[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering":3,"navigation-en-us":39,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Bill Staples":690,"blog-related-posts-en-us-gitlab-18-3-expanding-ai-orchestration-in-software-engineering":704,"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":28,"isFeatured":11,"meta":29,"navigation":11,"path":30,"publishedDate":20,"seo":31,"stem":34,"tagSlugs":35,"__hash__":38},"blogPosts/en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering.yml","Gitlab 18 3 Expanding Ai Orchestration In Software Engineering",[7],"bill-staples",null,"ai-ml",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-18-3-expanding-ai-orchestration-in-software-engineering",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab 18.3: Expanding AI orchestration in software engineering","Learn how we're advancing human-AI collaboration with enhanced Flows, enterprise governance, and seamless tool integration.",[18],"Bill Staples","https://res.cloudinary.com/about-gitlab-com/image/upload/v1755711502/wuuadis1pza3zehqohcc.png","2025-08-21","Today, GitLab is a comprehensive DevSecOps platform, unifying every stage of the software lifecycle. Building on that foundation, we're on a journey toward becoming the world's first AI-native platform for software engineering. At GitLab, we believe the future of software engineering is an inherently human and AI collaboration, and we want to bring the very best AI capabilities to every GitLab user.  \n\nThis transformation is happening at three distinct layers that go beyond what other AI dev tools are doing:\n\n![AI-native transformation slide visualizing what's laid out below](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755762266/iwuugge3cxweiyvi0yjk.png)\n\n**First, we are a system of record.** Our unified data platform holds your most valuable digital assets. This includes your source code and intellectual property, as well as a wealth of unstructured data spanning project plans, bug backlogs, CI/CD configurations, deployment histories, security reports, and compliance data. This creates a treasure trove of contextual data that remains securely within your GitLab environment, unavailable to generic agents or large language models.\n\n**Second, we act as your software control plane.** We orchestrate your most critical business processes through Git repositories, REST APIs, and webhook-based interfaces that power your end-to-end software delivery. Many of our customers consider this a tier-0 dependency that their critical business processes rely on daily.\n\n**Third, we deliver a powerful user experience.** We deliver an integrated interface that helps eliminate the costly context-switching that slows down most engineering teams. With complete lifecycle visibility and collaboration tools in one platform, over 50 million registered users and our vast community depend on GitLab to get their work done. This expertise positions GitLab uniquely to pioneer intuitive human-to-AI collaboration that amplifies team productivity while preserving the workflows that our users know and trust.\n\n**Extending our platform with AI natively integrated at every layer**\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) integrates and extends all three of these layers. It is designed for extensibility and interoperability, enabling customers and partners to build solutions that create even more value. Our open platform approach emphasizes seamless connectivity with external AI tools and systems while being deeply integrated into our existing stack at all three layers.\n\n* First, we're extending our unified data platform with a **Knowledge Graph,** which indexes and stitches together code with all of the rest of your unstructured data, specifically optimized for agentic access. AI thrives on context, and we believe this will not only accelerate reasoning and inference by agents but also deliver lower-cost and higher-quality agentic outcomes.\n* Second, we're adding an important **Orchestration Layer** to our existing Control Plane in three distinct parts: enabling agents and flows to register as subscribers for GitLab SDLC events, building a new orchestration engine that allows for purpose-built, multi-agent flows, and exposing GitLab tools, agents, and flows via MCP and standard protocols for unparalleled interoperability.\n* Finally, we're extending the **GitLab experience** to deliver first-class agents and agent flows across the entire software development lifecycle. You'll be able to assign async tasks to agents, @ mention them in comments, and create custom agents with context specific to your workflows — but more importantly, GitLab is shipping native agents for every stage of development while unlocking a rich ecosystem of third-party agents. This creates true human-to-AI collaboration where agents become as natural to work with as your human teammates.\n\nWatch this video to see what's coming in 18.3 and beyond, or read on.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111796316?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"GitLab_18.3 Release_081925_MP_v1\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## What's new in GitLab 18.3\n\nWith 18.2, we introduced specialized [AI agents](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=Agents%20that%20work%20out%20of%20the%20box) that work alongside developers across the software development lifecycle, plus our [Software Development Flow](\u003Chttps://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=we%20are%20building%3A-,Software%20Development%20Flow,-(now%20in%20beta>) — a powerful feature that gives users the ability to orchestrate multiple agents to plan, implement, and test code changes end-to-end.