[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/gitlab-18-4-ai-native-development-with-automation-and-insight":3,"navigation-en-us":38,"banner-en-us":438,"footer-en-us":448,"blog-post-authors-en-us-Bill Staples":689,"blog-related-posts-en-us-gitlab-18-4-ai-native-development-with-automation-and-insight":703,"assessment-promotions-en-us":743,"next-steps-en-us":780},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":11,"meta":28,"navigation":11,"path":29,"publishedDate":20,"seo":30,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/gitlab-18-4-ai-native-development-with-automation-and-insight.yml","Gitlab 18 4 Ai Native Development With Automation And Insight",[7],"bill-staples",null,"ai-ml",{"featured":11,"template":12,"slug":13},true,"BlogPost","gitlab-18-4-ai-native-development-with-automation-and-insight",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab 18.4: AI-native development with automation and insight","With GitLab 18.4, teams create custom agents, unlock Knowledge Graph context, and auto-fix pipelines so developers stay focused and in flow.",[18],"Bill Staples","https://res.cloudinary.com/about-gitlab-com/image/upload/v1758541195/kig7sww6jyvxzmkmimbv.png","2025-09-23","As a developer, you know modern development isn't just about writing code — it's about managing change across the entire software development lifecycle. \n\nIn [GitLab 18.3](https://about.gitlab.com/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering/), we laid the groundwork for true human-AI collaboration. We introduced leading AI tools such as Claude Code, Codex CLI, Amazon Q CLI, and Gemini CLI as native integrations to GitLab, delivered our first preview of the GitLab Model Context Protocol ([MCP](https://about.gitlab.com/topics/ai/model-context-protocol/)) server in partnership with Cursor, and shipped two new flows, Issue to MR and Convert CI File for Jenkins Flows, to help teams tackle every day problems. \n\nWith [GitLab 18.4](https://about.gitlab.com/releases/2025/09/18/gitlab-18-4-released/) we are expanding your ability to build and share custom agents, collaborate more effectively through Agentic Chat, navigate codebases with the Knowledge Graph, and keep pipelines green with the Fix Failed Pipelines Flow, while also delivering greater security and governance over your AI usage. \n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1120293274?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=\"18.4 Release video placeholder\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n\n> Have questions on the latest features in the GitLab 18.4 release? [Join us for The Developer Show](https://www.linkedin.com/events/q-a-code-exploringgitlab18-4and7373772262312906753/theater/) live on LinkedIn on Sept. 23 at 10:00 am PT, or on-demand shortly after! \n\n## Build your experience\n\n*Start your day by pulling from the AI Catalog — a library of specialized agents that surface priorities, automate routine work, and keep you focused on building.*\n\n### AI Catalog as your library of specialized agents (Experimental)\n\nWith GitLab 18.4, we're introducing the GitLab Duo AI Catalog — a central library where teams can create, share, and collaborate with custom-built agents across their organization. Every team has ‘their way' of doing things. So creating a custom agent is just like training a fellow engineer on the ‘right way' to do things in your organization.  \n\nFor example, a custom Product Planning agent can file bugs in the specific format, following your labeling standards, or a Technical Writer agent can draft concise documentation following your conventions, or a Security agent can make sure your security and compliance standards are met for every MR. Instead of functioning as disconnected tools, these agents become part of the natural stream of work inside GitLab — helping accelerate tasks without disrupting established processes.\n\n**Note:** This capability is currently only available on GitLab.com as an Experiment. We plan to deliver this to our self-managed customers next month in the 18.5 release. \n\n## Stay in your flow\n\n*GitLab Duo Agentic Chat makes collaboration with agents seamless.*\n\n### Smarter Agentic Chat to streamline collaboration with agents (Beta)\n\nAs the centerpiece of GitLab Duo Agent Platform (Beta), [Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/) gives you a seamless way to collaborate with AI agents. The latest update to Agentic Chat with GitLab 18.4 improves the chat experience and expands how sessions are managed and surfaced.