[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/introduction-to-gitlab-duo-agent-platform":3,"navigation-en-us":37,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-Itzik Gan Baruch":689,"blog-related-posts-en-us-introduction-to-gitlab-duo-agent-platform":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":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":33,"tagSlugs":34,"__hash__":36},"blogPosts/en-us/blog/introduction-to-gitlab-duo-agent-platform.yml","Introduction To Gitlab Duo Agent Platform",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"introduction-to-gitlab-duo-agent-platform",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 the basics of GitLab Duo Agent Platform and complete your first agent interaction.","Introduction to GitLab Duo Agent Platform","*Welcome to Part 1 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\nGitLab Duo Agent Platform represents a fundamental shift in how developers interact with AI during the software development lifecycle. Moving beyond code into full SDLC context, GitLab Duo Agent Platform enables multiple specialized AI agents to work alongside your team, handling complex tasks asynchronously while you focus on innovation and problem-solving.\n\nGitLab Duo Agent Platform transforms traditional linear development workflows into dynamic, multi-agent collaboration systems.\n\n## What is GitLab Duo Agent Platform?\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) is an AI orchestration layer that enables:\n\n- Asynchronous collaboration between developers and specialized AI agents\n- Full SDLC context across code, issues, epics, merge requests, CI/CD pipelines, wikis, analytics, and security scans\n- Multi-agent flows where many agents collaborate in parallel on complex tasks\n- Intelligent automation that understands your organization's standards, practices, and compliance requirements\n\nThink of it as adding AI team members who can take on entire workflows, from understanding requirements to creating merge requests, while you maintain full visibility and control.\n\n> 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today!\n## Platform architecture\n\nGitLab Duo Agent Platform consists of several interconnected components working together to provide comprehensive AI assistance. The diagram below shows the *user interaction methods* with GitLab Duo Agent Platform. It illustrates the four ways users can engage with agents:\n\n\n\n\n![GitLab Duo Agent Platform architecture diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373441/k0ktrcnyuqbq3unbcvyp.png \"GitLab Duo Agent Platform architecture diagram\")\n\n### How teams interact with GitLab Duo Agent Platform\n\n**Four ways to use agents**\n\n1. **GitLab Duo Agentic Chat** — Open the chat panel in the GitLab UI or your IDE for interactive conversations with foundational and custom agents. Select from available AI models and get real-time help.\n\n2. **Trigger Custom Flows** — Mention flows in issue or merge request comments, or assign reviewers to automatically trigger Custom Flows. These run asynchronously via runner execution.\n\n3. **Trigger Foundational Flows** — Built and maintained by GitLab, including **[Developer](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/developer/)**, **[Code Review](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/)**, **[Fix CI/CD Pipeline](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/fix_pipeline/)**, **[Convert Jenkins to GitLab CI/CD](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/)**, and **[Software Development Flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/)**.\n4. **Trigger External Agents** — Assign or mention external AI agents (like Claude Code or OpenAI Codex) in issue or merge request comments to automatically trigger them. These run asynchronously via runner execution.\n\n**Where to manage and discover**\n\n- **AI Catalog** — Browse, create, and share agents and flows across your organization. Discover agents and flows created by GitLab and your team, then add them to your projects. You can also create and publish your own custom agents and flows for others to use.\n\n- **Automate Capabilities** — Your central hub for managing everything. View and manage your agents, configure and monitor flows, review all activity in sessions (including pipeline status), and set up triggers for event-based automation.\n\nLet's explore each component briefly (we'll dive deeper in subsequent posts):\n\n**GitLab Duo Agentic Chat**\n\nYour primary interface for interacting with agents. Available as a persistent panel in the GitLab UI and in your IDE. Learn more in [Part 2: Getting Started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/).\n\n![GitLab Duo Agentic Chat](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618251/gdkojstbdsruen4bo5fw.png \"GitLab Duo Agentic Chat panel in the web UI\")\n\n![GitLab Duo Agentic Chat IDE](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373438/gjojavrvjhhvglgkvxmw.png \"GitLab Duo Agentic Chat panel in VS Code\")\n\n**Agents**\n\nAgents are specialized AI-powered assistants designed to handle specific tasks throughout your development workflow. Think of them as team members with unique expertise and capabilities.