[{"data":1,"prerenderedAt":789},["ShallowReactive",2],{"/en-us/blog/gitlab-duo-agent-platform-is-generally-available":3,"navigation-en-us":36,"banner-en-us":436,"footer-en-us":446,"blog-post-authors-en-us-Bill Staples":688,"blog-related-posts-en-us-gitlab-duo-agent-platform-is-generally-available":702,"assessment-promotions-en-us":742,"next-steps-en-us":779},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":11,"meta":27,"navigation":11,"path":28,"publishedDate":20,"seo":29,"stem":32,"tagSlugs":33,"__hash__":35},"blogPosts/en-us/blog/gitlab-duo-agent-platform-is-generally-available.yml","Gitlab Duo Agent Platform Is Generally Available",[7],"bill-staples",null,"ai-ml",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-duo-agent-platform-is-generally-available",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Announcing general availability for GitLab Duo Agent Platform ","Discover how agentic AI across the entire software lifecycle translates faster coding into accelerated innovation cycles.",[18],"Bill Staples","https://res.cloudinary.com/about-gitlab-com/image/upload/v1768314192/llizjeumcduj2enqpdi4.png","2026-01-15","We’re excited to announce the general availability (GA) of GitLab Duo Agent Platform. This is an important moment for GitLab, our customers and the industry at large. It is our first step in delivering our vision to bring agentic AI into the entire software development lifecycle.\n\nAI tools have been rapidly improving developers’ ability to write code, and in some cases, developers are reporting 10x productivity gains. Unfortunately, since only about 20% of a developer’s time is spent writing code, the associated improvement in total innovation velocity and delivery gained by AI is incremental. This is often described as the [AI paradox](https://about.gitlab.com/press/releases/2025-11-10-gitlab-survey-reveals-the-ai-paradox/) in software delivery.\n\nIn addition, for many teams, increasing the speed of code authoring has led to new bottlenecks including a larger backlog of code reviews, security vulnerabilities, compliance checks and downstream bug fixes.\n\nGitLab Duo Agent Platform addresses the AI paradox by unlocking intelligent orchestration and agentic AI automation across the software lifecycle.\n\nLearn more in this video, and read more below.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1154785472?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"18.8 Release Video V2\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n> :bulb: Join GitLab Transcend on February 10 to learn how agentic AI transforms software delivery. Hear from customers and discover how to jumpstart your own modernization journey. [Register now](https://about.gitlab.com/events/transcend/virtual/).\n\nWe're also excited to announce that GitLab customers with active GitLab Premium and Ultimate subscriptions are being credited with $12 and $24 dollars, respectively, in GitLab Credits per user at no additional cost.\\* These credits will refresh every month and give users access to all GitLab Duo Agent Platform features.\n\nHere is a simple explanation for how GitLab Credits work: a GitLab Credit is a virtual currency used for GitLab’s usage-based products. GitLab Duo Agent Platform usage will draw down on available credits, starting with the included credits mentioned above. From there, customers can decide to commit to a shared pool of credits for their entire organization, or pay for them monthly, on demand. For more information, please check out [our article introducing GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/).\n\nCustomers of GitLab Duo Pro or Duo Enterprise subscriptions are welcome to continue using those products, or migrate to Duo Agent Platform at any time. The remainder of your Duo Enterprise contract value can be converted into GitLab Credits at any time. Contact your GitLab representative to learn more.\n\nHere are exciting use cases and capabilities you can try today:\n\n### A unified experience for human and agent collaboration\n\n[GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/?utm_source=chatgpt.com) introduces a unified user experience designed for seamless integration between humans and their AI agents inside GitLab. Developers and their teams can engage Duo Agentic Chat on nearly every page, ask questions contextually, follow async agentic sessions and interact with agents within familiar workflows like issues, merge requests, and pipeline activities — making AI actions transparent and easy to guide through everyday work.\n\n### Agentic Chat: Intelligent, context-aware assistance\n\n[Gitlab Duo Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/) brings true multi-step reasoning across the GitLab Web UI and IDEs, using full lifecycle context from issues, merge requests, pipelines, security findings, and more. Building on the previously released Duo Chat, Agentic Chat can perform actions on your behalf autonomously and help you answer complex questions more comprehensively. It gives every member of the software team accurate, context-aware guidance that helps improve onboarding, code quality, and delivery speed.\n\n\n GitLab Duo Agentic Chat supports numerous [use cases](https://about.gitlab.com/gitlab-duo/prompt-library/) to enable developer \\\u003C\\> AI collaboration. For additional details on how to get started, please [see our \"Getting started with GitLab Duo Agent Platform\" guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/) and check out this [growing set of suggested prompts](https://about.gitlab.com/gitlab-duo/prompt-library/).\n\n\n* **Analyze**\n  In the Web UI, Agentic Chat can create issues, epics, merge requests, and provide summaries, highlight key findings, and offer actionable guidance based on real-time context from the specific project, issue, epic, merge request, and more. Agentic Chat helps developers understand unfamiliar code, dependencies, architecture, and project structure, in the IDE or inside a GitLab repo.