\n\nGitLab 18.3 introduces expanded integrations and interoperability, more Flows, and enhanced context awareness across the entire software development lifecycle.\n\n### Expanded integrations and interoperability\n\nWe're delivering comprehensive AI extensibility through both first-party GitLab agents and a rich ecosystem of third-party agents, all with full access to project context and data. This approach maintains native GitLab workflows and governance while providing the flexibility to choose preferred tools through highly integrated orchestration between these agents and GitLab's core platform. Teams gain enhanced AI functionality while preserving key integration, oversight, and user experience benefits.\n\n* **MCP server - Universal AI integration:** GitLab's MCP ([Model Context Protocol](https://about.gitlab.com/topics/ai/model-context-protocol/)) server enables AI systems to securely integrate directly with your GitLab projects and development processes. This standardized interface eliminates custom integration overhead and allows your AI tools — including [Cursor](https://docs.cursor.com/en/tools/mcp) — to work intelligently within your existing GitLab environment. See our [docs](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/) for a full list of tools included with 18.3. **This is only the start; additional tools are planned for 18.4.**\n> *“Bringing GitLab workflows directly into Cursor is a critical step in reducing friction for developers. By minimizing the need for context switching, teams can check issue status, review merge requests, and monitor pipeline results without ever leaving their coding environment. This integration is a natural fit for our shared customers, and we look forward to a long-term partnership with GitLab to continue enhancing developer productivity.”*\n>\n> \\- **Ricky Doar, VP of Field Engineering at Cursor**\n>\n> *“GitLab's MCP server and CLI agent support create powerful new ways for Amazon Q to integrate with development workflows. Amazon Q Developer can now connect directly through GitLab's remote MCP interface, while teams can delegate development tasks by simply @ mentioning Amazon Q CLI in issues and merge requests. The robust security and governance capabilities built into these integrations give enterprises the confidence to leverage AI coding tools while preserving their development standards. Our partnership with GitLab demonstrates AWS' ongoing commitment to expanding our AI ecosystem and making intelligent development tools accessible wherever developers work.\"*\n>\n> \\- **Deepak Singh, Vice President of Developer Agents and Experiences at AWS**\n\n* **CLI agent support for Claude Code, Codex, Amazon Q, Google Gemini, and opencode (Bring Your Own Key):** 18.3 introduces integrations that enable teams to delegate routine development work by @ mentioning their agents directly in issues or merge requests. When developers mention these AI assistants, they automatically read the surrounding context and repository code, then respond to the user's comment with either ready-to-review code changes or inline comments. These integrations require you to bring your own API key for the respective AI providers and keep all interactions natively within GitLab's interface while maintaining proper permissions and audit trails.\n\n\n  **Note:** Third-party agents is a GitLab Premium Beta feature and only available to GitLab Duo Enterprise customers for evaluation.  \n\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111784124?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Third Party Agents Flows Claude Code\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n> *“Bringing Claude Code directly into GitLab puts AI assistance where millions of developers already collaborate and ship code daily. The ability to mention Claude directly in issues and merge requests removes friction while maintaining quality with human oversight and review processes. This update brings Claude Code's capabilities to more places where teams work, making AI a natural part of their developer workflow.”*\n>\n> **\\- Cat Wu, Claude Code Product Lead, Anthropic**\n>\n> *“With GitLab's new agent integration in 18.3 you can use opencode within your existing workflows. You can @mention opencode in an issue or merge request and it'll run your agent right in your CI pipeline. This ability to configure and run opencode the way you want is the type of integration we know the open source community really values.”*\n>\n> **\\- Jay V., CEO, opencode**\n\n* **Agentic Chat support for Visual Studio IDE and GitLab UI available to all Premium and Ultimate customers:** With 18.3, you no longer need to context-switch between tools to access GitLab's full development lifecycle data. Our enhanced integrations bring the complete power of GitLab Duo into the GitLab UI as well as IDEs — expanding support from JetBrains and VS Code to now include Visual Studio. This helps developers stay in flow while accessing rich project context, deployment history, and team collaboration data directly within their preferred environment.\n* **Expanded AI model support:** GitLab Duo Self-Hosted now supports additional AI models, giving teams more flexibility in their AI-supported development workflows. You can now deploy open source OpenAI GPT models (20B and 120B parameters) through vLLM on your datacenter hardware, or through cloud services like Azure OpenAI and AWS Bedrock in your private cloud. Additionally, Anthropic's Claude 4 is available on AWS Bedrock\n\n### New automated development flows\n\nGitLab Flows coordinate multiple AI agents with pre-built instructions to autonomously handle those time-consuming, mundane tasks so developers can focus on the work that matters most.