[{"data":1,"prerenderedAt":792},["ShallowReactive",2],{"/en-us/blog/customizing-gitlab-duo-chat-rules-prompts-workflows":3,"navigation-en-us":39,"banner-en-us":439,"footer-en-us":449,"blog-post-authors-en-us-Itzik Gan Baruch":691,"blog-related-posts-en-us-customizing-gitlab-duo-chat-rules-prompts-workflows":705,"assessment-promotions-en-us":745,"next-steps-en-us":782},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":38},"blogPosts/en-us/blog/customizing-gitlab-duo-chat-rules-prompts-workflows.yml","Customizing Gitlab Duo Chat Rules Prompts Workflows",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"customizing-gitlab-duo-chat-rules-prompts-workflows",false,"BlogPost",{"tags":15,"category":9,"date":20,"heroImage":21,"authors":22,"description":24,"title":25,"body":26},[16,17,18,19],"AI/ML","product","features","tutorial","2026-01-14","https://res.cloudinary.com/about-gitlab-com/image/upload/v1765809212/noh0mdfn9o94ry9ykura.png",[23],"Itzik Gan Baruch","Learn how to customize GitLab Duo Agent Platform to match your team's workflow. Configure chat rules, craft system prompts, set up agent tools, and tailor flows for your specific needs.","Customizing GitLab Duo Agent Platform: Chat rules, prompts, and workflows","*Welcome to Part 8 of our eight-part guide, [Getting started with GitLab Duo Agent Platform](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/), where you'll master building and deploying AI agents and workflows within your development lifecycle. Follow tutorials that take you from your first interaction to production-ready automation workflows with full customization.*\n\n**In this article:**\n* [Introduction to customization](#introduction-to-customization)\n* [Customize agent behavior](#part-1-customize-agent-behavior)\n* [Extend capabilities with MCP](#part-2-extend-capabilities-with-mcp)\n* [Create custom agents and flows](#part-3-create-custom-agents-and-flows)\n\n> 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today! \n## Introduction to customization\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) delivers powerful capabilities right away, and you can unlock even greater value by tailoring it to your team's specific needs. GitLab offers flexible customization options across multiple levels:\n\n- **User-level**: Personal preferences that apply across all projects (custom rules, AGENTS.md, MCP config)\n- **Workspace-level**: Project-specific configurations (custom rules, AGENTS.md, MCP config)\n- **Project-level**: Custom agents and flows you create and manage within a specific project\n\n## Part 1: Customize agent behavior\n\n### Custom rules\n\n[Custom rules](https://docs.gitlab.com/user/gitlab_duo/customize_duo/custom_rules/) provide instructions for agents and flows, ensuring consistent behavior across your team without requiring repetition. For example, in development style guides or how to execute tests.\n\nNavigate to **IDE workspace or user configuration directory**.\n\n### User-level custom rules\n\nUser-level rules apply to all your projects and workspaces.\n\nFor detailed instructions on creating user-level custom rules, see the [GitLab documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/custom_rules/#create-user-level-custom-rules).\n**Create the file** `~/.gitlab/duo/chat-rules.md` in your home directory.\n**Example rules:**\n\n```markdown - Always use TypeScript for new code, never JavaScript\n- Include JSDoc comments for all functions\n- Use single quotes for strings\n- Follow the existing code style in the repository\n- Write concise explanations, avoid lengthy descriptions\n- Suggest tests for any code changes\n- Use async/await instead of promises\n```\n\n### Workspace-level custom rules\n\nWorkspace rules apply only to a specific project. They override user-level rules for that project.\n\n**Create the file** `.gitlab/duo/chat-rules.md` in your project root.\n\n**Example rules for a Vue.js project:**\n\n```markdown\n- Use Vue 3 Composition API with `\u003Cscript setup>`\n- Always include TypeScript types for props\n- Use scoped styles with SCSS\n- Follow the Slippers UI design system\n- Keep components under 300 lines\n- Use kebab-case for component names\n- Include accessibility attributes (aria-*, role)\n```\n\n### Best practices for custom rules\n\n- **Be specific**: \"Use single quotes\" is better than \"follow style guide.\"\n- **Prioritize**: List most important rules first.\n- **Team-focused**: Rules should reflect your team's standards, not personal preferences.\n- **Actionable**: Rules should be clear enough for an AI agent to follow.\n- **Maintainable**: Update rules when your standards change.