\n\n\n\n* **Code**\n  Agentic Chat can generate code, configurations, and infrastructure-as-code across a wide range of languages and frameworks. It can help fix bugs, modernize architecture and code, generate tests, and produce documentation for faster onboarding. Directly at developers' fingertips, Agentic Chat is their collaboration partner in VS Code, JetBrains IDEs, Cursor, and Windsurf, with optional user- and workspace-level rules to tailor responses.\n\n* **CI/CD**\n  Agentic Chat can help you better understand, configure, and troubleshoot existing pipelines, or create new ones from scratch.\n\n* **Secure**\n  Agentic Chat can explain vulnerabilities, prioritize issues based on reachability, and recommend fixes that can help save you time.\n\n## Agents: Specialists that collaborate on demand\n\nGitLab Duo Agent Platform enables developers to delegate tasks to specialized agents. The platform offers a unique combination of foundational, custom, and external agents, all seamlessly integrated into GitLab user experience, making it easy to choose the right agent for any task.\n\n**[Foundational agents](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/)** are pre-built by GitLab experts and are ready out-of-the-box to handle the most complex tasks in the software delivery cycle. The following foundational agents are included as part of GitLab Duo Agent Platform’s general availability, with others currently in beta and coming soon.\n\n* [**Planner Agent**](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/planner/) helps teams structure, prioritize, and break down work directly inside GitLab so planning becomes clearer, faster, and easier to act on.\n\n* [**Security Analyst Agent**](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/security_analyst_agent/) reviews vulnerabilities and security signals, explains their impact in plain language, and helps teams understand what to address first.\n\n[**Custom agents**](https://docs.gitlab.com/user/duo_agent_platform/ai_catalog/) can be built using the AI Catalog, a central repository where teams create, publish, manage, and share custom agents and flows across the organization. Teams can create agents and flows with specific context and capabilities to replicate the way their engineering team works — and tackle problems using the engineering standards and guardrails their engineers use.\n\n[**External agents**](https://docs.gitlab.com/user/duo_agent_platform/agents/external/) are seamlessly integrated into GitLab and include some of the very best AI tools available, including Claude Code from Anthropic and Codex CLI from OpenAI. Users will enjoy native GitLab access to these tools for use cases like code generation, code review, and analysis with transparent security and embedded LLM subscriptions.\n\nTogether, these approaches give teams flexibility in how they adopt agentic AI, from specialized agents, to organization-specific automation, to integrating external AI tools — all within a single, governed platform.\n\n## Flows: Turning multi-step work into repeatable, guided progress\n\nFlows automate complex tasks with multiple agentic workflows, from start to finish.\n\nOur engineering team has built several flows included at GA, with more on the way:\n\n* [**Developer (Issue to Merge Request)**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/issue_to_mr/) flow builds a structured MR from a well-defined issue so teams can begin work immediately.\n\n* [**Convert to GitLab CI/CD**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/) flow helps teams migrate or modernize pipeline configurations without manual rewriting.\n\n* [**Fix CI/CD pipeline**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/fix_pipeline/) flow analyzes failures, identifies likely causes, and prepares recommended changes.\n\n* [**Code Review**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/) flow analyzes code changes, merge request comments, and more to streamline code reviews with AI-native analysis and feedback.\n\n* [**Software development in IDE**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/software_development/) flow guides work through everyday development and review stages.\n\n## MCP Client: Connect GitLab Duo Agent Platform to the tools your teams already use\n\nThe [MCP Client](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/) enables GitLab Duo Agent Platform in IDEs to securely connect to external systems like Jira, Slack, Confluence, and other MCP-compatible tools to pull in context and take action across your DevSecOps toolchain.\n\nInstead of AI assistance being siloed inside individual tools, the MCP Client allows GitLab Duo Agent Platform to understand and operate across the systems where planning, collaboration, and execution actually happen. This reduces manual context switching and enables more complete, end-to-end AI-powered workflows that reflect how teams work in practice.\n\nIncluded at GA:\n\n* Connection to external MCP-compatible systems such as Jira, Confluence, Slack, Playwright, and Grafana\n* Configuration at the workspace and user level\n* Group-level controls to enable or restrict MCP usage\n* User approval flow for tool access\n* Support across Agentic Chat in the IDE extensions\n\nWe plan to add more features to the GitLab MCP server capability, which is currently in beta, and make it generally available in upcoming releases.\n\n## Choose the right model for your team and workloads\n\nGitLab Duo Agent Platform is built on a flexible model selection framework that enables teams to tailor the platform to align with their privacy, security, and compliance needs. GitLab defaults to an optimal LLM for each feature, but administrators have the option to select from supported models such as OpenAI GPT-5 variants, Mistral, Meta Llama, and Anthropic Claude. This gives teams more precise control and flexibility over what is used for chat, coding tasks, and agent interactions for each specific use case, based on your organization’s standards. For a full list of supported models and details on model section configuration, see the [Model Selection](https://docs.gitlab.com/administration/gitlab_duo/model_selection/) section of our documentation.