[{"data":1,"prerenderedAt":790},["ShallowReactive",2],{"/en-us/blog/understanding-flows-multi-agent-workflows":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-understanding-flows-multi-agent-workflows":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/understanding-flows-multi-agent-workflows.yml","Understanding Flows Multi Agent Workflows",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"understanding-flows-multi-agent-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","Deep dive into GitLab Duo Agent Platform flows. Learn about foundational flows, create custom user-defined workflows, and learn flow orchestration patterns.","Understanding flows: Multi-agent workflows","\n*Welcome to Part 4 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\n- [What are flows and how do they work?](#introduction-to-flows)\n- [Foundational flows provided by GitLab](#foundational-flows)\n- [Creating custom flows](#how-to-create-custom-flows)\n- [Flow execution and orchestration](#flow-execution)\n- [Real-world examples and use cases](#example-custom-flow-yaml)\n\n> 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today!\n## Introduction to flows\nFlows are combinations of one or more agents collaborating together. They orchestrate multi-step workflows to solve complex problems, and are executed on the GitLab platform compute.\n\n**Key characteristics of flows:**\n\n- **Multi-agent orchestration**: Combine multiple specialized agents\n- **Built-in**: Run on platform compute, no extra environment necessary\n- **Event-driven**: Triggered by mention, assignment, or assign as reviewer\n- **Asynchronous**: Run in background while you continue working\n- **Complete workflows**: Handle end-to-end tasks from analysis to implementation\n\nThink of flows as autonomous workflows that can gather context, make decisions, execute changes, and deliver results, all while you focus on other work.\n\n## Flows vs. agents: Understanding the difference\nAgents work with you interactively. Flows work for you autonomously.\n\n| Aspect | Agents | Flows |\n|--------|--------|-------|\n| **Interaction** | Interactive chat | Autonomous execution |\n| **When to use** | Questions, guidance, and performing tasks interactively | Autonomous multi-step workflows |\n| **User involvement** | Active conversation | Trigger and review results |\n| **Execution time** | Real-time responses | Background processing |\n| **Complexity** | Single-agent tasks | Multi-agent orchestration |\n\n## Flow types overview\n\n| Type | Interface | Maintainer | Use Case |\n|------|-------------|---------------|----------|\n| **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/)** | UI actions, IDE interface | GitLab | Software Development, Developer in issues, Fix CI/CD Pipeline, Convert to GitLab CI/CD, Code Review, SAST false positive detection |\n| **[Custom](https://docs.gitlab.com/user/duo_agent_platform/flows/custom/)** | Mention, assign, assign reviewer | You | Examples: Larger migration/modernization, release automation, dependency update management |\n\n## Foundational flows\nFoundational flows are production-ready workflows created and maintained by GitLab. They're accessible through dedicated UI controls or IDE interfaces.\n### Currently available foundational flows\n\n| Flow | Where Available | How to Access | Best For |\n|------|-----------------|---------------|----------|\n| [**Software Development**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/software_development.html) | IDEs (VS Code, JetBrains, Visual Studio) | Flows tab in IDE | Feature implementation, complex refactoring, multi-file changes |\n| [**Developer**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/developer.html) | GitLab Web UI | \"Generate MR with Duo\" button on issues | Well-defined features, bug fixes with clear steps |\n| [**Fix CI/CD Pipeline**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/fix_pipeline.html) | GitLab Web UI | Failed pipeline interface | Pipeline debugging, CI/CD configuration issues |\n| [**Convert to GitLab CI/CD**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci.html) | GitLab Web UI | \"Convert to GitLab CI/CD\" button on Jenkinsfile | Jenkins to GitLab CI/CD migration |\n| [**Code Review**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review.html) | GitLab Web UI | Assign as reviewer on MR | Automated code review with AI-native analysis and feedback |\n| [**SAST false positive detection**](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/sast_false_positive_detection.html) | GitLab Web UI | Security scan results | Automatically identify and filter false positives in SAST findings |\n\n## Custom flows\n\nCustom flows are YAML-defined workflows you create for your team's specific needs. They run in GitLab Runner and can be triggered by GitLab events.