\n\nGitLab 18.3 comes with two new Flows:\n\n* **Issue to MR Flow enabling automated code generation from concept to completion in minutes:** This Flow automatically converts issues into actionable merge requests (MRs) by coordinating agents to analyze requirements, prepare comprehensive implementation plans, and generate production-grade code that's ready for review — helping you turn ideas into reviewable implementations in minutes, not hours.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111782058?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Issue to MR\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n* **Convert CI File Flow built for seamless migration intelligence:** Our Convert CI File Flow streamlines migration workflows by having agents analyze existing CI/CD configurations and intelligently convert them to GitLab CI format with full pipeline compatibility. This helps eliminate the manual effort and potential errors of rewriting CI configurations from scratch, enabling teams to migrate entire deployment pipelines with confidence. 18.3 includes support for Jenkins migrations. Additional support is planned for future releases.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783724?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Convert to CI Flow\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Intelligent code and search\n\nAI point solutions typically operate with limited visibility into isolated code snippets, but GitLab's Knowledge Graph provides agents with environment context to help inform faster and more intelligent responses.\n\n* **Knowledge Graph for real-time code intelligence:** With 18.3, GitLab's Knowledge Graph now delivers real-time code indexing to enable faster code searches, delivering more accurate and contextual results. By understanding the relationships between files, dependencies, and development patterns across your entire codebase, our agents are designed to provide insights that would take human developers hours to uncover — **and this is just the first step in unlocking the powerful capabilities that are planned for Knowledge Graph.**\n\n### Enterprise governance\n\nAI transparency and organizational control are critical challenges that can hold teams back from fully adopting AI-powered development tools, with [85% of executives agreeing that agentic AI will create unprecedented security challenges](https://about.gitlab.com/software-innovation-report/).\n\nThese new features in 18.3 help address concerns around data governance, compliance requirements, and the need for visibility into AI decision-making processes so organizations can integrate AI within their existing security and policy frameworks.\n\n* **Agent Insights for transparency through intelligence:** Our built-in agent tracking provides visibility into agent decision-making processes. Users can optimize workflows and follow best practices through transparent activity tracking.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783244?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Agent Insights\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **GitLab Duo Code Review for Self-Hosted:** This brings the intelligence of GitLab Duo to organizations with strict data governance requirements by allowing teams to keep sensitive code in controlled environments.\n* **Hybrid model configurations for flexible AI deployment:** GitLab Duo Self-Hosted customers can now use hybrid model configurations, combining self-hosted AI models via their local AI gateway with GitLab's cloud models through GitLab's AI gateway, enabling access to various features.\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1111783569?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Self Hosted Models Code Review\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **Enhanced security with OAuth support:** Our MCP server now includes full OAuth 2.0 authentication support, enabling secure connections to protected resources and sensitive development environments. This implementation follows the draft OAuth specification for MCP, handling authorization flows, token management, and dynamic client registration. \n\n### Secure by Design platform: Governance that scales\n\nTrue platform security requires consistent application of governance principles across every layer of the development lifecycle. The same security fundamentals that make AI adoption safe — least-privilege access, centralized policy management, proactive monitoring, and granular permissions — must be embedded throughout the entire SDLC to create a cohesive, defense-in-depth approach.\n\nGitLab 18.3 strengthens the foundational controls that help protect your entire software supply chain with these new updates:\n\n* **Custom admin role:** Provides granular, purpose-built administrative permissions, replacing blanket admin access with precise, least-privilege controls. Instead of granting blanket administrative privileges that create security risks, organizations can now create specialized roles tailored to specific functions — platform teams managing runners and monitoring, support teams handling user management, and leadership accessing dashboards and usage statistics. With complete role lifecycle management through UI and API, audit logging, and auto-generated documentation, this feature enables true least-privilege administration while helping maintain operational efficiency and improve overall instance security.