\n\n* **Chat with custom agent**\n\n  Let's start with your newly-created custom agent. Once designed, you can immediately put that agent to work through Agentic Chat. For example, you could ask your new agent “give me a list of assignments” to get started with your priorities for the day. Additionally, you now have the ability to start fresh conversations with new agents and resume previous conversations with agents without losing context. \n\n* [**User model selection**](https://docs.gitlab.com/user/gitlab_duo/model_selection/#select-a-model-to-use-in-gitlab-duo-agentic-chat)\n\n  With previous releases, you're able to select models at a namespace level, but in 18.4 you can now choose models at the user level for a given chat session. This empowers you to make the call on which LLM is right for the job, or experiment with different LLMs to see which delivers the best answer for your task. \n\n* **Improved formatting and visual design**\n\n  We hope you love the new visual design for GitLab Duo Agentic Chat, including improved handling of tool call approvals to ensure your experience is more enjoyable.  \n\n* **Agent Sessions available through Agentic Chat**\n\n  Sessions are expanding to become a core part of the Agentic Chat experience. Any agent run or flow now appears in the Sessions overview available from Agentic Chat. Within each session, you'll see rich details like job logs, user information, and tool metadata — providing critical transparency into how agents are working on your behalf.  \n  \n  \n  **Note:** Sessions in Agentic Chat is available on GitLab.com only, this enhancement is planned for self-managed customers next month in the 18.5 update.\n\n## Unlock your codebase\n\n*With agents, context is king. With Knowledge Graph, you can give your agents more context so they can reason faster and give you better results.* \n\n### Introducing the GitLab Knowledge Graph (Beta)\n\nThe [GitLab Knowledge Graph](https://gitlab-org.gitlab.io/rust/knowledge-graph/) in 18.4 transforms how developers and agents understand and navigate complex codebases. The Knowledge Graph provides a connected map of your entire project, linking files, routes, and references across the software development lifecycle. By leveraging tools such as go-to-definition, codebase search, and reference tracking through in-chat queries, developers gain the ability to ask precise questions like “show me all route files” or “what else does this change impact?”\nThis deeper context helps teams move faster and with more confidence — whether it's onboarding new contributors, conducting deep research across a project, or exploring how a modification impacts dependent code. The more of your ecosystem that lives in GitLab, the more powerful the Knowledge Graph becomes, giving both humans and AI agents the foundation to build with accuracy, speed, and full project awareness. In future releases, we'll be stitching all of your GitLab data into the Knowledge Graph, including plans, MRs, security vulnerabilities, and more.\nThis release of the Knowledge Graph focuses on local code indexing, where the `gkg` CLI turns your codebase into a live, embeddable graph database for RAG. You can install it with a simple one-line script, parse local repositories, and connect via MCP to query your workspace. \nOur vision for the Knowledge Graph project is twofold: building a vibrant community edition that developers can run locally today, which will serve as the foundation for a future, fully-integrated Knowledge Graph Service within GitLab.com and self-managed instances.\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1121017374?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=\"18.4 Knowledge Graph Demo\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Automate your pipeline maintenance\n\n*Fix pipeline failures faster and stay in the flow with the Fixed Failed Pipelines Flow.*\n\n### Fix Failed Pipelines Flow with business awareness\n\nKeeping pipelines green is critical for your development velocity, but traditional approaches focus only on technical troubleshooting without considering the business impact. The **Fix Failed Pipelines Flow** addresses this challenge by combining technical analysis with strategic context. For example, it can automatically prioritize fixing a failed deployment pipeline for a customer-facing service ahead of a nightly test job, or flag build issues in a high-priority release branch differently than experimental feature branches.\n\n* **Business-aware failure detection** monitors pipeline executions while understanding the importance of different workflows and deployment targets.  \n* **Contextual root cause analysis** analyzes failure logs alongside business requirements, recent changes, and cross-project dependencies to identify underlying causes.  \n* **Strategic fix prioritization** generates appropriate fixes while considering business impact, deadlines, and resource allocation priorities.  \n* **Workflow-integrated resolution** automatically creates merge requests with fixes that maintain proper review processes while providing business context for prioritization decisions.\n\nThis flow keeps pipelines green while maintaining strategic alignment, enabling automated fixes to support business objectives rather than just resolving technical issues in isolation.\n\n## Customize your AI environment\n\n*Automation only works if you trust the models behind it. That's why 18.4 delivers governance features like model selection and GitLab-managed keys.