\n- **Avoid conflicts**: Don't contradict your codebase's actual style.\n\n**Tip:** Use Code Owners to manage who approves changes to `.gitlab/duo/chat-rules.md`.\n\nFor a detailed use case tutorial for custom rules, see the [Custom rules in GitLab Duo Agentic Chat for greater developer efficiency deep-dive blog post](https://about.gitlab.com/blog/custom-rules-duo-agentic-chat-deep-dive/).\n\n## AGENTS.md for customizing agent behavior\n\n[AGENTS.md](https://agents.md/) is an industry-standard file for customizing agent behavior. It allows you to define how agents should behave in your chat conversations, foundational flows, and custom flows without modifying the agents themselves.\n\n**Difference to custom rules:** AGENTS.md are consumed by all agents and flows (foundational and custom). It also follows an industry standard that other AI tools can use, for example, Claude Code as [external agent](https://docs.gitlab.com/user/duo_agent_platform/agents/external/). Use AGENTS.md when you want your instructions to apply across multiple contexts.\n\n**User-level** (applies to all your projects and workspaces):\n- macOS/Linux: `~/.gitlab/duo/AGENTS.md`\n\n- Windows: `%APPDATA%\\GitLab\\duo\\AGENTS.md`\n\n**Workspace-level** (applies to a specific project):\n- Create `AGENTS.md` in your project root.\n\n**Subdirectory-level** (applies to specific directories in monorepos):\n- Create `AGENTS.md` in subdirectories for context-specific instructions.\n\n**How it works:**\n- User-level AGENTS.md applies globally across all projects.\n- Workspace-level AGENTS.md applies to a specific project.\n- Subdirectory-level AGENTS.md files provide context for specific parts of your codebase.\n- Agents and flows combines instructions from all applicable levels.\n- Newly added or updated AGENTS.md instructions require triggering new flows, or starting a new chat with a (custom) agent.\n\n### What AGENTS.md controls\n\n- Agent personality and tone\n- Project-specific instructions\n- Coding standards and conventions\n- Tool usage preferences\n- Output formatting requirements\n- Repository structure and organization\n\n### Example AGENTS.md\n\n```markdown\n# Agent Customization for Our Project\n## General Guidelines\n- Always prioritize code quality over speed\n- Follow our project's architecture patterns\n- Reference existing code examples when suggesting changes\n- Ask for clarification if requirements are ambiguous\n## Code Style\n- Use TypeScript for all new code\n- Follow ESLint configuration in the project\n- Include unit tests for all new functions\n- Use descriptive variable names (no single letters except loops)\n## Documentation\n- Add JSDoc comments to all public functions\n- Update README.md if adding new features\n- Include examples in code comments\n## Security\n- Never suggest hardcoding secrets or API keys\n- Always validate user input\n- Use parameterized queries for database operations\n- Flag potential security issues immediately\n```\n\n### Best practices for AGENTS.md\n\n- **Be specific**: Include concrete examples from your project.\n- **Keep it concise**: Focus on what's unique to your project.\n- **Version control**: Commit to your repository and track changes.\n- **Team alignment**: Discuss with your team before finalizing.\n- **Update regularly**: Refine as your project evolves.\n- **Document repository structure**: Help agents understand your codebase organization.\n\n### Requirements\n- GitLab 18.8 or later\n- For VS Code: GitLab Workflow extension 6.60 or later\n- For JetBrains: GitLab plugin 3.26.0 or later\n- For flows: Update flow configuration to access the `user_rule` context\n\n[Learn more about AGENTS.md](https://docs.gitlab.com/user/gitlab_duo/customize_duo/agents_md/).\n\n### Custom review instructions\n\n[Custom review instructions](https://docs.gitlab.com/user/gitlab_duo/customize_duo/review_instructions/) provide specific guidelines for the [Code Review foundational flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/). The instructions ensure consistent code review standards, and can be tailored to specific file types in your project.\n\n\n**Create the file** `.gitlab/duo/mr-review-instructions.yaml` in your project root.\n\n**Example review instructions:**\n\n```yaml\ninstructions:\n  - name: Ruby Style Guide\n    fileFilters:\n      - \"*.rb\"           # Ruby files in the root directory\n      - \"lib/**/*.rb\"    # Ruby files in lib and its subdirectories\n      - \"!spec/**/*.rb\"  # Exclude test files\n    instructions: |\n      1. Ensure all methods have proper documentation\n      2. Follow Ruby style guide conventions\n      3. Prefer symbols over strings for hash keys\n\n  - name: TypeScript Source Files\n    fileFilters:\n      - \"**/*.ts\"        # TypeScript files in any directory\n      - \"!