions on your behalf.\n\n\n    ## A community-driven future\n\n\n    Community contributions have long fueled GitLab's innovation and software development. We're excited to partner with our community with the introduction of the AI Catalog. The AI Catalog will allow you to create and share agents and Flows within your organization and across the GitLab Ecosystem in our upcoming beta.\n\n\n    We believe that the most valuable AI applications are likely to emerge from you, our community, thanks to your daily application of GitLab Duo Agent Platform to solve numerous real-world use cases. By enabling seamless sharing of agents and Flows, we're creating a network effect where each contribution enhances the platform's collective intelligence and value. Over time, we believe that the most valuable use cases from Agent Platform will come from our thriving GitLab community. \n\n\n    ![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752685501/awdwx08udwrxgvcpmssb.png \"AI Catalog\")\n\n\n    ## Available today in the GitLab Duo Agent Platform in public beta\n\n\n    The GitLab Duo Agent Platform public beta is available now to Premium and Ultimate customers with these capabilities:\n\n\n    **Software Development Flow:** Our first Flow orchestrates agents in gathering comprehensive context, clarifying ambiguities with human developers, and executing strategic plans to make precise changes to your codebase and repository. It leverages your entire project, including its structure, codebase, and history, along with additional context like GitLab issues or merge requests to amplify developer productivity.\n\n\n    **New Agent tools available:** Agents now have access to multiple tools to do their work, including:\n\n      - File System (Read, Create, Edit, Find Files, List, Grep)\n      - Execute Command Line*\n      - Issues (List, Get, Get Comments, Edit*, Create*, Add/Update Comments*)\n      - Epics (Get, Get Comments)\n      - MR (Get, Get Comments, Get Diff, Create, Update)\n      - Pipeline (Job Logs, Pipeline Errors)\n      - Project (Get, Get File)\n      - Commits (Get, List, Get Comments, Get Diff)\n      - Search (Issue Search)\n      - Secure (List Vulnerabilities)\n      - Documentation Search\n\n      \n    *=Requires user approval\n\n\n    **GitLab Duo Agentic Chat in the IDE:** Duo Agentic Chat transforms the chat experience from a passive Q&A tool into an active development partner directly in your IDE.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1103237126?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=\"agentic-ai-launch-video_NEW\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\u003Cp>\u003C/p>\n\n\n    - **Iterative feedback and chat history:** Duo Agentic Chat now supports chat history and iterative feedback, transforming the agent into a stateful, conversational partner. This fosters trust, enabling developers to delegate more complex tasks and offer corrective guidance.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743173?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"agentic-chat-history\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n    \u003Cp>\u003C/p>\n\n\n    - **Streamlined delegation with slash commands:** Expanded, more powerful slash commands, such as /explain, /tests, and /include, create a “delegation language” for quick and precise intent. The /include command allows the explicit injection of context from specific files, open issues, merge requests, or dependencies directly into the agent's working memory, making the agent more powerful and teaching users how to provide optimal context for high-quality responses.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743187?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"include-agentic-chat-jc-voiceover\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n    \u003Cp>\u003C/p>\n\n\n    - **Personalization through custom rules:** New Custom Rules enables developers to tailor agent behavior to individual and team preferences using natural language, for example, development style guides. This foundational mechanism shapes the agent's persona into a personalized assistant, evolving toward specialized agents based on user-defined preferences and organizational policies.\n        \n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743179?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"custom-rules-with-jc-voiceover\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n    \u003Cp>\u003C/p>\n\n\n    - **Support for GitLab Duo Agentic Chat in JetBrains IDE:** To help meet developers where they work, we have expanded Duo Agentic Chat support to the JetBrains family of IDEs, including IntelliJ, PyCharm, GoLand, and Webstorm. This adds to our existing support for VS Code. Existing users get agentic capabilities automatically, while new users can install the plugin from the JetBrains Marketplace.\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743193?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"jetbrains-support-jc-voiceover\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n    \u003Cp>\u003C/p>\n        \n    - **MCP client support:** Duo Agentic Chat can now act as an MCP client, connecting to remote and locally running MCP servers. This capability unlocks the agent's ability to connect to systems beyond GitLab like Jira, ServiceNow, and ZenDesk to gather context or take actions. Any service that exposes itself via MCP can now become part of the agent's skill set. The official GitLab MCP Server is coming soon!