*\n\n### GitLab Duo model selection to optimize feature performance\n\n[Model selection](https://docs.gitlab.com/user/gitlab_duo/model_selection/) is now generally available, giving you direct control over which large language models ([LLMs](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) power GitLab Duo. You and your team can select the models of your choice, apply them across the organization or tailor them per feature. You can set defaults to ensure consistency across namespaces and tools, with governance, compliance, and security requirements in mind. \n\nFor customers using GitLab Duo Self-Hosted, newly added support for GPT OSS and GPT-5 provides additional flexibility for AI-powered development workflows.\n\n**Note:** GitLab Duo Self-Hosted is not available to GitLab.com customers, and GPT models are not supported on GitLab.com. \n\n## Protect your sensitive context\n\n*Alongside governance comes data protection, giving you fine-grained control over what AI can and can't see.*\n\n### GitLab Duo Context Exclusion for granular data protection\n\nIt's no surprise — you need granular control over what information AI agents can access. **GitLab Duo Context Exclusion** in 18.4 provides project-level settings that let teams exclude specific files or file paths from AI access. Capabilities include:\n\n* **File-specific exclusions** to help protect sensitive files such as password configurations, secrets, and proprietary algorithms.  \n* **Path-based rules** to create exclusion patterns based on directory structures or file naming conventions.  \n* **Flexible configuration** to apply exclusions at the project level while maintaining development workflow efficiency.  \n* **Audit visibility** to track what content is excluded to support compliance with data governance policies.\n\nGitLab Duo Context Exclusion helps you protect sensitive data while you accelerate development with agentic AI.\n\n## Extend your AI capabilities with new MCP tools\n\n*Expanded MCP tools extend those capabilities even further, connecting your GitLab environment with a broader ecosystem of intelligent agents.*\n\n### New tools for GitLab MCP server\n\nExpanding on the initial MCP server introduced in [18.3](https://about.gitlab.com/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering/), GitLab 18.4 adds more MCP tools — capabilities that define how MCP clients interact with GitLab. These new tools extend integration possibilities, enabling both first-party and third-party AI agents to take on richer tasks such as accessing project data, performing code operations, or searching across repositories, all while respecting existing security and permissions models. For a full list of MCP tools, including the new additions in 18.4, visit our [MCP server documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/).\n\n## Experience the future of intelligent software development\n\nWith [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/), engineers can begin to move from working on one issue at a time in single threaded fashion, to multi-threaded collaboration with asynchronous agents that act like teammates to get work done, faster. We are bringing to market this unique vision with our customer's preferences for independence and choice: run in your preferred cloud environments using the LLMs and AI tools that work best for you, within the security and compliance guardrails you set. \n\nAs an integral part of this innovation, GitLab 18.4 is more than a software upgrade — it's about making the day-to-day experience of developers smoother, smarter, and more secure. From reusable agents to business-aware pipeline fixes, every feature is designed to keep teams in flow while balancing speed, security, and control. For a deeper look at how these capabilities come together in practice, check out our walkthrough video.\n\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1120288083?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=\"A day in the life with GitLab Duo Agent Platform\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\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](https://about.gitlab.com/dedicated/) customers coming next month. \n\n> **Enable beta and experimental features in GitLab Duo Agent Platform today** and 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## 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\n*This blog post contains \"forward-looking statements\" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in these statements are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to differ materially. Further information on these risks and other factors is included under the caption \"Risk Factors\" in our filings with the SEC. 