**/*.test.ts\"  # Exclude test files\n    instructions: |\n      1. Ensure proper TypeScript types (avoid 'any')\n      2. Follow naming conventions\n      3. Document complex functions\n\n```\n\n**Best practices for custom review instructions:**\n- **Be specific and actionable**: Clear, numbered instructions work best.\n- **Use glob patterns**: Target specific file types with `fileFilters`.\n- **Focus on important standards**: Prioritize the most critical review points.\n- **Explain the \"why\"**: Help reviewers understand the reasoning.\n- **Test patterns**: Ensure glob patterns match the intended files.\n\n**Tip:** Use Code Owners to protect changes to `.gitlab/duo/mr-review-instructions.yaml`.\n\nFor detailed setup instructions and examples, see the [Custom Review Instructions documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/review_instructions/).\n\n## Part 2: Extend capabilities with MCP\n\nModel Context Protocol (MCP) enables agents to access external systems like Jira, Slack, AWS, and more. This section covers MCP configuration for extending agent capabilities.\n\n> **🎯 Try it now:** [Interactive demo of MCP](https://gitlab.navattic.com/mcp) - Explore how to use Model Context Protocol.\n\n### MCP configuration for external integrations\n\nModel Context Protocol (MCP) enables agents to access external systems like Jira, Slack, AWS, and more.\n\n**Scope:** User-level (applies to all workspaces) or Workspace-level (project-specific, overrides user config)\n\n**Create user configuration:**\n- **macOS/Linux**: `~/.gitlab/duo/mcp.json`\n- **Windows**: `C:\\Users\\\u003Cusername>\\AppData\\Roaming\\GitLab\\duo\\mcp.json`\n- **VS Code**: Run command `GitLab MCP: Open User Settings (JSON)`\n\n**Create workspace configuration:**\n- **Create file**: `.gitlab/duo/mcp.json` in your project root\n\n**Best practices:**\n- **Security first**: Use MCP servers that require OAuth and not plain-text password tokens.\n- **Minimal scope**: Only enable MCP servers you actually use and trust.\n- **Test locally**: Verify MCP connections and authorization work before sharing across teams.\n- **Document integrations**: Explain what each MCP server provides.\n- **Version control**: Store configuration in `.gitlab/duo/mcp.json` with Code Owners' approval.\n\nFor detailed setup instructions and configuration examples, see [Part 7: Model Context Protocol (MCP) Integration](/blog/duo-agent-platform-with-mcp/).\n\n## Part 3: Create custom agents and flows\n\nCustom agents and flows allow you to automate your team's specific workflows. Before diving into customization, it's helpful to understand what they are and how they work. Here are parts of the [Getting started with GitLab Duo Agent Platform guide](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/) that can help.\n- **[Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/)** — Learn about foundational, custom, and external agents, and when to use each type.\n- **[Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/)** — Discover how flows orchestrate multiple agents to solve complex problems.\n- **[Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/)** — Learn how to discover, create, and share agents and flows across your organization.\nOnce you understand the basics, this section provides an overview of customization options with links to detailed guides.\n\n### System prompts for custom agents\n\nSystem prompts define an agent's personality, expertise, and behavior. A well-crafted prompt makes agents more effective and aligned with your team's needs.\n\n**What are system prompts?** System prompts are instructions that tell an agent how to behave, what expertise it has, and how to respond to requests. They're the foundation of custom agent behavior.\n\n**Key elements of a strong system prompt:**\n- **Role definition**: What the agent is and what it does\n- **Expertise areas**: Specific domains or technologies\n- **Behavior guidelines**: How it should interact and respond\n- **Output format**: Structure of responses\n- **Constraints**: What it should avoid\n\n**Best practices:**\n- **Be detailed**: More specific prompts produce better results.\n- **Use examples**: Show the agent what good output looks like.\n- **Define scope**: Clearly state what the agent should and shouldn't do.\n- **Test iteratively**: Refine prompts based on agent behavior.\n- **Version control**: Track prompt changes in your repository.\n\nFor detailed guidance on crafting system prompts and creating custom agents, see [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/).