[{"data":1,"prerenderedAt":802},["ShallowReactive",2],{"/en-us/blog/get-started-with-gitlab-duo-agentic-chat-in-the-web-ui":3,"navigation-en-us":38,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-Fatima Sarah Khalid|Daniel Helfand":688,"blog-related-posts-en-us-get-started-with-gitlab-duo-agentic-chat-in-the-web-ui":714,"assessment-promotions-en-us":754,"next-steps-en-us":792},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":22,"seo":30,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/get-started-with-gitlab-duo-agentic-chat-in-the-web-ui.yml","Get Started With Gitlab Duo Agentic Chat In The Web Ui",[7,8],"fatima-sarah-khalid","daniel-helfand",null,"ai-ml",{"featured":12,"template":13,"slug":14},true,"BlogPost","get-started-with-gitlab-duo-agentic-chat-in-the-web-ui",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"Get started with GitLab Duo Agentic Chat in the web UI","Learn about our new GitLab Duo AI feature that automates tasks by breaking down complex problems and executing operations across multiple sources.",[19,20],"Fatima Sarah Khalid","Daniel Helfand","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664458/Blog/Hero%20Images/Gartner_AI_Code_Assistants_Blog_Post_Cover_Image_1800x945.png","2025-08-11","In May 2025, GitLab launched an experimental feature called [GitLab Duo Agentic Chat](https://about.gitlab.com/blog/gitlab-duo-chat-gets-agentic-ai-makeover/). The goal of Agentic Chat was to build on the success of [GitLab Duo Chat](https://docs.gitlab.com/user/gitlab_duo_chat/), which is an AI chat experience built into supported IDEs and in the GitLab UI. While Chat provides answers and suggestions for developers using the GitLab platform, Agentic Chat can more directly interact with the GitLab API on behalf of users, taking actions on their behalf as a result of the conversation.\n\nIn addition to being available in a variety of IDEs, Agentic Chat is available directly within the GitLab UI for GitLab users with the Duo Pro or Enterprise add-on. Adding Agentic Chat to the GitLab UI helps make this experience more accessible to all GitLab users and easy to integrate into your workflows. To open Agentic Chat:\n\n1. Navigate to any Group or Project in your GitLab instance.  \n2. Look for the GitLab Duo Chat button (typically in the top right corner).  \n3. Click to open the chat panel.  \n4. Toggle to **Agentic mode (Beta)** in the chat window.\n\n**Pro tip:** Keep the chat panel open as you work — it maintains context and can help you across different pages and projects.\n\nTo get familiar with Agentic Chat, ask about the tools it can work with. This is like using the help command for a command-line tool.  \n\n```text\nWhat tools do you have access to? \n```\n![GitLab Duo Agentic Chat screen](https://res.cloudinary.com/about-gitlab-com/image/upload/v1754584200/emtgilbzbu8ftkynjozg.png)\nThe output above shows us that Agentic Chat has access to a variety of GitLab APIs and data that will allow it to perform complex tasks across the software development lifecycle.   \n## Issue management made easy\n\nGitLab Duo Agentic Chat can help you keep track of issues, find specific ones, understand the status, and take actions based on conversations in these issues. Instead of navigating through pages and pages of issues, you ask Agentic Chat about the issues in a project. It will respond with high-level information about the issues, including the priority, labels, and the status of the issue.\n\nFor a specific issue, Agentic Chat will fetch the issue details, provide a concise summary, highlight recent activity, and share the goal of the issue. This is particularly helpful when you need context or updates before a meeting or are researching the issue before picking it up. \n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1107479358?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 UI Issue Management\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\nYou can also try more complex queries if you're looking to better understand a project overall. And once you've discovered these issues, you can make changes to them like adding labels, updating milestones, and re-organizing them. \n\nFor example, maybe you're looking for all the issues that are database- or performance-related in order to prioritize them in the next sprint. You could task Agentic Chat with the following prompt.\n\n```text\nAnalyze all issues labeled 'performance' and 'database' - group them by component and show me which ones have had the most discussion activity in the last 30 days.\n```\n\nAgentic Chat will respond with issues grouped by the backend and frontend component of a project, identify the issues with significant discussion activity, and provide insights on these kinds of issues (e.g., when were most of these issues created or which component issues have more active discussion).