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prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[709],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[23,714],"DevOps platform",{"featured":32,"template":12,"slug":716},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":718,"config":728},{"title":719,"description":720,"heroImage":721,"authors":722,"date":724,"body":725,"category":9,"tags":726},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[723],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[23,727],"security",{"featured":11,"template":12,"slug":729},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":731,"config":741},{"title":732,"description":733,"authors":734,"category":9,"tags":736,"date":738,"heroImage":739,"body":740},"Secure and fast deployments to Google Agent Engine with GitLab","Follow this step-by-step guide to build an AI agent with Google's Agent Development Kit and deploy to Agent Engine using GitLab.",[735],"Regnard Raquedan",[23,737,107,554],"google","2026-02-26","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","In this tutorial, you'll learn how to deploy an AI agent built with Google's Agent Development Kit ([ADK](https://google.github.io/adk-docs/)) to [Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using GitLab's native Google Cloud integration and CI/CD pipelines. We'll cover IAM configuration, pipeline setup, and testing your deployed agent.\n\n## What is Agent Engine and why does it matter?\n\nAgent Engine is Google Cloud's managed runtime specifically designed for AI agents. Think of it as the production home for your agents — where they live, run, and scale without you having to manage the underlying infrastructure. Agent Engine handles infrastructure, scaling, session management, and memory storage so you can focus on building your agent — not managing servers. It also integrates natively with Google Cloud's logging, monitoring, and IAM.\n\n## Why use GitLab to deploy to Agent Engine?\n\nAI agent deployment is typically difficult to configure correctly. Security considerations, CI/CD orchestration, and cloud permissions create friction that slows down development cycles.\n\nGitLab streamlines this entire process while enhancing security:\n\n- **Built-in security scanning** — Every deployment is automatically scanned for vulnerabilities without additional configuration.\n- **Native Google Cloud integration** — Workload Identity Federation eliminates the need for service account keys.\n- **Simplified CI/CD** — GitLab's templates handle complex deployment logic.\n\n## Prerequisites\n\nBefore you begin, ensure you have:\n\n- A Google Cloud project with the following APIs enabled:\n  - Cloud Storage API\n  - Vertex AI API\n- A GitLab project for your source code and CI/CD pipeline\n- A Google Cloud Storage bucket for staging deployments\n- Google Cloud IAM integration configured in GitLab (see Step 1)\n\nHere are the steps to follow.\n\n## 1. Configure IAM integration\n\nThe foundation of secure deployment is proper IAM configuration between GitLab and Google Cloud using Workload Identity Federation.\n\nIn your GitLab project:\n\n1. Navigate to **Settings > Integrations**.\n2. Locate the **Google Cloud IAM** integration.\n3. Provide the following information:\n   - **Project ID**: Your Google Cloud project ID\n   - **Project Number**: Found in your Google Cloud console\n   - **Workload Identity Pool ID**: A unique identifier for your identity pool\n   - **Provider ID**: A unique identifier for your identity provider\n\nGitLab generates a script for you. Copy and run this script in Google Cloud Shell to establish the Workload Identity Federation between platforms.\n\n**Important:** Add these additional roles to your service principal for Agent Engine deployment:\n\n- `roles/aiplatform.user`\n- `roles/storage.objectAdmin`\n\nYou can add these roles using gcloud commands:\n\n```bash\nGCP_PROJECT_ID=\"\u003Cyour-project-id>\"\nGCP_PROJECT_NUMBER=\"\u003Cyour-project-number>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003Cyour-pool-id>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/aiplatform.user'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/storage.objectAdmin'\n```\n\n## 2. Create the CI/CD pipeline\n\nNow for the core of the deployment — the CI/CD pipeline. Create a `.gitlab-ci.yml` file in your project root:\n\n```yaml\nstages:\n  - test\n  - deploy\n\ncache:\n  paths:\n    - .cache/pip\n  key: ${CI_COMMIT_REF_SLUG}\n\nvariables:\n  GCP_PROJECT_ID: \"\u003Cyour-project-id>\"\n  GCP_REGION: \"us-central1\"\n  STORAGE_BUCKET: \"\u003Cyour-staging-bucket>\"\n  AGENT_NAME: \"Canada City Advisor\"\n  AGENT_ENTRY: \"canada_city_advisor\"\n\nimage: google/cloud-sdk:slim\n\n# Security scanning templates\ninclude:\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n\ndeploy-agent:\n  stage: deploy\n  identity: google_cloud\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n  before_script:\n    - gcloud config set core/disable_usage_reporting true\n    - gcloud config set component_manager/disable_update_check true\n    - pip install -q --no-cache-dir --upgrade pip google-genai google-cloud-aiplatform -r requirements.txt --break-system-packages\n  script:\n    - gcloud config set project $GCP_PROJECT_ID\n    - adk deploy agent_engine \n        --project=$GCP_PROJECT_ID \n        --region=$GCP_REGION \n        --staging_bucket=gs://$STORAGE_BUCKET \n        --display_name=\"$AGENT_NAME\" \n        $AGENT_ENTRY\n```\n\nThe pipeline consists of two stages:\n\n**Test stage** — GitLab's security scanners run automatically. The included templates provide dependency scanning, static application security testing (SAST), and secret detection without additional configuration.