\n\n| Type | Description | Where Used | Setup Required |\n|------|-------------|------------|----------------|\n| **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/)** | Maintained by GitLab for common development workflows (Security Analyst, Planner, GitLab Duo), available by default in the chat of any project| GitLab Duo Chat | No |\n| **[Custom](https://docs.gitlab.com/user/duo_agent_platform/agents/custom/)** | Created by you for team-specific needs with custom prompts and tools | GitLab Duo Chat | Yes |\n| **[External](https://docs.gitlab.com/user/duo_agent_platform/agents/external/)** | External AI providers (Claude, OpenAI) triggered via mentions or assignments | @mentions, assignments | Optional |\n### About external agents\nExternal agents run in the background on GitLab platform compute when triggered by mentions (e.g., `@ai-codex`) or assignments in issues and merge requests. Unlike foundational and custom agents that use synchronous feedback loops, external agents execute asynchronously, enabling powerful automation with specialized AI providers.\n### What makes agents powerful\n- **Specialized prompts**: Each agent has a unique system prompt that defines its expertise, behavior, and communication style.\n- **Access to tools**: Agents can read files, access issues/MRs/epics, search code, analyze CI/CD job logs and vulnerability reports, and more based on their configuration.\n- **Project context:** Access to issues, merge requests, code, CI/CD pipelines, and security vulnerabilities.\n\nLearn more in [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/). Discover how to create custom agents, integrate external AI providers, and configure agent prompts and tools for your team's specific needs.\n\n**Flows**\n\nFlows are multi-step workflows that combine multiple actions to solve complex problems. Unlike agents that respond to questions, flows execute complete workflows autonomously via runner execution.\n\n| Type | Description | Where Triggered | Setup Required |\n|------|-------------|-----------------|----------------|\n| **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/)** | Maintained by GitLab for common development workflows (Developer, Fix Pipeline, Convert Jenkins to GitLab CI/CD, Software Development) | You invoke using dedicated UI action buttons, or using the IDE extension Flows tab| No |\n| **[Custom](https://docs.gitlab.com/user/duo_agent_platform/flows/custom/)** | User-defined workflows you create, tailored to your needs | Mentions in issues/MRs, assignment | Yes |\n### What makes flows powerful\n- **Multi-step execution**: Combine multiple operations into a single workflow\n- **Asynchronous processing**: Run in background while you continue working\n- **Full pipeline access**: Execute via runner execution with complete project context\n- **Event-driven**: Automatically triggered by GitLab events\n\nLearn more in [Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/), including multi-agent workflows.\n\n## Agents vs. flows: What's the difference?\n\nUnderstanding when to use an agent vs. a flow is key to working effectively with GitLab Duo Agent Platform.\n\n| Aspect | Agents (Interactive in Chat) | Flows (Automated on Platform) |\n|--------|------------------------------|-------------------------------|\n| **Purpose** | Interactive work, quick iterations, conversational guidance | Complex multi-step tasks, background automation, event-driven workflows |\n| **Where** | GitLab Duo Chat (Web UI, IDEs) | Issues, Merge Requests, UI action buttons |\n| **How** | Real-time conversation with ability to take actions | Triggered by events or button clicks |\n| **Execution** | Interactive, runs immediately in chat context | Asynchronous via runner execution |\n| **Example** | \"Refactor this function\" (agent modifies code), \"Create tests\" (agent generates test file) | \"Generate MR for issue #123\" (flow creates branch, commits, opens MR) |\n### Quick decision guide\n- Working interactively or want instant feedback? → Use chat\n- Need background automation, MR review, or complex multi-file tasks? → Use flow\n### Key insight\n\nBoth agents and flows can take actions and create code. The main difference is how they interact and run: Agents communicate interactively in your chat interface, while flows run asynchronously in the background on platform compute.\n\n#### AI Catalog\n\nA centralized library where you can browse, discover, create, and share agents and flows across your organization, detailed in [Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/).\n\n![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618250/sdtnio4rrbmwlh4iia4l.png \"AI Catalog\")\n\n#### Automate capabilities\n\nYour hub for managing agent and flow workflows:\n\n- **Agents**: View and manage agents in your project, detailed in [Part 3](/blog/understanding-agents-foundational-custom-external/).\n- **Flows**: View, create, and manage flows in your project, detailed in [Part 4](/blog/understanding-flows-multi-agent-workflows/).\n- **Sessions**: Agent activity logs\n- **Triggers**: Event-based automation management for flows in your project\n\n## Understanding sessions\n\nEvery agent and flow execution creates a session that logs agentic activities. Sessions provide full transparency into what happened, including agent reasoning, execution details, tool calling, outputs, and the complete decision trail.\n\n![Sessions Monitoring](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618251/jpqv5frskvgzz6fnmvjl.png \"Sessions overview showing execution status and progress\")\n\n\n\n\nTo view sessions: Navigate to your project > **Automate** > **Sessions**. From there, you can access the pipeline console to see detailed execution logs.\n\n## Model selection\n\nOne of the powerful features of GitLab Duo Agent Platform is the ability to choose which AI model powers your conversation.\n\n**Available in:** GitLab 18.4 and later\n\n**How to select:**\n\n1. Open GitLab Duo Agentic Chat.\n2. Look for the model dropdown.\n3. Click to see available models.\n4. Select the model best suited for your task.