[{"data":1,"prerenderedAt":789},["ShallowReactive",2],{"/en-us/blog/gitlab-18-5-intelligence-that-moves-software-development-forward":3,"navigation-en-us":36,"banner-en-us":436,"footer-en-us":446,"blog-post-authors-en-us-Bill Staples":687,"blog-related-posts-en-us-gitlab-18-5-intelligence-that-moves-software-development-forward":701,"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-18-5-intelligence-that-moves-software-development-forward.yml","Gitlab 18 5 Intelligence That Moves Software Development Forward",[7],"bill-staples",null,"ai-ml",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-18-5-intelligence-that-moves-software-development-forward",{"heroImage":15,"title":16,"description":17,"authors":18,"date":20,"body":21,"category":9,"tags":22},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1760970883/asrc2c2hejqp5o1tan4c.png","GitLab 18.5: Intelligence that moves software development forward","GitLab 18.5 delivers new specialized agents, security insights that cut through the noise, and a reimagined interface that keeps your AI teammate always in view.",[19],"Bill Staples","2025-10-21","Software development teams are drowning in noise. Thousands of vulnerabilities flood security dashboards, but only a fraction pose real risk. Developers context-switch between planning backlogs, triaging security findings, reviewing code, and responding to CI/CD failures — losing hours to manual work. [GitLab 18.5](https://about.gitlab.com/releases/2025/10/16/gitlab-18-5-released/) calms this chaos.\n\nAt the heart of this release is a valuable improvement in overall usability of GitLab and how AI integrates into your user experience. A new panel-based UI makes it easier to see data in context, and allows GitLab Duo Chat to be persistently visible across the platform, wherever it is needed. Purpose-built agents tackle vulnerability triage and backlog management, and popular AI tools integrate with agentic workflows even more seamlessly than before. We’ve also extended our market-leading security capabilities to help you better identify exploitable vulnerabilities versus theoretical ones, distinguish active credentials from expired ones, and scan only changed code to keep developers in flow.\n\n## What’s new in 18.5\n\n18.5 represents our biggest release so far this year — watch our introduction to the release, and read more details below. \n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1128975773?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_18.5 Release_101925_MP_v2\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\u003Cp>\u003C/p>\n\n### Modern user experience with quick access to GitLab Duo everywhere\n\nGitLab 18.5 delivers a modernized user experience with a more intuitive interface driven by a new panel-based layout.\n\nWith panels, key information appears side by side so that you can work contextually, without losing your place. For example, when you click on an issue in the issues list, its details automatically open in a side panel. You can also launch the GitLab Duo panel on the right, bringing Duo wherever you are in GitLab. This lets you ask contextual questions or give instructions, right alongside your work.\n\nSeveral usability improvements make navigation easier. The global search box now appears at the top center for improved accessibility. Global navigation elements, including Issues, Merge Requests, To-Dos, and your avatar have moved to the top right. Additionally, the left sidebar is now collapsible and expandable, giving you more control over your workspace.\n\nTeams using experimental and GitLab Duo beta features will be the first to receive the new interface, followed by all GitLab.com users who will be able to turn this experience on using the toggle located under your user icon. To learn more about this feature, reference our documentation [here](https://docs.gitlab.com/user/interface_redesign/#turn-new-navigation-on-or-off). Please share your feedback or report any issues [here](https://gitlab.com/gitlab-org/gitlab/-/issues/577554), you're helping us shape a better GitLab! \n\n### Updates to GitLab Duo Agent Platform\n\n**Security Analyst Agent: Transform manual vulnerability triage into intelligent automation**\n\nGitLab Duo [Security Analyst Agent](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/security_analyst_agent/) automates vulnerability management workflows through AI-powered analysis, helping transform hours of manual triage into intelligent automation. Building on the Vulnerability Management Tools available through GitLab Duo Agentic Chat, Security Analyst Agent orchestrates multiple tools, applying security policies, and creating custom flows for recurring workflows automatically.\n\nSecurity teams can access enriched vulnerability data, including CVE details, static reachability analysis, and code flow information, while executing operations like dismissing false positives, confirming threats, adjusting severity levels, and creating linked issues for remediation — all through conversational AI. The agent reduces repetitive clicking through vulnerability dashboards and replaces custom scripts with simple natural language commands.\n\nFor example, when a security scan reveals dozens of vulnerabilities, simply prompt: \"Dismiss vulnerabilities with reachable=FALSE and create issues for critical findings.\" Security Analyst Agent analyzes reachability data, applies security policies, and completes bulk operations in moments — helping decrease work that would otherwise take hours. \n\nWhile individual Vulnerability Management Tools can be accessed directly through Agentic Chat for specific tasks, Security Analyst Agent orchestrates these tools intelligently and automates complex multi-step workflows. Note that Vulnerability Management Tools are available through Agentic Chat on GitLab Self-managed and GitLab.com instances, and Security Analyst Agent is available on GitLab.com only for 18.5, while availability in Self-managed and Dedicated environments will come with our next release.\nWatch this demo:\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1128975984?