\n\n> **🎯 Try it now:** [Interactive demo of Custom Flows](https://gitlab.navattic.com/custom-flows) — Explore how to create and configure Custom Flows.\n\n### Why create custom flows?\n\nCustom flows automate repetitive multi-step tasks that are specific to your team's workflow. Unlike foundational flows that serve general purposes, custom flows are tailored to your organization's processes, tools, and requirements.\n\n**Common use cases:**\n\n- **Automated code review**: Multi-stage review process (security scan → quality check → style validation)\n- **Compliance checking**: Verify regulatory requirements, license compliance, or security policies on each MR\n- **Documentation generation**: Auto-update API docs, README files, or changelogs based on code changes\n- **Dependency management**: Weekly security scans, automated updates, and vulnerability reports\n- **Custom testing**: Specialized test suites for your tech stack or integration tests\n\n### Real-world example\n\nA fintech company creates a compliance flow that runs on every merge request. When triggered by `@compliance-flow`, the flow executes the following steps:\n1. **Security agent** scans code for PCI-DSS violations and checks for exposed sensitive data.\n2. **Code review agent** verifies that changes follow secure coding standards and best practices.\n3. **Documentation agent** checks that API changes include updated documentation.\n4. **Summary agent** aggregates findings and posts a compliance report with pass/fail status.\n\nThe entire compliance review happens automatically in 5-10 minutes, providing consistent checks across all merge requests.\n\n### How to trigger custom flows\n\nCustom flows can be triggered in multiple ways:\n\n**1. Via mentions in Issues/MRs:**\nMention the flow in a comment to trigger it. Example for a documentation generation flow:\n```text\n@doc-generator Generate API documentation for this feature\n```\n\n**2. By assigning the flow to an issue or MR:**\nAssign the flow using either:\n- **GitLab UI**: Click the \"Assign\" button on the issue/MR and select the flow\n- **Command**: Use the `/assign` command in a comment. Example:\n```shell\n/assign @doc-generator\n```\n\n**3. By assigning the flow as a reviewer:**\nAssign the flow as a reviewer on a merge request using either:\n- **GitLab UI**: Click the \"Assign reviewer\" button on the merge request and select the flow\n- **Command**: Use the `/assign_reviewer` command in a comment. Example:\n```shell\n/assign_reviewer @doc-reviewer\n```\nAny of these methods automatically triggers the flow to execute and perform its tasks.\n\n### How to create custom flows\n\nCustom flows are created through the GitLab UI at **Automate → Flows → New flow** in your project, or from **Explore → AI Catalog → Flows → New flow**. You define your flow using YAML configuration that specifies components, prompts, routing, and execution flow. The YAML schema allows you to create sophisticated multi-agent workflows with precise control over agent behavior and orchestration.\n\n\n**Key elements of a custom flow:**\n\n- **Components**: Define the agents and steps in your workflow\n- **Prompts**: Configure AI model behavior and instructions\n- **Routers**: Control the flow between components\n- **Toolsets**: Specify which GitLab API tools agents can use\n\n### Example custom flow YAML\n**Background:** This example shows a feature implementation flow for a travel booking platform. When a developer creates an issue with feature requirements, they can trigger this flow to automatically analyze the requirements, review the codebase, implement the solution, and create a merge request, all without manual intervention.