\n* **Instance-level compliance framework and security policy management**: Organizations can now designate a dedicated compliance group that has the authority to apply standardized frameworks and security policies directly to top-level groups, automatically cascading enforcement to all their subgroups and projects. This centralized approach eliminates the compliance adoption blocker of fragmented policy management while maintaining group autonomy for additional local policies.\n* **Enhanced violations reporting:** Teams now receive immediate notifications when unauthorized changes are made to MR approval rules, framework policies lack proper approvals, or time-based compliance controls are violated. By directly linking violations to specific compliance framework controls, teams get actionable insights that tell them exactly which requirement was breached, turning compliance from a reactive checkbox exercise into a proactive, integrated part of the development and security workflow.\n* **Fine-grained permissions for CI/CD job tokens:** Replaces broad token access with granular, explicit permissions that grant CI/CD jobs access only to specific API endpoints they actually need. Instead of allowing jobs blanket access to project resources, teams can now define precise permissions for deployments, packages, releases, environments, and other critical resources, reducing the attack surface and potential for privilege escalation.\n* **AWS Secrets Manager integration:** Teams using AWS Secrets Manager can now retrieve secrets directly in GitLab CI/CD jobs, simplifying the build and deploy processes. Secrets are accessed by a GitLab Runner using OpenID Connect protocol-based authentication, masked to prevent exposure in job logs, and destroyed after use. This approach eliminates the need to store secrets in variables and integrates cleanly into existing GitLab and AWS-based workflows. Developed in close collaboration with Deutsche Bahn and the AWS Secrets Manager team, this integration reflects our commitment to building solutions alongside customers to solve real-world challenges.\n\n### Artifact management: Securing your software supply chain\n\nWhen artifacts aren't properly governed, small changes can have big consequences. Mutable packages, overwritten container images, and inconsistent rules across tools can trigger production outages, introduce vulnerabilities, and create compliance gaps. For enterprise DevSecOps, secure, centralized artifact management is essential for keeping the software supply chain intact.\n\n#### Enterprise-grade artifact protection in 18.3\n\nBuilding on our comprehensive package protection capabilities, GitLab 18.3 adds important new features:\n\n* **Conan revisions support:** New in 18.3, [Conan revisions](https://docs.gitlab.com/user/packages/conan_2_repository/#conan-revisions) provide package immutability for C++ developers. When changes are made to a package without changing its version, Conan calculates unique identifiers to track these changes, enabling teams to maintain immutable packages while preserving version clarity.\n* **Enhanced Container Registry security:** Following the successful launch of [immutable container tags](https://docs.gitlab.com/user/packages/container_registry/immutable_container_tags/) in 18.2, we're seeing strong enterprise adoption. Once a tag is created that matches an immutable rule, no one — regardless of permission level — can modify that container image, preventing unintended changes to production dependencies.\n\nThese enhancements complement our existing protection capabilities for npm, PyPI, Maven, NuGet, Helm charts, and generic packages, enabling platform teams to implement consistent governance across their entire software supply chain — a requirement for organizations building secure internal developer platforms.\n\nUnlike standalone artifact solutions, GitLab's integrated approach eliminates context switching between tools while providing end-to-end traceability from code to deployment, enabling platform teams to implement consistent governance across their entire software delivery pipeline.\n\n### Embedded views: Real-time visibility and reports\n\nAs GitLab projects grow in complexity, teams find themselves navigating between issues, merge requests, epics, and milestones to maintain visibility into work status. The challenge lies in consolidating this information efficiently while ensuring teams have real-time access to project progress without context switching or breaking their flow.\n**Launching real-time work status visibility in 18.3**\nGitLab 18.3's [embedded views, powered by our powerful GitLab Query Language](https://docs.gitlab.com/user/glql/#embedded-views) (GLQL), eliminate context switching by bringing live project data directly into your workflow:\n* **Dynamic views:** Insert live GLQL queries in Markdown code blocks throughout wiki pages, epics, issues, and merge requests that automatically refresh with current project states each time you load the page.\n* **Contextual personalization:** Views automatically adapt using functions like `currentUser()` and `today()` to show relevant information for whoever is viewing, without manual configuration.\n* **Powerful filtering:** Filter by 25+ fields, including assignee, author, label, milestone, health status, and creation date.\n* **Display flexibility:** Present data as tables, lists, or numbered lists with customizable field selection, item limits, and sort orders to keep your views focused and actionable\n\nUnlike fragmented project management approaches, we've designed embedded views to maintain your workflow continuity while providing real-time visibility, enabling teams to make informed decisions without losing focus or switching between multiple tools and interfaces.\n\n> Learn about the [newest features in GitLab 18.3](https://about.gitlab.com/releases/2025/08/21/gitlab-18-3-released/).