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=\"18.5 Security Demo\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\u003Cp>\u003C/p>\n\n**GitLab Duo Planner: Turn backlog chaos into strategic clarity**\n\nManaging complex software delivery requires constant context-switching between planning tasks. [GitLab Duo Planner](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/planner/) addresses the real-world planning challenges we see teams face every day. Duo Planner acts as your teammate with awareness of your project context, including how you manage issues, epics, and merge requests. Unlike generic AI assistants, it's purpose-built with deep knowledge of GitLab's planning workflows coupled with Agile and prioritization frameworks to help you balance effort, risk, and strategic alignment.\n\nGitLab Duo Planner can turn vague ideas into structured planning hierarchies, identify stale backlog items, and draft executive updates. For example, when refining your backlog with hundreds of issues accumulated over months, simply prompt: \"Identify stale backlog items and suggest priorities.\" Within seconds, you'll receive a structured summary showing issues without recent activity, items missing key details, duplicate work, and recommended priorities based on labels and milestones, complete with actionable recommendations. \n\nFor teams managing complex roadmaps, the Planner aims to eliminate hours of manual analysis and context-switching, helping Product Managers and engineering leads make faster, more informed decisions. As of 18.5, GitLab Duo Planner is currently “read-only,” meaning that it can analyze, plan, and suggest, but cannot yet take direct action to modify anything. Please see our [documentation](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/planner/) for more information. \n\n**Extensible Agent Catalog: Popular AI tools as native GitLab agents**\n\nGitLab 18.5 introduces popular AI agents directly into the [AI Catalog](https://docs.gitlab.com/user/duo_agent_platform/ai_catalog/), making external tools like Claude, OpenAI Codex, Google Gemini CLI, Amazon Q Developer, and OpenCode available as native GitLab agents. Users can now discover, configure, and deploy these agents through the same unified catalog interface used for GitLab's built-in agents, with automatic syncing of foundational agents across organization catalogs. \n\nThis eliminates the complexity of manual agent setup by providing a point-and-click catalog experience while maintaining enterprise-grade security through GitLab's authentication and audit systems. GitLab Duo Enterprise subscriptions now include built-in usage of Claude and Codex within GitLab, allowing you to use your existing GitLab subscription for these tools without requiring separate API keys or additional billing setup. Other agents may still require separate subscriptions and configuration while we finalize our integration plans.\n\n**Self-hosted GitLab Duo Agent Platform (Beta): Address data sovereignty requirements without sacrificing AI power**\n\nGitLab 18.5 moves GitLab Duo Agent Platform's self-hosted capabilities from experimental to beta, enabling organizations to execute AI agents and flows entirely within their own infrastructure — critical for regulated industries and data sovereignty requirements. The beta release includes improved timeout configurations and AI Gateway settings, allowing teams to use AI agents for code reviews, bug fixes, and feature implementations, while providing enterprise-grade security for sensitive code.\n\n## Smarter, faster security: Prioritize real risks and keep developers in the flow\n\nGitLab 18.5 introduces new application security capabilities that help teams focus on exploitable risk, reduce noise, and strengthen software supply chain security. These updates continue our commitment to building security directly into the development process — delivering precision, speed, and insight without disrupting developer flow.\n\n**Static Reachability Analysis**\n\nWith over [37,000 new CVEs](https://www.cvedetails.com/) issued this year, security teams face an overwhelming volume of vulnerabilities and struggle to understand which ones are truly exploitable. Static Reachability Analysis, now in limited availability, brings library-level precision by helping to identify whether vulnerable code is actually invoked in your application, not just present in dependencies. \n\nPaired with our [recently released](https://docs.gitlab.com/user/application_security/vulnerabilities/risk_assessment_data/) Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerability (KEV) data, security teams can more effectively accelerate vulnerability triage and prioritize real risks to help strengthen overall supply chain security. In 18.5, we’re adding support for Java, alongside existing support for Python, JavaScript, and TypeScript. \n\n**Secret Validity Checks**\n\nJust as Static Reachability Analysis helps teams prioritize exploitable vulnerabilities from open source dependencies, Secret Validity Checks bring the same insight to exposed secrets — currently available in beta on GitLab.com and GitLab Self-Managed. For GitLab-issued security tokens, instead of manually verifying whether a leaked credential or API key is active, GitLab automatically distinguishes active secrets from expired ones directly in the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/). This helps enable security and development teams to focus remediation efforts on genuine risks. Support for AWS- and GCP-issued secrets is planned for future releases. \n\n**Custom rules for Advanced SAST**\n\nAdvanced SAST runs on rules informed by our in-house security research team, designed to maximize accuracy out of the box. However, some teams required additional flexibility to tune the SAST engine for their specific organization. With Custom Rules for Advanced SAST, AppSec teams can define atomic, pattern-based detection logic to help capture security issues specific to their organization — like flagging banned function calls — while still using GitLab’s curated ruleset as the baseline. Customizations are managed through