\n\n### Governance, visibility, and deployment flexibility\n\nThe GitLab Duo Agent Platform gives organizations the control and transparency they need to help them adopt AI responsibly, while offering flexible deployment options that work across different environments.\n\nIncluded at GA:\n\n* **Available on all platforms:** GitLab.com, GitLab Self-Managed, and GitLab Dedicated as part of the GitLab 18.8 release cycle.\n\n* **Governance and visibility:** Teams can see how agents are used, what actions they perform, and how they contribute to work. Usage and activity details help leaders understand adoption, measure impact, and ensure AI is being used appropriately. These controls make it easier to roll out AI at scale with confidence.\n\n* **Group-based access controls:** Administrators can define namespace-level rules governing which users can access GitLab Duo Agent Platform features, supporting flexible adoption from immediate organization-wide enablement to phased rollouts. With LDAP and SAML integration, they can enable governance at scale without manual configuration.\n\n* **Model selection and self-hosted options:** LLM selection is available for all GA features across GitLab.com, Self-Managed, and Dedicated. Top-level namespace owners choose the model, and subgroups inherit those settings automatically. For organizations that want more control, the platform supports self-hosted models for GitLab Self-Managed deployments.\n\nWatch a demo of GitLab Duo Agent Platform in action:\n\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1154786333?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"18.8 Demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Stay up to date with GitLab\n\nTo make sure you’re getting the latest features, security updates, and performance improvements, we recommend keeping your GitLab instance up to date. The following resources can help you plan and complete your upgrade:\n\n* [Upgrade Path Tool](https://gitlab-com.gitlab.io/support/toolbox/upgrade-path/) – enter your current version and see the exact upgrade steps for your instance\n* [Upgrade Documentation](https://docs.gitlab.com/update/upgrade_paths/) – detailed guides for each supported version, including requirements, step-by-step instructions, and best practices\n\nBy upgrading regularly, you’ll ensure your team benefits from the newest GitLab capabilities and remains secure and supported.\n\nFor organizations that want a hands-off approach, consider [GitLab’s Managed Maintenance service](https://content.gitlab.com/viewer/d1fe944dddb06394e6187f0028f010ad#1). Managed Maintenance can help your team stay focused on innovation while GitLab experts keep your Self-Managed instance reliably upgraded, secure, and ready to lead in DevSecOps. Ask your account manager for more information.\n\n---\n\n\\* GitLab customers with active Premium and Ultimate subscriptions will automatically receive $12 and $24 of included credits per user, respectively, which will reset each month. These credits are available for a limited time, and are subject to change ([see promo terms](https://about.gitlab.com/pricing/terms/)).\n\n*This blog post contains \"forward‑looking statements\" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934\\. Although we believe that the expectations reflected in these statements are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to differ materially. Further information on these risks and other factors is included under the caption \"Risk Factors\" in our filings with the SEC. 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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.",[708],"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.",[23,713],"DevOps platform",{"featured":31,"template":12,"slug":715},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":717,"config":727},{"title":718,"description":719,"heroImage":720,"authors":721,"date":723,"body":724,"category":9,"tags":725},"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",[722],"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/)",[23,726],"security",{"featured":11,"template":12,"slug":728},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":730,"config":740},{"title":731,"description":732,"authors":733,"category":9,"tags":735,"date":737,"heroImage":738,"body":739},"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.",[734],"Regnard Raquedan",[23,736,105,553],"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":31,"template":12,"slug":741},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":743},[744,757,768],{"id":745,"categories":746,"header":747,"text":748,"button":749,"image":754},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":750,"config":751},"Get your AI maturity score",{"href":752,"dataGaName":753,"dataGaLocation":240},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":755},{"src":756},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":758,"categories":759,"header":760,"text":748,"button":761,"image":765},"devops-modernization",[24,556],"Are you just managing tools or shipping innovation?",{"text":762,"config":763},"Get your DevOps maturity score",{"href":764,"dataGaName":753,"dataGaLocation":240},"/assessments/devops-modernization-assessment/",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":769,"categories":770,"header":771,"text":748,"button":772,"image":776},"security-modernization",[726],"Are you trading speed for security?",{"text":773,"config":774},"Get your security maturity score",{"href":775,"dataGaName":753,"dataGaLocation":240},"/assessments/security-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":780,"blurb":781,"button":782,"secondaryButton":787},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":783,"config":784},"Get your free trial",{"href":785,"dataGaName":47,"dataGaLocation":786},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":492,"config":788},{"href":51,"dataGaName":52,"dataGaLocation":786},1772652073427]