\n## Get started today\nGitLab 18.3 is available now for GitLab Premium and Ultimate users on GitLab.com and self-managed environments.\n\nGitLab Dedicated customers are now upgraded to 18.2 and will be able to use the features released with GitLab 18.3 next month.\n\nReady to experience the future of software engineering?[ Enable beta and experimental features for GitLab Duo](https://docs.gitlab.com/user/gitlab_duo/turn_on_off/#turn-on-beta-and-experimental-features) and start collaborating with AI agents that understand your complete development context.\n\nNew to GitLab? [Start your free trial](https://gitlab.com/-/trials/new) today and discover why the future of software engineering is human and AI collaboration, orchestrated through the world's most comprehensive DevSecOps platform.\n\n\u003Cp>\u003Csmall>\u003Cem>This blog post contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in the forward-looking statements contained in this blog post are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements.\u003C/em>\u003C/p>\n\u003Cp>\u003Cem>Further information on risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those included in or contemplated by the forward-looking statements contained in this blog post are included under the caption “Risk Factors” and elsewhere in the filings and reports we make with the Securities and Exchange Commission. We do not undertake any obligation to update or release any revisions to any forward-looking statement or to report any events or circumstances after the date of this blog post or to reflect the occurrence of unanticipated events, except as required by law.\u003C/em>\u003C/small>\u003C/p>",[23,24,25,26,27],"product","AI/ML","DevSecOps platform","features","security","yml",{},"/en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering",{"config":32,"title":15,"description":16},{"noIndex":33},false,"en-us/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering",[23,36,37,26,27],"aiml","devsecops-platform","X_DfjtFycAkxOIGqXLAJbtlPJAamTX_OdeLRPQSGSF8",{"data":40},{"logo":41,"freeTrial":46,"sales":51,"login":56,"items":61,"search":369,"minimal":400,"duo":419,"pricingDeployment":429},{"config":42},{"href":43,"dataGaName":44,"dataGaLocation":45},"/","gitlab logo","header",{"text":47,"config":48},"Get free trial",{"href":49,"dataGaName":50,"dataGaLocation":45},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":52,"config":53},"Talk to sales",{"href":54,"dataGaName":55,"dataGaLocation":45},"/sales/","sales",{"text":57,"config":58},"Sign in",{"href":59,"dataGaName":60,"dataGaLocation":45},"https://gitlab.com/users/sign_in/","sign in",[62,89,184,189,290,350],{"text":63,"config":64,"cards":66},"Platform",{"dataNavLevelOne":65},"platform",[67,73,81],{"title":63,"description":68,"link":69},"The intelligent orchestration platform for DevSecOps",{"text":70,"config":71},"Explore our Platform",{"href":72,"dataGaName":65,"dataGaLocation":45},"/platform/",{"title":74,"description":75,"link":76},"GitLab Duo Agent Platform","Agentic AI for the entire software lifecycle",{"text":77,"config":78},"Meet GitLab Duo",{"href":79,"dataGaName":80,"dataGaLocation":45},"/gitlab-duo-agent-platform/","gitlab duo agent platform",{"title":82,"description":83,"link":84},"Why GitLab","See the top reasons enterprises choose GitLab",{"text":85,"config":86},"Learn more",{"href":87,"dataGaName":88,"dataGaLocation":45},"/why-gitlab/","why gitlab",{"text":90,"left":11,"config":91,"link":93,"lists":97,"footer":166},"Product",{"dataNavLevelOne":92},"solutions",{"text":94,"config":95},"View all Solutions",{"href":96,"dataGaName":92,"dataGaLocation":45},"/solutions/",[98,122,145],{"title":99,"description":100,"link":101,"items":106},"Automation","CI/CD and automation to accelerate deployment",{"config":102},{"icon":103,"href":104,"dataGaName":105,"dataGaLocation":45},"AutomatedCodeAlt","/solutions/delivery-automation/","automated software delivery",[107,111,114,118],{"text":108,"config":109},"CI/CD",{"href":110,"dataGaLocation":45,"dataGaName":108},"/solutions/continuous-integration/",{"text":74,"config":112},{"href":79,"dataGaLocation":45,"dataGaName":113},"gitlab duo agent platform - product menu",{"text":115,"config":116},"Source Code Management",{"href":117,"dataGaLocation":45,"dataGaName":115},"/solutions/source-code-management/",{"text":119,"config":120},"Automated Software Delivery",{"href":104,"dataGaLocation":45,"dataGaName":121},"Automated software delivery",{"title":123,"description":124,"link":125,"items":130},"Security","Deliver code faster without compromising security",{"config":126},{"href":127,"dataGaName":128,"dataGaLocation":45,"icon":129},"/solutions/application-security-testing/","security and compliance","ShieldCheckLight",[131,135,140],{"text":132,"config":133},"Application Security Testing",{"href":127,"dataGaName":134,"dataGaLocation":45},"Application security testing",{"text":136,"config":137},"Software Supply Chain Security",{"href":138,"dataGaLocation":45,"dataGaName":139},"/solutions/supply-chain/","Software supply chain security",{"text":141,"config":142},"Software Compliance",{"href":143,"dataGaName":144,"dataGaLocation":45},"/solutions/software-compliance/","software compliance",{"title":146,"link":147,"items":152},"Measurement",{"config":148},{"icon":149,"href":150,"dataGaName":151,"dataGaLocation":45},"DigitalTransformation","/solutions/visibility-measurement/","visibility and measurement",[153,157,161],{"text":154,"config":155},"Visibility & Measurement",{"href":150,"dataGaLocation":45,"dataGaName":156},"Visibility and Measurement",{"text":158,"config":159},"Value Stream Management",{"href":160,"dataGaLocation":45,"dataGaName":158},"/solutions/value-stream-management/",{"text":162,"config":163},"Analytics & Insights",{"href":164,"dataGaLocation":45,"dataGaName":165},"/solutions/analytics-and-insights/","Analytics and insights",{"title":167,"items":168},"GitLab for",[169,174,179],{"text":170,"config":171},"Enterprise",{"href":172,"dataGaLocation":45,"dataGaName":173},"/enterprise/","enterprise",{"text":175,"config":176},"Small Business",{"href":177,"dataGaLocation":45,"dataGaName":178},"/small-business/","small business",{"text":180,"config":181},"Public Sector",{"href":182,"dataGaLocation":45,"dataGaName":183},"/solutions/public-sector/","public sector",{"text":185,"config":186},"Pricing",{"href":187,"dataGaName":188,"dataGaLocation":45,"dataNavLevelOne":188},"/pricing/","pricing",{"text":190,"config":191,"link":193,"lists":197,"feature":277},"Resources",{"dataNavLevelOne":192},"resources",{"text":194,"config":195},"View all resources",{"href":196,"dataGaName":192,"dataGaLocation":45},"/resources/",[198,231,249],{"title":199,"items":200},"Getting started",[201,206,211,216,221,226],{"text":202,"config":203},"Install",{"href":204,"dataGaName":205,"dataGaLocation":45},"/install/","install",{"text":207,"config":208},"Quick start guides",{"href":209,"dataGaName":210,"dataGaLocation":45},"/get-started/","quick setup checklists",{"text":212,"config":213},"Learn",{"href":214,"dataGaLocation":45,"dataGaName":215},"https://university.gitlab.com/","learn",{"text":217,"config":218},"Product documentation",{"href":219,"dataGaName":220,"dataGaLocation":45},"https://docs.gitlab.com/","product documentation",{"text":222,"config":223},"Best practice videos",{"href":224,"dataGaName":225,"dataGaLocation":45},"/getting-started-videos/","best practice videos",{"text":227,"config":228},"Integrations",{"href":229,"dataGaName":230,"dataGaLocation":45},"/integrations/","integrations",{"title":232,"items":233},"Discover",[234,239,244],{"text":235,"config":236},"Customer success stories",{"href":237,"dataGaName":238,"dataGaLocation":45},"/customers/","customer success stories",{"text":240,"config":241},"Blog",{"href":242,"dataGaName":243,"dataGaLocation":45},"/blog/","blog",{"text":245,"config":246},"Remote",{"href":247,"dataGaName":248,"dataGaLocation":45},"https://handbook.gitlab.com/handbook/company/culture/all-remote/","remote",{"title":250,"items":251},"Connect",[252,257,262,267,272],{"text":253,"config":254},"GitLab Services",{"href":255,"dataGaName":256,"dataGaLocation":45},"/services/","services",{"text":258,"config":259},"Community",{"href":260,"dataGaName":261,"dataGaLocation":45},"/community/","community",{"text":263,"config":264},"Forum",{"href":265,"dataGaName":266,"dataGaLocation":45},"https://forum.gitlab.com/","forum",{"text":268,"config":269},"Events",{"href":270,"dataGaName":271,"dataGaLocation":45},"/events/","events",{"text":273,"config":274},"Partners",{"href":275,"dataGaName":276,"dataGaLocation":45},"/partners/","partners",{"backgroundColor":278,"textColor":279,"text":280,"image":281,"link":285},"#2f2a6b","#fff","Insights for the future of software development",{"altText":282,"config":283},"the source promo card",{"src":284},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758208064/dzl0dbift9xdizyelkk4.svg",{"text":286,"config":287},"Read the latest",{"href":288,"dataGaName":289,"dataGaLocation":45},"/the-source/","the source",{"text":291,"config":292,"lists":294},"Company",{"dataNavLevelOne":293},"company",[295],{"items":296},[297,302,308,310,315,320,325,330,335,340,345],{"text":298,"config":299},"About",{"href":300,"dataGaName":301,"dataGaLocation":45},"/company/","about",{"text":303,"config":304,"footerGa":307},"Jobs",{"href":305,"dataGaName":306,"dataGaLocation":45},"/jobs/","jobs",{"dataGaName":306},{"text":268,"config":309},{"href":270,"dataGaName":271,"dataGaLocation":45},{"text":311,"config":312},"Leadership",{"href":313,"dataGaName":314,"dataGaLocation":45},"/company/team/e-group/","leadership",{"text":316,"config":317},"Team",{"href":318,"dataGaName":319,"dataGaLocation":45},"/company/team/","team",{"text":321,"config":322},"Handbook",{"href":323,"dataGaName":324,"dataGaLocation":45},"https://handbook.gitlab.com/","handbook",{"text":326,"config":327},"Investor relations",{"href":328,"dataGaName":329,"dataGaLocation":45},"https://ir.gitlab.com/","investor relations",{"text":331,"config":332},"Trust Center",{"href":333,"dataGaName":334,"dataGaLocation":45},"/security/","trust center",{"text":336,"config":337},"AI Transparency Center",{"href":338,"dataGaName":339,"dataGaLocation":45},"/ai-transparency-center/","ai transparency center",{"text":341,"config":342},"Newsletter",{"href":343,"dataGaName":344,"dataGaLocation":45},"/company/contact/#contact-forms","newsletter",{"text":346,"config":347},"Press",{"href":348,"dataGaName":349,"dataGaLocation":45},"/press/","press",{"text":351,"config":352,"lists":353},"Contact us",{"dataNavLevelOne":293},[354],{"items":355},[356,359,364],{"text":52,"config":357},{"href":54,"dataGaName":358,"dataGaLocation":45},"talk to sales",{"text":360,"config":361},"Support portal",{"href":362,"dataGaName":363,"dataGaLocation":45},"https://support.gitlab.com","support portal",{"text":365,"config":366},"Customer portal",{"href":367,"dataGaName":368,"dataGaLocation":45},"https://customers.gitlab.com/customers/sign_in/","customer portal",{"close":370,"login":371,"suggestions":378},"Close",{"text":372,"link":373},"To search repositories and projects, login to",{"text":374,"config":375},"gitlab.com",{"href":59,"dataGaName":376,"dataGaLocation":377},"search login","search",{"text":379,"default":380},"Suggestions",[381,383,387,389,393,397],{"text":74,"config":382},{"href":79,"dataGaName":74,"dataGaLocation":377},{"text":384,"config":385},"Code Suggestions (AI)",{"href":386,"dataGaName":384,"dataGaLocation":377},"/solutions/code-suggestions/",{"text":108,"config":388},{"href":110,"dataGaName":108,"dataGaLocation":377},{"text":390,"config":391},"GitLab on AWS",{"href":392,"dataGaName":390,"dataGaLocation":377},"/partners/technology-partners/aws/",{"text":394,"config":395},"GitLab on Google Cloud",{"href":396,"dataGaName":394,"dataGaLocation":377},"/partners/technology-partners/google-cloud-platform/",{"text":398,"config":399},"Why GitLab?",{"href":87,"dataGaName":398,"dataGaLocation":377},{"freeTrial":401,"mobileIcon":406,"desktopIcon":411,"secondaryButton":414},{"text":402,"config":403},"Start free trial",{"href":404,"dataGaName":50,"dataGaLocation":405},"https://gitlab.com/-/trials/new/","nav",{"altText":407,"config":408},"Gitlab Icon",{"src":409,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203874/jypbw1jx72aexsoohd7x.svg","gitlab icon",{"altText":407,"config":412},{"src":413,"dataGaName":410,"dataGaLocation":405},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1758203875/gs4c8p8opsgvflgkswz9.svg",{"text":415,"config":416},"Get Started",{"href":417,"dataGaName":418,"dataGaLocation":405},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com/compare/gitlab-vs-github/","get started",{"freeTrial":420,"mobileIcon":425,"desktopIcon":427},{"text":421,"config":422},"Learn more about GitLab Duo",{"href":423,"dataGaName":424,"dataGaLocation":405},"/gitlab-duo/","gitlab duo",{"altText":407,"config":426},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":428},{"src":413,"dataGaName":410,"dataGaLocation":405},{"freeTrial":430,"mobileIcon":435,"desktopIcon":437},{"text":431,"config":432},"Back to pricing",{"href":187,"dataGaName":433,"dataGaLocation":405,"icon":434},"back to pricing","GoBack",{"altText":407,"config":436},{"src":409,"dataGaName":410,"dataGaLocation":405},{"altText":407,"config":438},{"src":413,"dataGaName":410,"dataGaLocation":405},{"title":440,"button":441,"config":446},"See how agentic AI transforms software delivery",{"text":442,"config":443},"Watch GitLab Transcend now",{"href":444,"dataGaName":445,"dataGaLocation":45},"/events/transcend/virtual/","transcend event",{"layout":447,"icon":448},"release","AiStar",{"data":450},{"text":451,"source":452,"edit":458,"contribute":463,"config":468,"items":473,"minimal":679},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":453,"config":454},"View page source",{"href":455,"dataGaName":456,"dataGaLocation":457},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":459,"config":460},"Edit this page",{"href":461,"dataGaName":462,"dataGaLocation":457},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/content/","web ide",{"text":464,"config":465},"Please contribute",{"href":466,"dataGaName":467,"dataGaLocation":457},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/-/blob/main/CONTRIBUTING.md/","please contribute",{"twitter":469,"facebook":470,"youtube":471,"linkedin":472},"https://twitter.com/gitlab","https://www.facebook.com/gitlab","https://www.youtube.com/channel/UCnMGQ8QHMAnVIsI3xJrihhg","https://www.linkedin.com/company/gitlab-com",[474,521,574,618,645],{"title":185,"links":475,"subMenu":490},[476,480,485],{"text":477,"config":478},"View plans",{"href":187,"dataGaName":479,"dataGaLocation":457},"view plans",{"text":481,"config":482},"Why Premium?",{"href":483,"dataGaName":484,"dataGaLocation":457},"/pricing/premium/","why premium",{"text":486,"config":487},"Why Ultimate?",{"href":488,"dataGaName":489,"dataGaLocation":457},"/pricing/ultimate/","why ultimate",[491],{"title":492,"links":493},"Contact Us",[494,497,499,501,506,511,516],{"text":495,"config":496},"Contact sales",{"href":54,"dataGaName":55,"dataGaLocation":457},{"text":360,"config":498},{"href":362,"dataGaName":363,"dataGaLocation":457},{"text":365,"config":500},{"href":367,"dataGaName":368,"dataGaLocation":457},{"text":502,"config":503},"Status",{"href":504,"dataGaName":505,"dataGaLocation":457},"https://status.gitlab.com/","status",{"text":507,"config":508},"Terms of use",{"href":509,"dataGaName":510,"dataGaLocation":457},"/terms/","terms of use",{"text":512,"config":513},"Privacy statement",{"href":514,"dataGaName":515,"dataGaLocation":457},"/privacy/","privacy statement",{"text":517,"config":518},"Cookie preferences",{"dataGaName":519,"dataGaLocation":457,"id":520,"isOneTrustButton":11},"cookie preferences","ot-sdk-btn",{"title":90,"links":522,"subMenu":530},[523,526],{"text":25,"config":524},{"href":72,"dataGaName":525,"dataGaLocation":457},"devsecops platform",{"text":527,"config":528},"AI-Assisted Development",{"href":423,"dataGaName":529,"dataGaLocation":457},"ai-assisted development",[531],{"title":532,"links":533},"Topics",[534,539,544,549,554,559,564,569],{"text":535,"config":536},"CICD",{"href":537,"dataGaName":538,"dataGaLocation":457},"/topics/ci-cd/","cicd",{"text":540,"config":541},"GitOps",{"href":542,"dataGaName":543,"dataGaLocation":457},"/topics/gitops/","gitops",{"text":545,"config":546},"DevOps",{"href":547,"dataGaName":548,"dataGaLocation":457},"/topics/devops/","devops",{"text":550,"config":551},"Version Control",{"href":552,"dataGaName":553,"dataGaLocation":457},"/topics/version-control/","version control",{"text":555,"config":556},"DevSecOps",{"href":557,"dataGaName":558,"dataGaLocation":457},"/topics/devsecops/","devsecops",{"text":560,"config":561},"Cloud Native",{"href":562,"dataGaName":563,"dataGaLocation":457},"/topics/cloud-native/","cloud native",{"text":565,"config":566},"AI for Coding",{"href":567,"dataGaName":568,"dataGaLocation":457},"/topics/devops/ai-for-coding/","ai for coding",{"text":570,"config":571},"Agentic AI",{"href":572,"dataGaName":573,"dataGaLocation":457},"/topics/agentic-ai/","agentic