simple TOML files, just like other SAST ruleset configurations. While these rules will not support taint analysis, they do give organizations greater flexibility in achieving accurate SAST results. \n\n**Advanced SAST C and C++ language support** \n\nWe’re expanding our language coverage for Advanced SAST to include C and C++, which are widely used languages in embedded systems software development. To enable scanning, projects must generate a compilation database that captures compiler commands and includes paths used during builds. This works to ensure the scanner can accurately parse and analyze source files, delivering precise, context-aware results that help security teams identify real vulnerabilities in the development process. The implementation requirements for C and C++ require specific configurations, which can be found in our [documentation](https://docs.gitlab.com/user/application_security/sast/cpp_advanced_sast/). Advanced SAST C and C++ support are currently available in beta. \n\n**Diff-based SAST scanning** \n\nTraditional SAST scans re-analyze entire codebases with every commit, slowing pipelines and disrupting developer flow. The developer experience is a critical consideration that can make or break the adoption of application security testing. Diff-based SAST scanning aims to speed up scan times by focusing only on the code changed in a merge request, reducing redundant analysis and surfacing relevant results tied to the developer’s work. By aligning scans with actual code changes, GitLab delivers faster, more focused feedback that helps keep developers in flow while maintaining strong security coverage.\n\n## Simplify API configurations\n\nAPI-driven workflows offer power and flexibility, but they can also create unnecessary complexity for tasks that teams need to perform regularly. The new Maven Virtual Registry interface brings a UI layer to these operations.\n\n### Maven Virtual Registry interface\n\nThe new web-based interface for managing Maven Virtual Registries turns complex API configurations into visual simplicity, providing a more intuitive experience for package administrators and platform engineers.\n\nPreviously, teams configured and maintained virtual registries only through API calls, which made routine maintenance time-consuming and required specialized platform knowledge. The new interface removes that barrier, helping to make everyday tasks faster and easier.\n\nWith this update, you can now:\n\n* Create virtual registries to simplify dependency configuration  \n* Create and order upstreams to help improve performance and compliance  \n* Browse and clear stale cache entries directly in the UI\n\nThis visual experience helps reduce operational overhead and provides development teams with clearer insight into how dependencies are resolved, enabling them to make better decisions about build performance and security policies.\n\nWatch a demo:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n\u003Ciframe src=\"https://www.youtube.com/embed/CiOZJPhAvaI?si=cYaoR_OIgqFKbyM2\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n\u003Cp>\u003C/p>\n\nWe invite enterprise customers to join the [Maven Virtual Registry Beta program](https://gitlab.com/gitlab-org/gitlab/-/issues/543045) and share feedback to help shape the final release. \n\n## AI that adapts to your workflow\n\nThis release represents more than new capabilities — it's about choice and control. Watch the walkthrough video here:\n\n\u003Cp>\u003C/p>\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1128992281?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=\"18.5-tech-demo\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\u003Cp>\u003C/p>\n\nGitLab Premium and Ultimate users can start using these capabilities today on [GitLab.com](https://GitLab.com) and self-managed environments, with availability for GitLab Dedicated customers planned for next month. \n\nGitLab Duo Agent Platform is currently in **beta** — enable beta and experimental features to experience how full-context AI can transform the way your teams build software. New to GitLab? [Start your free trial](https://about.gitlab.com/free-trial/devsecops/) and see why the future of development is AI-powered, secure, and orchestrated through the world’s most comprehensive DevSecOps platform.\n\n***Note:** Platform capabilities that are in beta are available as part of the GitLab Beta program. They are free to use during the beta period, and when generally available, they will be made available with a paid add-on option for GitLab Duo Agent Platform.*\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). With Managed Maintenance, your team stays 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*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. We do not undertake any obligation to update or revise these statements after the date of this blog post, except as required by law.*",[23,24,25],"features","product","DevSecOps platform","yml",{},"/en-us/blog/gitlab-18-5-intelligence-that-moves-software-development-forward",{"config":30,"title":16,"description":17},{"noIndex":31},false,"en-us/blog/gitlab-18-5-intelligence-that-moves-software-development-forward",[23,24,34],"devsecops-platform","XzZv1NX8GCUneo6xZmDcC-J_zFWx-9KWxhPTSS7nUdc",{"data":37},{"logo":38,"freeTrial":43,"sales":48,"login":53,"items":58,"search":366,"minimal":397,"duo":416,"pricingDeployment":426},{"config":39},{"href":40,"dataGaName":41,"dataGaLocation":42},"/","gitlab logo","header",{"text":44,"config":45},"Get free trial",{"href":46,"dataGaName":47,"dataGaLocation":42},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":49,"config":50},"Talk to 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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.",[707],"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.",[712,713],"AI/ML","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/)",[712,726],"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",[712,736,105,552],"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",[24,555],"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},1772652086778]