[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/gitlab-duo-agent-platform-public-beta":3,"navigation-en-us":37,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-Bill Staples":689,"blog-related-posts-en-us-gitlab-duo-agent-platform-public-beta":703,"assessment-promotions-en-us":743,"next-steps-en-us":780},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":11,"meta":28,"navigation":11,"path":29,"publishedDate":20,"seo":30,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/gitlab-duo-agent-platform-public-beta.yml","Gitlab Duo Agent Platform Public Beta",[7],"bill-staples",null,"ai-ml",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-duo-agent-platform-public-beta",{"tags":15,"category":9,"date":20,"heroImage":21,"authors":22,"description":24,"title":25,"body":26},[16,17,18,19],"AI/ML","product","features","news","2025-07-17","https://res.cloudinary.com/about-gitlab-com/image/upload/v1752678395/impw8no5tbskr6k2afgu.jpg",[23],"Bill Staples","Introducing the DevSecOps orchestration platform designed to unlock asynchronous collaboration between developers and AI agents.","GitLab Duo Agent Platform Public Beta: Next-gen AI orchestration and more","**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. 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Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[16,714],"DevOps platform",{"featured":31,"template":12,"slug":716},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":718,"config":728},{"title":719,"description":720,"heroImage":721,"authors":722,"date":724,"body":725,"category":9,"tags":726},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[723],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[16,727],"security",{"featured":11,"template":12,"slug":729},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":731,"config":741},{"title":732,"description":733,"authors":734,"category":9,"tags":736,"date":738,"heroImage":739,"body":740},"Secure and fast deployments to Google Agent Engine with GitLab","Follow this step-by-step guide to build an AI agent with Google's Agent Development Kit and deploy to Agent Engine using GitLab.",[735],"Regnard Raquedan",[16,737,106,554],"google","2026-02-26","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","In this tutorial, you'll learn how to deploy an AI agent built with Google's Agent Development Kit ([ADK](https://google.github.io/adk-docs/)) to [Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using GitLab's native Google Cloud integration and CI/CD pipelines. We'll cover IAM configuration, pipeline setup, and testing your deployed agent.\n\n## What is Agent Engine and why does it matter?\n\nAgent Engine is Google Cloud's managed runtime specifically designed for AI agents. Think of it as the production home for your agents — where they live, run, and scale without you having to manage the underlying infrastructure. Agent Engine handles infrastructure, scaling, session management, and memory storage so you can focus on building your agent — not managing servers. It also integrates natively with Google Cloud's logging, monitoring, and IAM.\n\n## Why use GitLab to deploy to Agent Engine?\n\nAI agent deployment is typically difficult to configure correctly. Security considerations, CI/CD orchestration, and cloud permissions create friction that slows down development cycles.\n\nGitLab streamlines this entire process while enhancing security:\n\n- **Built-in security scanning** — Every deployment is automatically scanned for vulnerabilities without additional configuration.\n- **Native Google Cloud integration** — Workload Identity Federation eliminates the need for service account keys.\n- **Simplified CI/CD** — GitLab's templates handle complex deployment logic.\n\n## Prerequisites\n\nBefore you begin, ensure you have:\n\n- A Google Cloud project with the following APIs enabled:\n  - Cloud Storage API\n  - Vertex AI API\n- A GitLab project for your source code and CI/CD pipeline\n- A Google Cloud Storage bucket for staging deployments\n- Google Cloud IAM integration configured in GitLab (see Step 1)\n\nHere are the steps to follow.\n\n## 1. Configure IAM integration\n\nThe foundation of secure deployment is proper IAM configuration between GitLab and Google Cloud using Workload Identity Federation.\n\nIn your GitLab project:\n\n1. Navigate to **Settings > Integrations**.\n2. Locate the **Google Cloud IAM** integration.\n3. Provide the following information:\n   - **Project ID**: Your Google Cloud project ID\n   - **Project Number**: Found in your Google Cloud console\n   - **Workload Identity Pool ID**: A unique identifier for your identity pool\n   - **Provider ID**: A unique identifier for your identity provider\n\nGitLab generates a script for you. Copy and run this script in Google Cloud Shell to establish the Workload Identity Federation between platforms.\n\n**Important:** Add these additional roles to your service principal for Agent Engine deployment:\n\n- `roles/aiplatform.user`\n- `roles/storage.objectAdmin`\n\nYou can add these roles using gcloud commands:\n\n```bash\nGCP_PROJECT_ID=\"\u003Cyour-project-id>\"\nGCP_PROJECT_NUMBER=\"\u003Cyour-project-number>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003Cyour-pool-id>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/aiplatform.user'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/storage.objectAdmin'\n```\n\n## 2. Create the CI/CD pipeline\n\nNow for the core of the deployment — the CI/CD pipeline. Create a `.gitlab-ci.yml` file in your project root:\n\n```yaml\nstages:\n  - test\n  - deploy\n\ncache:\n  paths:\n    - .cache/pip\n  key: ${CI_COMMIT_REF_SLUG}\n\nvariables:\n  GCP_PROJECT_ID: \"\u003Cyour-project-id>\"\n  GCP_REGION: \"us-central1\"\n  STORAGE_BUCKET: \"\u003Cyour-staging-bucket>\"\n  AGENT_NAME: \"Canada City Advisor\"\n  AGENT_ENTRY: \"canada_city_advisor\"\n\nimage: google/cloud-sdk:slim\n\n# Security scanning