\nHere's the YAML configuration:\n\n```yaml\nversion: \"v1\"\nenvironment: ambient\ncomponents:\n  - name: \"implement_feature\"\n    type: AgentComponent\n    prompt_id: \"implementation_prompt\"\n    inputs:\n      - from: \"context:goal\"\n        as: \"user_goal\"\n      - from: \"context:project_id\"\n        as: \"project_id\"\n    toolset:\n      - \"get_issue\"\n      - \"get_repository_file\"\n      - \"list_repository_tree\"\n      - \"find_files\"\n      - \"blob_search\"\n      - \"create_file\"\n      - \"create_commit\"\n      - \"create_merge_request\"\n      - \"create_issue_note\"\n    ui_log_events:\n      - \"on_agent_final_answer\"\n      - \"on_tool_execution_success\"\n      - \"on_tool_execution_failed\"\n\nprompts:\n  - name: \"Cheapflights Feature Implementation\"\n    prompt_id: \"implementation_prompt\"\n    unit_primitives: []\n    prompt_template:\n      system: |\n        You are an expert full-stack developer specializing in travel booking platforms, specifically Cheapflights.\n\n        Your task is to:\n        1. Extract the issue IID from the goal (look for \"Issue IID: XX\")\n        2. Use get_issue with project_id={{project_id}} and issue_iid to retrieve issue details\n        3. Analyze the requirements for the flight search feature\n        4. Review the existing codebase using list_repository_tree, find_files, and get_repository_file\n        5. Design and implement the solution following Cheapflights best practices\n        6. Create all necessary code files using create_file (call multiple times for multiple files)\n        7. Commit the changes using create_commit\n        8. Create a merge request using create_merge_request\n        9. Post a summary comment to the issue using create_issue_note with the MR link\n\n        Cheapflights Domain Expertise:\n        - Flight search and booking systems (Amadeus, Sabre, Skyscanner APIs)\n        - Fare comparison and pricing strategies\n        - Real-time availability and inventory management\n        - Travel industry UX patterns\n        - Performance optimization for high-traffic flight searches\n\n        Code Standards:\n        - Clean, maintainable code (TypeScript/JavaScript/Python/React)\n        - Proper state management for React components\n        - RESTful API endpoints with comprehensive error handling\n        - Mobile-first responsive design\n        - Proper timezone handling (use moment-timezone or date-fns-tz)\n        - WCAG 2.1 accessibility compliance\n\n        Flight-Specific Best Practices:\n        - Accurate fare calculations (base fare + taxes + fees + surcharges)\n        - Flight duration calculations across timezones\n        - Search filter logic (price range, number of stops, airlines, departure/arrival times)\n        - Sort algorithms (best value, fastest, cheapest)\n        - Handle edge cases: date line crossing, daylight saving time, red-eye flights\n        - Currency amounts use proper decimal handling (avoid floating point errors)\n        - Dates use ISO 8601 format\n        - Flight codes follow IATA standards (3-letter airport codes)\n\n        Implementation Requirements:\n        - No TODOs or placeholder comments\n        - All functions must be fully implemented\n        - Include proper TypeScript types or Python type hints\n        - Add JSDoc/docstring comments for all functions\n        - Comprehensive error handling and input validation\n        - Basic unit tests for critical functions\n        - Performance considerations for handling large result sets\n\n        CRITICAL - Your final comment on the issue MUST include:\n        - **Implementation Summary**: Brief description of what was implemented\n        - **Files Created/Modified**: List of all files with descriptions\n        - **Key Features**: Bullet points of main functionality\n        - **Technical Approach**: Brief explanation of architecture/patterns used\n        - **Testing Notes**: How to test the implementation\n        - **Merge Request Link**: Direct link to the created MR (format: [View Merge Request](MR_URL))\n\n        IMPORTANT TOOL USAGE:\n        - Extract the issue IID from the goal first (e.g., \"Issue IID: 12\" means issue_iid=12)\n        - Use get_issue with project_id={{project_id}} and issue_iid=\u003Cextracted_iid>\n        - Create multiple files by calling create_file multiple times (once per file)\n        - Use create_commit to commit all files together with a descriptive commit message\n        - Use create_merge_request to create the MR and capture the MR URL from the response\n        - Use create_issue_note with project_id={{project_id}}, noteable_id=\u003Cissue_iid>, and body=\u003Cyour complete summary with MR link>\n        - Make sure to include the MR link