\n\n```text\nCreate an issue template for bug reports that includes:\n- Steps to reproduce\n- Expected behavior vs actual behavior\n- Environment details (browser, OS, GitLab version)\n- Severity assessment\n- Screenshots/error logs section\nName it \"bug_report.md\" and format it as a proper GitLab issue template\n```\n\n## CI/CD support  \n\nThis is where GitLab Duo Agentic Chat truly becomes your debugging superhero. We've all been there: a pipeline fails and you have to click through job logs trying to understand what went wrong. Agentic Chat can do more than just explain the failure to you and suggest recommendations. After reviewing the failed pipeline logs, Agentic Chat can suggest a fix and also add the fix to a merge request you are working on.\n\nLet's say you have a merge request adding a new feature, but the pipeline is failing. Instead of clicking through each failed job and trying to piece together what's wrong, you can ask Agentic Chat to investigate.\n\nAgentic Chat will analyze the pipeline, check the job logs, and explain that the tests are failing because of missing test data or configuration issues. But here's where it gets even more powerful — you don't have to stop at understanding the problem. Agentic Chat can also act on the advice it presents and add commits to fix the pipeline in the merge request.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1107495269?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 Agentic Chat CI/CD Fix\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n## Building complex prompts\n\nGitLab Duo Agentic Chat can also help you craft your prompts. Let's say that you're running a bug bash with your team and want to triage all possible issues that might be bug reports.\n\nIf you use a simple prompt like below, Agentic Chat will come up with ways to find the related issues, such as searching for terms or pattern matching:\n\n```text\nI need help writing an effective prompt to find all possible bug report issues in my GitLab project, including those that might not be properly labeled as \"bug\".\n```\nOften you can use the recommendations in Agentic Chat to build a more in-depth prompt based on what you're looking for: \n```text\nI need help writing an effective prompt to find all possible bug report issues in my GitLab project, including those that might not be properly labeled as \"bug\". Please help me create a prompt that will:\n\n1. Search for common bug-related terminology beyond just the word \"bug\"\n2. Identify patterns that indicate bug reports (like \"steps to reproduce\", \"expected vs actual behavior\")\n3. Find technical issues that might be bugs (errors, crashes, performance problems)\n4. Catch user-reported problems that could be bugs but use different language\n\nThe prompt should ensure we don't miss any potential bugs regardless of how they're described or labeled. What would be the most effective approach and search strategy for this?\n```\n\nOnce you have the prompt and you're able to search for the issues you're looking for, that's where Agentic Chat really shines. Agentic Chat can triage and update those issues for you to prepare them for the bug bash:\n\n```markdown\nFind and triage all bug-related issues for our bug bash event. Execute these steps:\n\n1. Search for potential bugs using individual searches:\n   - Core terms: \"bug\", \"fix\", \"error\", \"broken\", \"issue\", \"problem\", \"not working\"\n   - Bug patterns: \"steps to reproduce\", \"expected behavior\", \"regression\"\n   - Technical issues: \"exception\", \"crash\", \"console error\", \"500 error\", \"404 error\"\n   - Performance: \"slow\", \"freezes\", \"unresponsive\"\n\n2. For each issue found:\n   - Add the \"Event - Bug Bash\" label\n   - Assign appropriate bug severity label (critical/high/medium/low)\n   - Add to the current bug bash milestone\n   - If missing \"bug\" label, add it\n\n3. Create a triage list organized by:\n   - Critical bugs (data loss, crashes, security)\n   - High priority (blocking features, frequent errors)\n   - Medium priority (workarounds available)\n   - Low priority (minor UI issues)\n\nSearch both open and closed issues. Focus on actionable bugs that can be fixed during the bug bash, excluding enhancement requests. Provide a summary table with issue numbers, titles, and assigned severity for the bug bash team.\n```\n\nYou can ask Agentic Chat to create a bug report template, which increases efficiency and eliminates some manual effort. Also, future bug reports will have the structure and labels you need for more efficient triaging.   \n## Tips for effective prompting \n\nWhen you're working with GitLab Duo Agentic Chat, it's important to phrase your requests with action-oriented verbs like \"create,\" \"update,\" \"fix,\" or \"assign.