ai",{"title":575,"links":576},"Solutions",[577,579,581,586,590,593,597,600,602,605,608,613],{"text":132,"config":578},{"href":127,"dataGaName":132,"dataGaLocation":457},{"text":121,"config":580},{"href":104,"dataGaName":105,"dataGaLocation":457},{"text":582,"config":583},"Agile development",{"href":584,"dataGaName":585,"dataGaLocation":457},"/solutions/agile-delivery/","agile delivery",{"text":587,"config":588},"SCM",{"href":117,"dataGaName":589,"dataGaLocation":457},"source code management",{"text":535,"config":591},{"href":110,"dataGaName":592,"dataGaLocation":457},"continuous integration & delivery",{"text":594,"config":595},"Value stream management",{"href":160,"dataGaName":596,"dataGaLocation":457},"value stream management",{"text":540,"config":598},{"href":599,"dataGaName":543,"dataGaLocation":457},"/solutions/gitops/",{"text":170,"config":601},{"href":172,"dataGaName":173,"dataGaLocation":457},{"text":603,"config":604},"Small business",{"href":177,"dataGaName":178,"dataGaLocation":457},{"text":606,"config":607},"Public sector",{"href":182,"dataGaName":183,"dataGaLocation":457},{"text":609,"config":610},"Education",{"href":611,"dataGaName":612,"dataGaLocation":457},"/solutions/education/","education",{"text":614,"config":615},"Financial services",{"href":616,"dataGaName":617,"dataGaLocation":457},"/solutions/finance/","financial services",{"title":190,"links":619},[620,622,624,626,629,631,633,635,637,639,641,643],{"text":202,"config":621},{"href":204,"dataGaName":205,"dataGaLocation":457},{"text":207,"config":623},{"href":209,"dataGaName":210,"dataGaLocation":457},{"text":212,"config":625},{"href":214,"dataGaName":215,"dataGaLocation":457},{"text":217,"config":627},{"href":219,"dataGaName":628,"dataGaLocation":457},"docs",{"text":240,"config":630},{"href":242,"dataGaName":243,"dataGaLocation":457},{"text":235,"config":632},{"href":237,"dataGaName":238,"dataGaLocation":457},{"text":245,"config":634},{"href":247,"dataGaName":248,"dataGaLocation":457},{"text":253,"config":636},{"href":255,"dataGaName":256,"dataGaLocation":457},{"text":258,"config":638},{"href":260,"dataGaName":261,"dataGaLocation":457},{"text":263,"config":640},{"href":265,"dataGaName":266,"dataGaLocation":457},{"text":268,"config":642},{"href":270,"dataGaName":271,"dataGaLocation":457},{"text":273,"config":644},{"href":275,"dataGaName":276,"dataGaLocation":457},{"title":291,"links":646},[647,649,651,653,655,657,659,663,668,670,672,674],{"text":298,"config":648},{"href":300,"dataGaName":293,"dataGaLocation":457},{"text":303,"config":650},{"href":305,"dataGaName":306,"dataGaLocation":457},{"text":311,"config":652},{"href":313,"dataGaName":314,"dataGaLocation":457},{"text":316,"config":654},{"href":318,"dataGaName":319,"dataGaLocation":457},{"text":321,"config":656},{"href":323,"dataGaName":324,"dataGaLocation":457},{"text":326,"config":658},{"href":328,"dataGaName":329,"dataGaLocation":457},{"text":660,"config":661},"Sustainability",{"href":662,"dataGaName":660,"dataGaLocation":457},"/sustainability/",{"text":664,"config":665},"Diversity, inclusion and belonging (DIB)",{"href":666,"dataGaName":667,"dataGaLocation":457},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":331,"config":669},{"href":333,"dataGaName":334,"dataGaLocation":457},{"text":341,"config":671},{"href":343,"dataGaName":344,"dataGaLocation":457},{"text":346,"config":673},{"href":348,"dataGaName":349,"dataGaLocation":457},{"text":675,"config":676},"Modern Slavery Transparency Statement",{"href":677,"dataGaName":678,"dataGaLocation":457},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":680},[681,684,687],{"text":682,"config":683},"Terms",{"href":509,"dataGaName":510,"dataGaLocation":457},{"text":685,"config":686},"Cookies",{"dataGaName":519,"dataGaLocation":457,"id":520,"isOneTrustButton":11},{"text":688,"config":689},"Privacy",{"href":514,"dataGaName":515,"dataGaLocation":457},[691],{"id":692,"title":18,"body":8,"config":693,"content":695,"description":8,"extension":28,"meta":699,"navigation":11,"path":700,"seo":701,"stem":702,"__hash__":703},"blogAuthors/en-us/blog/authors/bill-staples.yml",{"template":694},"BlogAuthor",{"name":18,"config":696,"role":698},{"headshot":697},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750434080/glxv59lh9qftpdbsb4ph.png","CEO",{},"/en-us/blog/authors/bill-staples",{},"en-us/blog/authors/bill-staples","K-ulWVa7KOFAxgiGSmeiIjz3KeQyIkhm95lIRX_r6Zc",[705,718,730],{"content":706,"config":716},{"title":707,"description":708,"authors":709,"heroImage":711,"date":712,"body":713,"category":9,"tags":714},"10 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.",[710],"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.",[24,715],"DevOps platform",{"featured":33,"template":12,"slug":717},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":719,"config":728},{"title":720,"description":721,"heroImage":722,"authors":723,"date":725,"body":726,"category":9,"tags":727},"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",[724],"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/)",[24,27],{"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",[24,737,108,555],"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":33,"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":243},"/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",[23,558],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":243},"/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",[27],"Are you trading speed for security?",{"text":774,"config":775},"Get your security maturity score",{"href":776,"dataGaName":754,"dataGaLocation":243},"/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":50,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":789},{"href":54,"dataGaName":55,"dataGaLocation":787},1772652072580]