in the comment body so users can easily access it\n\n      user: |\n        Goal: {{user_goal}}\n        Project ID: {{project_id}}\n\n        Please complete the following steps:\n        1. Extract the issue IID and retrieve full issue details\n        2. Analyze the requirements thoroughly\n        3. Review the existing codebase structure and patterns\n        4. Implement the feature with production-ready code\n        5. Create all necessary files (components, APIs, tests, documentation)\n        6. Commit all changes with a clear commit message\n        7. Create a merge request\n        8. Post a detailed summary comment to the issue including the MR link\n\n      placeholder: history\n    params:\n      timeout: 300\n\nrouters:\n  - from: \"implement_feature\"\n    to: \"end\"\n\nflow:\n  entry_point: \"implement_feature\"\n```\n**What this flow does:** This flow orchestrates an AI agent to automatically implement a feature by analyzing issue requirements, reviewing the codebase, writing production-ready code with domain expertise, and creating a merge request with a detailed summary comment.\n\nFor complete documentation and examples, see:\n\n- [Custom Flows documentation](https://docs.gitlab.com/user/duo_agent_platform/flows/custom.html)\n- [Flow Registry Framework (YAML Schema)](https://gitlab.com/gitlab-org/modelops/applied-ml/code-suggestions/ai-assist/-/blob/main/docs/flow_registry/v1.md)\n\n## Flow execution\n\nFlows run on GitLab platform compute. When triggered by an event (mention, assignment, or button click), a session is created and the flow starts to execute.\n### Available environment variables\n\nFlows have access to environment variables that provide context about the trigger and the GitLab object:\n\n- **`AI_FLOW_CONTEXT`** — JSON-serialized context including MR diffs, issue descriptions, comments, and discussion threads\n- **`AI_FLOW_INPUT`** — The user's prompt or comment text that triggered the flow\n- **`AI_FLOW_EVENT`** — The event type that triggered the flow (`mention`, `assign`, `assign_reviewer`)\n\nThese variables allow your flow to understand what triggered it and access the relevant GitLab data to perform its task.\n\n### Multi-agent flows\n\nCustom flows can include multiple agent components that work together sequentially. The flow's YAML configuration defines:\n\n- **Components**: One or more agents (AgentComponent) or deterministic steps\n- **Routers**: Define the flow between components (e.g., from component A to component B to end)\n- **Prompts**: Configure each agent's behavior and model\n\nFor example, a code review flow might have a security agent, then a quality agent, then an approval agent, with routers connecting them in sequence.\n\n### Monitoring flow execution\n\nTo view flows that are running for your project:\n\n1. Navigate to **Automate → Sessions**.\n2. Select any session to view more details.\n3. The **Details** tab shows a link to the CI/CD job logs.\n\nSessions show detailed information including step-by-step progress, tool invocations, reasoning, and decision-making process.\n### When to use flows\n- Complex multi-step tasks\n- Background automation\n- Event-driven workflows\n- Multi-file changes\n- Tasks that take time\n- Automated reviews/checks\n## What's next?\nYou now understand flows, how to create them, and when to use them vs. agents. Next, learn how to discover, create, and share agents and flows across your organization in [Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/). Explore the AI Catalog to browse available flows and agents, add them to your projects, and publish your own agents and flows.\n\n## Resources\n\n- [GitLab Duo Agent Platform Flows](https://docs.gitlab.com/user/duo_agent_platform/flows/)\n- [Foundational Flows documentation](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/)\n- [Custom Flows documentation](https://docs.gitlab.com/user/duo_agent_platform/flows/custom.html)\n- [Flow execution configuration](https://docs.gitlab.com/user/duo_agent_platform/flows/execution.html)\n- [GitLab CI/CD Variables guide](https://docs.gitlab.com/ci/variables/)\n- [Service Accounts](https://docs.gitlab.com/user/profile/service_accounts/)\n\n---\n**Next:** [Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/)\n\n**Previous:** [Part 3: Understanding 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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},1772652083152]