\" This will trigger the agentic tools to take action rather than summarize or share information with you. One approach before taking agentic actions can be to request summaries and analyses — the way we did with the issues about bugs. Then, see what gets returned before taking actions like applying a label or adding to a milestone. \n\nIt's also important to give clear criteria when asking for bulk operations. Specify exact conditions like \"all issues with the 'bug' label created in the last week\" or \"merge requests waiting for review for more than 3 days.\" The more specific you are, the more accurate and helpful the results will be.\n\nSince Agentic Chat has the ability to maintain context, you can chain requests and build on previous requests. After getting an initial set of issues, you might ask \"From those issues, which ones are unassigned?\" and then follow up with \"Assign the high-priority ones to the backend team.\" This allows you to refine and act on information iteratively. \n\nWe recommend starting with an open-ended request and allowing GitLab Duo to help you look for patterns or similar problems across your project. That will help you catch any problem that you may have missed or understand the scope of the challenge before taking action.   \n\n## Get hands-on with GitLab Duo Agentic Chat\n\nWe hope all the ideas above give you some thoughts on getting started with Agentic Chat, but we are even more excited to see all our users' ideas come to life with it. To try the Agentic Chat UI experience in your next project, sign up for a [free trial of GitLab Ultimate with Duo Enterprise](https://about.gitlab.com/free-trial/). You can learn more about GitLab Duo Agentic Chat on our [documentation page](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/), which also details how to enable Agentic Chat in the GitLab UI.\n",[25,26],"AI/ML","CI/CD","yml",{},"/en-us/blog/get-started-with-gitlab-duo-agentic-chat-in-the-web-ui",{"config":31,"title":16,"description":17},{"noIndex":32},false,"en-us/blog/get-started-with-gitlab-duo-agentic-chat-in-the-web-ui",[35,36],"aiml","cicd","ZiRwQG4YvBfsrchdc6MVs_POMnlvlgO7Uxkbxl5xX4I",{"data":39},{"logo":40,"freeTrial":45,"sales":50,"login":55,"items":60,"search":367,"minimal":398,"duo":417,"pricingDeployment":427},{"config":41},{"href":42,"dataGaName":43,"dataGaLocation":44},"/","gitlab logo","header",{"text":46,"config":47},"Get free trial",{"href":48,"dataGaName":49,"dataGaLocation":44},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":51,"config":52},"Talk to 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AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[720],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. 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.",[25,725],"DevOps platform",{"featured":32,"template":13,"slug":727},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":729,"config":739},{"title":730,"description":731,"heroImage":732,"authors":733,"date":735,"body":736,"category":10,"tags":737},"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",[734],"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/)",[25,738],"security",{"featured":12,"template":13,"slug":740},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":742,"config":752},{"title":743,"description":744,"authors":745,"category":10,"tags":747,"date":749,"heroImage":750,"body":751},"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.",[746],"Regnard Raquedan",[25,748,26,553],"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":32,"template":13,"slug":753},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":755},[756,769,781],{"id":757,"categories":758,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[10],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":773,"text":760,"button":774,"image":778},"devops-modernization",[772,556],"product","Are you just managing tools or shipping innovation?",{"text":775,"config":776},"Get your DevOps maturity score",{"href":777,"dataGaName":765,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":782,"categories":783,"header":784,"text":760,"button":785,"image":789},"security-modernization",[738],"Are you trading speed for security?",{"text":786,"config":787},"Get your security maturity score",{"href":788,"dataGaName":765,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":793,"blurb":794,"button":795,"secondaryButton":800},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":796,"config":797},"Get your free trial",{"href":798,"dataGaName":49,"dataGaLocation":799},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":493,"config":801},{"href":53,"dataGaName":54,"dataGaLocation":799},1772652086217]