\n\n### Custom agents and flows\n\nThere is a lot to learn, and for easier reading, the tutorials are split:\n\n**Custom agents:**\n- Learn how to create agents with custom system prompts, configure tools, and manage permissions.\n- See [Part 3: Understanding agents - Custom agents section](/blog/understanding-agents-foundational-custom-external/#custom-agents).\n\n**Custom flows:**\n- Learn how to create multi-step workflows, configure components, and set up event-driven automation.\n- See [Part 4: Understanding flows — Custom flows section](/blog/understanding-flows-multi-agent-workflows/#custom-flows).\n\n**Agent tools:**\n- Tools determine what actions agents can perform. Configure tools based on your agent's purpose and security requirements.\n- See [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/) for tool configuration details.\n\n\n## Quick reference: When to use customizations\n\n| Tool | Best For | Location |\n|------|----------|----------|\n| **Custom Rules** | Guiding Chat responses in IDE (tone, style, behavior) | `~/.gitlab/duo/chat-rules.md` (user) or `.gitlab/duo/chat-rules.md` (workspace) |\n| **AGENTS.md** | Enforcing standards across chat, flows, and other AI tools | `~/.gitlab/duo/AGENTS.md` (user) or `AGENTS.md` (workspace root) |\n| **Custom Review Instructions** | Guiding code review standards for specific file types | `.gitlab/duo/mr-review-instructions.yaml` (workspace only) |\n| **System Prompts** | Customizing individual agent behavior | AI Catalog when creating an agent |\n| **MCP Configuration** | Connecting agents to external tools | `~/.gitlab/duo/mcp.json` (user) or `.gitlab/duo/mcp.json` (workspace) |\n| **Custom Agents** | Creating specialized agents for team-specific tasks | Automate → Agents or AI Catalog |\n| **Custom Flows** | Orchestrating multiple agents in workflows | Automate → Flows or AI Catalog |\n\n## What's next?\n\nCongratulations! You've completed the entire GitLab Duo Agent Platform series. You now understand:\n- How to use agents and flows across the entire SDLC, tailored to your use cases\n- How to discover and share solutions in the AI Catalog\n- How to monitor and manage your AI workflows\n- How to extend capabilities with MCP integrations\n- How to customize every aspect of GitLab Duo Agent Platform for your team\n\n**[Return to complete series overview](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)** to review all parts and explore specific topics in depth.\n\n## Resources\n\n- [Custom Rules documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/custom_rules/)\n- [AGENTS.md documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/agents_md/)\n- [Custom Review Instructions documentation](https://docs.gitlab.com/user/gitlab_duo/customize_duo/review_instructions/)\n- [Custom Agents documentation](https://docs.gitlab.com/user/duo_agent_platform/agents/custom.html)\n- [Custom Flows 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AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[711],"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.",[16,716],"DevOps platform",{"featured":12,"template":13,"slug":718},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":720,"config":730},{"title":721,"description":722,"heroImage":723,"authors":724,"date":726,"body":727,"category":9,"tags":728},"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",[725],"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/)",[16,729],"security",{"featured":29,"template":13,"slug":731},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":733,"config":743},{"title":734,"description":735,"authors":736,"category":9,"tags":738,"date":740,"heroImage":741,"body":742},"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.",[737],"Regnard Raquedan",[16,739,108,556],"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":12,"template":13,"slug":744},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":746},[747,760,771],{"id":748,"categories":749,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[17,559],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":774,"text":751,"button":775,"image":779},"security-modernization",[729],"Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":756,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":783,"blurb":784,"button":785,"secondaryButton":790},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":786,"config":787},"Get your free trial",{"href":788,"dataGaName":50,"dataGaLocation":789},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":495,"config":791},{"href":54,"dataGaName":55,"dataGaLocation":789},1772652082396]