\n\n**Deploy stage** — Uses the ADK CLI to deploy your agent directly to Agent Engine. The staging bucket temporarily holds your application workload before Agent Engine picks it up for deployment.\n\n### Key configuration notes\n\n- The `identity: google_cloud` directive enables keyless authentication via Workload Identity Federation.\n- Security scanners are included as templates, meaning they run by default with no setup required.\n- The `adk deploy agent_engine` command handles all the complexity of packaging and deploying your agent.\n- Pipeline caching speeds up subsequent deployments by preserving pip dependencies.\n\n## 3. Deploy and verify\n\nWith your pipeline configured:\n\n1. Commit your agent code and `.gitlab-ci.yml` to GitLab.\n2. Navigate to **Build > Pipelines** to monitor execution.\n3. Watch the test stage complete security scans.\n4. Observe the deploy stage push your agent to Agent Engine.\n\nOnce the pipeline succeeds, verify your deployment in the Google Cloud Console:\n\n1. Navigate to **Vertex AI > Agent Engine**.\n2. Locate your deployed agent.\n3. Note the **resource name** — you'll need this for testing.\n\n## 4. Test your deployed agent\n\nTest your agent using a curl command. You'll need three pieces of information:\n\n- **Agent ID**: From the Agent Engine console (the resource name's numeric identifier)\n- **Project ID**: Your Google Cloud project\n- **Location**: The region where you deployed (e.g., `us-central1`)\n\n```bash\nPROJECT_ID=\"\u003Cyour-project-id>\"\nLOCATION=\"us-central1\"\nAGENT_ID=\"\u003Cyour-agent-id>\"\nTOKEN=$(gcloud auth print-access-token)\n\ncurl -X POST \\\n  -H \"Authorization: Bearer $TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  \"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines/${AGENT_ID}:streamQuery\" \\\n  -d '{\n    \"input\": {\n      \"message\": \"I make $85,000 per year and I prefer cities with mild winters and a vibrant cultural scene. I also want to be near the coast if possible. What Canadian cities would you recommend?\",\n      \"user_id\": \"demo-user\"\n    }\n  }' | jq -r '.content.parts[0].text'\n```\n\nIf everything is configured correctly, your agent will respond with personalized city recommendations based on the budget and lifestyle preferences provided.\n\n## Security benefits of this approach\n\nThis deployment pattern provides several security advantages:\n\n- **No long-lived credentials**: Workload Identity Federation eliminates service account keys entirely.\n- **Automated vulnerability scanning**: Every deployment is scanned before reaching production.\n- **Complete audit trail**: GitLab maintains full visibility of who deployed what and when.\n- **Principle of least privilege**: Fine-grained IAM roles limit access to only what's needed.\n\n## Summary\n\nDeploying AI agents to production doesn't have to be complex. By combining GitLab's DevSecOps platform with Google Cloud's Agent Engine, you get:\n\n- A managed runtime that handles scaling and infrastructure\n- Built-in security scanning without additional tooling\n- Keyless authentication via native cloud integration\n- A streamlined deployment process that fits modern AI development workflows\n\nWatch the full demo:\n\n\n\u003Cfigure class=\"video_container\"> \u003Ciframe src=\"https://www.youtube.com/embed/sxVFa2Mk-x4?si=Oi3cUjhgd7FT2yEd\" frameborder=\"0\" allowfullscreen=\"true\" title=\"Deploy AI Agents to Agent Engine with GitLab\"> \u003C/iframe> \u003C/figure>\n\n> Ready to try it yourself? Use this tutorial's [complete code example](https://gitlab.com/gitlab-partners-public/google-cloud/demos/agent-engine-demo) to get started now. Not a GitLab customer yet? Explore the DevSecOps platform with [a free trial](https://about.gitlab.com/free-trial/).\n",{"featured":32,"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":242},"/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",[24,557],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":770,"categories":771,"header":772,"text":749,"button":773,"image":777},"security-modernization",[727],"Are you trading speed for security?",{"text":774,"config":775},"Get your security maturity score",{"href":776,"dataGaName":754,"dataGaLocation":242},"/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":49,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":494,"config":789},{"href":53,"dataGaName":54,"dataGaLocation":787},1772652074116]