\n\n\n    \u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1101743202?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"McpDemo\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n    \u003Cp>\u003C/p>\n        \n    - **GitLab Duo Agentic Chat in GitLab Web UI.** Duo Agentic Chat is also now available directly within the GitLab Web UI. This pivotal step evolves the agent from a coding assistant to a true DevSecOps agent, as it gains access to rich non-code context, such as issues and merge request discussions, allowing it to understand the \"why\" behind the work. Beyond understanding context, the agent can make changes directly from the WebUI, such as automatically updating issue statuses or editing merge request descriptions.\n\n\n    ## Coming soon to GitLab Duo Agent Platform\n\n\n    Over the coming weeks, we'll release new capabilities to Duo Agent Platform, including more out-of-the-box agents and Flows. These will bring the platform into the GitLab experience you love today and enable even greater customization and extensibility, amplifying productivity for our customers:\n\n\n    ![GitLab Duo Agent Platform public beta roadmap](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752685275/hjbe9iiu2ydp9slibsc2.png \"GitLab Duo Agent Platform public beta roadmap\")\n\n\n\n    - **Integrated GitLab experience:** Building on the IDE extensions available in 18.2, we're expanding agents and Flows within the GitLab platform. This deeper integration will expand the ways you can collaborate synchronously and asynchronously with agents. You will be able to assign issues directly to agents, @mention them within GitLab Duo Chat, and seamlessly invoke them from anywhere in the application while maintaining MCP connectivity from your developer tool of choice. This native integration transforms agents into true development team members, accessible across GitLab.\n\n\n    - **Agent observability:** As agents become more autonomous, we're building comprehensive visibility into their activity as they progress through Flows, enabling you to monitor their decision-making processes, track execution steps, and understand how they're interpreting and acting on your development challenges. This transparency into agent behavior builds trust and confidence while allowing you to optimize workflows and identify bottlenecks, and helps ensure agents are performing exactly as intended.\n\n\n    - **AI Catalog:** Recognizing that great solutions come from community innovation, we will soon introduce the public beta of our AI Catalog — a marketplace which will allow you to extend Duo Agent Platform with specialized Agents and Flows sourced from GitLab, and over time, the broader community.  You'll be able to quickly deploy these solutions in GitLab, leveraging context across your projects and codebase.\n\n\n    - **Knowledge Graph:** Leveraging GitLab's unique advantage as the system of record for source code and its surrounding context, we're building a comprehensive Knowledge Graph that not only maps files and dependencies across the codebase but also makes that map navigable for users while accelerating AI query times and helping increase accuracy. This foundation enables GitLab Duo agents to quickly understand relationships across your entire development environment, from code dependencies to deployment patterns, unlocking faster and more precise responses to complex questions.\n\n\n    ![GitLab Duo Agent Platform Knowledge Graph](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752685367/n0tvfgorchuhrronic3j.png \"GitLab Duo Agent Platform Knowledge Graph\")\n\n\n    - **Create and edit agents and Flows:** Understanding that every organization has unique workflows and requirements, we're developing powerful agent and Flow creation and editing capabilities that will be introduced as the AI Catalog matures. You'll be able to create and modify agents and Flows to operate precisely the way your organization works, delivering deep customization across the Duo Agent Platform that enables higher quality results and increased productivity. \n\n\n    ![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752684938/fruwqcqvvrx8gmkz5u0v.png \"AI Catalog\")\n\n\n    - **Official GitLab MCP Server:** Recognizing that developers work across multiple tools and environments, we're building an official GitLab MCP server that will enable you to access all of your agents and Flows via MCP. You'll be able to connect to and collaborate with your agents and Flows from anywhere MCP is supported, including popular tools like Claude Code, Cursor, Copilot, and Windsurf, unlocking seamless AI collaboration regardless of your preferred development environment.\n\n\n    - **GitLab Duo Agent Platform CLI:** Our upcoming CLI will allow you to invoke agents and trigger Flows on the command line, leveraging GitLab's rich context across the entire software development lifecycle—from code repositories and merge requests to CI/CD pipelines and issue tracking. \n\n\n    ## Get started now\n\n\n    - **GitLab Premium and Ultimate customers** in GitLab.com and self-managed environments using GitLab 18.2 can use Duo Agent Platform immediately (beta and experimental features for GitLab Duo [must be enabled](https://docs.gitlab.com/user/gitlab_duo/turn_on_off/#turn-on-beta-and-experimental-features)). 