templates\ninclude:\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n\ndeploy-agent:\n  stage: deploy\n  identity: google_cloud\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n  before_script:\n    - gcloud config set core/disable_usage_reporting true\n    - gcloud config set component_manager/disable_update_check true\n    - pip install -q --no-cache-dir --upgrade pip google-genai google-cloud-aiplatform -r requirements.txt --break-system-packages\n  script:\n    - gcloud config set project $GCP_PROJECT_ID\n    - adk deploy agent_engine \n        --project=$GCP_PROJECT_ID \n        --region=$GCP_REGION \n        --staging_bucket=gs://$STORAGE_BUCKET \n        --display_name=\"$AGENT_NAME\" \n        $AGENT_ENTRY\n```\n\nThe pipeline consists of two stages:\n\n**Test stage** — GitLab's security scanners run automatically. The included templates provide dependency scanning, static application security testing (SAST), and secret detection without additional configuration.\n\n**Deploy stage** — Uses the ADK CLI to deploy your agent directly to Agent Engine. The staging bucket temporarily holds your application workload before Agent Engine picks it up for deployment.\n\n### Key configuration notes\n\n- The `identity: google_cloud` directive enables keyless authentication via Workload Identity Federation.\n- Security scanners are included as templates, meaning they run by default with no setup required.\n- The `adk deploy agent_engine` command handles all the complexity of packaging and deploying your agent.\n- Pipeline caching speeds up subsequent deployments by preserving pip dependencies.\n\n## 3. Deploy and verify\n\nWith your pipeline configured:\n\n1. Commit your agent code and `.gitlab-ci.yml` to GitLab.\n2. Navigate to **Build > Pipelines** to monitor execution.\n3. Watch the test stage complete security scans.\n4. Observe the deploy stage push your agent to Agent Engine.\n\nOnce the pipeline succeeds, verify your deployment in the Google Cloud Console:\n\n1. Navigate to **Vertex AI > Agent Engine**.\n2. Locate your deployed agent.\n3. Note the **resource name** — you'll need this for testing.\n\n## 4. Test your deployed agent\n\nTest your agent using a curl command. You'll need three pieces of information:\n\n- **Agent ID**: From the Agent Engine console (the resource name's numeric identifier)\n- **Project ID**: Your Google Cloud project\n- **Location**: The region where you deployed (e.g., `us-central1`)\n\n```bash\nPROJECT_ID=\"\u003Cyour-project-id>\"\nLOCATION=\"us-central1\"\nAGENT_ID=\"\u003Cyour-agent-id>\"\nTOKEN=$(gcloud auth print-access-token)\n\ncurl -X POST \\\n  -H \"Authorization: Bearer $TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  \"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines/${AGENT_ID}:streamQuery\" \\\n  -d '{\n    \"input\": {\n      \"message\": \"I make $85,000 per year and I prefer cities with mild winters and a vibrant cultural scene. I also want to be near the coast if possible. What Canadian cities would you recommend?\",\n      \"user_id\": \"demo-user\"\n    }\n  }' | jq -r '.content.parts[0].text'\n```\n\nIf everything is configured correctly, your agent will respond with personalized city recommendations based on the budget and lifestyle preferences provided.\n\n## Security benefits of this approach\n\nThis deployment pattern provides several security advantages:\n\n- **No long-lived credentials**: Workload Identity Federation eliminates service account keys entirely.\n- **Automated vulnerability scanning**: Every deployment is scanned before reaching production.\n- **Complete audit trail**: GitLab maintains full visibility of who deployed what and when.\n- **Principle of least privilege**: Fine-grained IAM roles limit access to only what's needed.\n\n## Summary\n\nDeploying AI agents to production doesn't have to be complex. By combining GitLab's DevSecOps platform with Google Cloud's Agent Engine, you get:\n\n- A managed runtime that handles scaling and infrastructure\n- Built-in security scanning without additional tooling\n- Keyless authentication via native cloud integration\n- A streamlined deployment process that fits modern AI development workflows\n\nWatch the full demo:\n\n\n\u003Cfigure class=\"video_container\"> \u003Ciframe src=\"https://www.youtube.com/embed/sxVFa2Mk-x4?si=Oi3cUjhgd7FT2yEd\" frameborder=\"0\" allowfullscreen=\"true\" title=\"Deploy AI Agents to Agent Engine with GitLab\"> \u003C/iframe> \u003C/figure>\n\n> Ready to try it yourself? Use this tutorial's [complete code example](https://gitlab.com/gitlab-partners-public/google-cloud/demos/agent-engine-demo) to get started now. Not a GitLab customer yet? Explore the DevSecOps platform with [a free trial](https://about.gitlab.com/free-trial/).\n",{"featured":31,"template":12,"slug":742},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":744},[745,758,769],{"id":746,"categories":747,"header":748,"text":749,"button":750,"image":755},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":751,"config":752},"Get your AI maturity score",{"href":753,"dataGaName":754,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":756},{"src":757},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":759,"categories":760,"header":761,"text":749,"button":762,"image":766},"devops-modernization",[17,557],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":770,"categories":771,"header":772,"text":749,"button":773,"image":777},"security-modernization",[727],"Are you trading speed for security?",{"text":774,"config":775},"Get your security maturity score",{"href":776,"dataGaName":754,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":781,"blurb":782,"button":783,"secondaryButton":788},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":784,"config":785},"Get your free trial",{"href":786,"dataGaName":48,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":493,"config":789},{"href":52,"dataGaName":53,"dataGaLocation":787},1772652075004]