\n\n**Note:** Model selection is currently available in the Web UI only. IDE integration uses the default model selected for your group.\n\n## Your first agent interaction\n\nLet's walk through a simple first interaction with GitLab Duo Agentic Chat:\n\n### Example 1: Understanding your project (Agent)\n\n**Scenario:** You've just joined a project and need to understand its structure and architecture.\n\n**Steps:**\n\n1. Open GitLab Duo Chat panel (click Duo icon in top-right).\n2. Ensure Agentic mode (Beta) is toggled on.\n3. Select the Duo Agent (default).\n4. Type: \"Give me an overview of this project's architecture.\"\n5. Press **Enter**.\n\n**What happens:**\n\nThe agent:\n- Analyzes your repository structure\n- Reviews your README, code organization, and documentation\n- Provides a comprehensive overview with key components\n\nYou can ask follow-up questions for clarification.\n\n![Chat showing architecture overview](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373438/rvdxbupzh8bupt674kyc.png \"Chat showing Architecture Overview\")\n\n\n\n\n### Example 2: Generating a merge request (Flow)\n\n**Scenario:** You have an issue that needs to be resolved with code changes.\n\n**Steps:**\n\n1. Open the issue in GitLab.\n2. Click **Generate MR with Duo** button.\n3. An agent session starts.\n4. Within a few minutes, an MR is created with:\n\n   - Code changes across multiple files\n\n   - A descriptive commit message\n\n   - An explanation of changes in MR description\n\n\n**What happens:**\n\nThe Developer Flow:\n- Analyzes the issue\n- Understands repository structure, design patterns, and SDLC context\n- Makes appropriate code changes\n- Opens a ready-to-review MR\n\n\n\n\n![Issue with Generate MR with Duo button](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373443/gq57mpgyftvru1fyqh4o.png \"Issue with Generate MR with Duo button\")\n\n\n\n\n## Common questions\n\n**Q: Are my conversations with agents private?**\n\nA: Yes. Conversations follow GitLab's standard privacy and security models. [Learn more.](https://docs.gitlab.com/user/gitlab_duo/data_usage)\n\n**Q: Can I use GitLab Duo Agent Platform with self-hosted models?**\n\nA: Yes, starting with GitLab 18.8, it requires additional setup. See [GitLab documentation](https://docs.gitlab.com/administration/gitlab_duo_self_hosted/configure_duo_features/#configure-access-to-the-gitlab-duo-agent-platform).\n\n## What's next?\n\nNow that you understand the basics of GitLab Duo Agent Platform, you're ready to dive deeper into each component:\n\n- **[Part 2: Getting started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/)** — Master the persistent chat panel, learn model selection strategies, understand agent switching, and use chat effectively across Web UI and all supported IDEs.\n\n- **[Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/)** — Explore foundational agents built by GitLab, create custom agents with specialized prompts for your team's workflows, and integrate external CLI agents from providers like Claude Code and OpenAI Codex.\n\n- **[Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/)** — Discover how flows orchestrate multiple agents to solve complex problems, create custom YAML-defined workflows, and leverage external AI providers for automated pipeline execution.\n\n- **[Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/)** — Browse the centralized repository to discover agents and flows created by GitLab and the community, add them to your projects, and publish your own solutions for others to use.\n\n- **[Part 6: Monitor, manage, and automate AI workflows](/blog/monitor-manage-automate-ai-workflows/)** — Monitor all agent and flow activity through sessions, set up event-driven triggers to automate workflows, and manage your entire GitLab Duo Agent Platform ecosystem from one central location.\n\n- **[Part 7: Model Context Protocol integration](/blog/duo-agent-platform-with-mcp/)** — Extend GitLab Duo's capabilities by connecting to external tools like Jira, Slack, and AWS through the open MCP standard, and enable external AI tools to access your GitLab data.\n- **[Part 8: Customizing GitLab Duo Agent Platform](/blog/customizing-gitlab-duo-chat-rules-prompts-workflows/)** - Configure custom chat rules, create system prompts for agents, set up agent tools, integrate external systems with MCP, and customize flows for your team's specific needs.\n\n## Resources\n\n- [GitLab Duo Agent Platform documentation](https://docs.gitlab.com/user/duo_agent_platform/)\n- [GitLab Duo Agent Platform site](https://about.gitlab.com/gitlab-duo-agent-platform/)\n- [GitLab Community Forum](https://forum.gitlab.com/)\n\n---\n\n**Next:** [Part 2: Getting started with GitLab Duo Agentic 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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.",[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.",[16,714],"DevOps platform",{"featured":12,"template":13,"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/)",[16,727],"security",{"featured":29,"template":13,"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",[16,737,106,554],"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":12,"template":13,"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":241},"/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",[17,557],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":241},"/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":241},"/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":48,"dataGaLocation":787},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":493,"config":789},{"href":52,"dataGaName":53,"dataGaLocation":787},1772652075217]