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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.",[722],"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/).",[18,19,281],{"featured":13,"template":14,"slug":728},"gitlab-and-anthropic-governed-ai-for-enterprise-development",{"content":730,"config":740},{"title":731,"description":732,"authors":733,"heroImage":735,"date":736,"body":737,"category":11,"tags":738},"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.",[734],"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",[18,19,739],"tutorial",{"featured":13,"template":14,"slug":741},"give-your-ai-agent-direct-structured-gitlab-access-with-glab-cli",{"content":743,"config":751},{"title":744,"description":745,"authors":746,"heroImage":735,"date":748,"body":749,"category":11,"tags":750},"GitHub Copilot's new policy for AI training is a governance wake-up call","Learn what GitHub's Copilot policy change means for regulated industries, and why GitLab's commitment to customer data privacy matters.",[747],"Allie Holland","2026-04-20","GitHub recently [announced](https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/) a significant change to how it handles data from Copilot users. Starting April 24, 2026, interaction data from Copilot Free, Pro, and Pro+ users, including inputs, outputs, code snippets, and associated context, will be used to train AI models by default, unless users actively opt out. Copilot Business and Enterprise customers are exempt under existing contract terms.\n\nFor organizations in regulated industries, including finance, healthcare, defense, and public sector, the policy shift raises questions that go beyond individual developer preferences. It forces a harder look at a question that engineering and security leaders should be asking every AI vendor in their stack: Do you train on our code? \n\nGitLab's answer is no. GitLab does not train AI models on customer code at any tier, and AI vendors are contractually prohibited from using customer inputs or outputs for their own purposes. The [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) makes that commitment auditable: a single location documenting which models power which features, how data is handled, subprocessor relationships, and data retention periods. The GitLab AI Transparency Center also lists the compliance status of each feature, including confirmation that GitLab's current AI features do not qualify as high-risk systems under the EU AI Act. It's a standard GitLab CEO Bill Staples has consistently [reiterated](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf?utm_source=share&utm_medium=member_desktop&rcm=ACoAABsu7EUBcb_a1-JHKS9RC0B5rf8Ye-5XM60) and one reflected in GitLab's mission and [Trust Center](https://trust.gitlab.com/).\n\n## What the policy change actually means\n\nGitHub's announcement also specifies that the data may be shared with GitHub affiliates, including Microsoft, for AI development purposes.\n\nA policy change of this nature forces organizations to re-examine their AI governance posture, audit their Copilot license tiers, and confirm that the right controls are configured across their teams.\n\n## Why AI governance matters in regulated environments\n\nSource code is often among an organization's most sensitive intellectual property. It may contain references to internal systems, reflect proprietary business logic, or touch data flows governed by strict retention and access policies. When that code passes through an AI assistant, questions about training data usage, model vendor relationships, and data residency become compliance concerns.\n\nThe exposure is particularly acute for financial services firms that have invested in proprietary algorithms, fraud detection logic, credit risk models, underwriting rules, trading strategies. When AI tooling processes that code and uses it to train models serving competitors, vendor data practices become an IP concern.\n\nFinancial institutions operating under [the Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7) and the](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) [Digital Operational Resilience Act (DORA)](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) are required to maintain documented, auditable oversight of third-party technology providers, including understanding how those providers handle data. Third-party AI tools used in development workflows increasingly fall within the scope of model risk oversight, and material changes to vendor data practices require updated documentation. \n\nIn the public sector, [the National Institute of Standards and Technology Special Publication 800-53 (NIST 800-53)](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and the [Federal Information Security Modernization Act (FISMA)](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) establish that sensitive or classified code must never leave a controlled boundary. For U.S. Department of Defense and intelligence community environments in particular, a vendor's default data posture is an operational concern. In healthcare, [the Health Insurance Portability and Accountability Act (HIPAA)](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties, and development environments that touch clinical systems increasingly fall within that scope.\n\nAcross all of these contexts, the common thread is the same: A vendor policy that changes data usage defaults, requires individual opt-out, and offers different protections depending on account tier introduces exactly the kind of uncontrolled variable that compliance teams cannot afford.\n\n## What regulated industries actually need from AI vendors\n\nRegulated organizations have largely moved past debating whether to adopt AI in development workflows. The focus now is on doing so in a way they can defend to regulators, boards, and customers. That shift has surfaced a consistent set of requirements regardless of sector.\n\n**Contractual certainty.** Regulated firms need to know, with specificity, what happens to their data. A clear, documented, unconditional commitment is what's required, not something that varies by plan or requires action before a deadline.\n\n**Auditability.** Model risk management frameworks require organizations to understand and validate the AI systems they deploy, including the training data behind those models and the third parties involved in their development. Vendors who cannot answer these questions create documentation risk for the organizations relying on them.\n\n**Separation from vendor incentives.** When an AI vendor trains models on customer usage data, code and workflows become inputs to a system that also serves competitors. For institutions with proprietary trading logic, underwriting models, or fraud detection systems, that's a genuine IP exposure.\n\n## GitLab's position on AI data governance\n\nGitLab does not use customer code to train AI models. This commitment applies at every tier, and AI vendors are contractually prohibited from using inputs or outputs associated with GitLab customers for their own purposes.\n\nThis is a deliberate architectural and policy choice, not a feature of a particular pricing tier. As GitLab's [post on enterprise independence](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) notes, data governance has become \"an increasingly critical factor in enterprise technology decisions, driven by a complex web of national and regional data protection laws and growing concern about control over sensitive intellectual property.\"\n\nGitLab is also cloud-neutral and model-neutral while supporting self-hosted deployments, not commercially tied to any single cloud provider or large language model (LLM). That i[ndependence matters](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) for regulated organizations evaluating vendor concentration risk. The [AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including material changes to how AI vendors treat customer data, a direct response to the governance requirements under frameworks like [DORA](https://handbook.gitlab.com/handbook/legal/dora/). \n\n## The governance gap AI teams need to close\n\nGitHub's policy update is a reminder that for organizations in regulated industries, understanding exactly how an AI tool handles data is a prerequisite for using it at all. That means asking vendors for clear, documented answers: Is our data used for model training? Who are your AI model subprocessors? What happens if a vendor changes its data practices? Can we deploy in a way that keeps all AI processing within our own infrastructure? What indemnification do you offer for AI-generated output?\n\nVendors who can answer those questions clearly, and document those answers in an auditable form, are vendors you can build on. **Those who cannot will create compliance debt every time they ship a policy update.** And when a vendor can change its data practices with 30 days notice, that's not a partnership built for regulated industries. That's a liability.\n\n> Learn more about GitLab's approach to AI governance at the [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).",[18,19],{"featured":32,"template":14,"slug":752},"github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",{"promotions":754},[755,768,779,791],{"id":756,"categories":757,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[11],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[19,570],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":783,"text":759,"button":784,"image":788},"security-modernization",[782],"security","Are you trading speed for security?",{"text":785,"config":786},"Get your security maturity score",{"href":787,"dataGaName":764,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":792,"paths":793,"header":796,"text":797,"button":798,"image":803},"github-azure-migration",[794,795],"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":799,"config":800},"See how GitLab compares to GitHub",{"href":801,"dataGaName":802,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":804},{"src":778},{"header":806,"blurb":807,"button":808,"secondaryButton":813},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":809,"config":810},"Get your free trial",{"href":811,"dataGaName":50,"dataGaLocation":812},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":814},{"href":54,"dataGaName":55,"dataGaLocation":812},1777493592427]