[{"data":1,"prerenderedAt":787},["ShallowReactive",2],{"/en-us/blog/accelerate-learning-with-gitlab-duo-agent-platform":3,"navigation-en-us":33,"banner-en-us":433,"footer-en-us":443,"blog-post-authors-en-us-Halil Coban":685,"blog-related-posts-en-us-accelerate-learning-with-gitlab-duo-agent-platform":699,"assessment-promotions-en-us":740,"next-steps-en-us":777},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":12,"path":28,"publishedDate":20,"seo":29,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/accelerate-learning-with-gitlab-duo-agent-platform.yml","Accelerate Learning With Gitlab Duo Agent Platform",[7],"halil-coban",null,"ai-ml",{"slug":11,"featured":12,"template":13},"accelerate-learning-with-gitlab-duo-agent-platform",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"category":9,"tags":21,"body":25},"Accelerate learning with GitLab Duo Agent Platform","Learn how agentic AI helped generate comprehensive gRPC documentation in minutes, not hours.",[18],"Halil Coban","Blog/Hero%20Images/Workflow_1800x945.png","2025-07-07",[22,23,24],"tutorial","product","security","At GitLab, we continue to expand our AI capabilities so I often find myself learning and working in new codebases. Whether I'm debugging issues, implementing new features, or onboarding to different projects, understanding system architecture quickly is crucial. But let's be honest — manually tracing through complex communication flows, especially gRPC connections, can eat up hours of productive development time.\n\nThis is exactly the type of tedious, yet necessary, work [GitLab Duo Agent Platform](https://about.gitlab.com/blog/gitlab-duo-agent-platform-what-is-next-for-intelligent-devsecops/) is designed to handle. Instead of replacing developers, it amplifies our capabilities by automating routine tasks so we can focus on creative problem solving and strategic technical work.\n\nLet me show you how I used [Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) to generate comprehensive documentation for a Golang project's gRPC communication flow — and how it transformed hours of code analysis into a few minutes of guided interaction.\n\nYou can follow along with this video:\n\n\n\u003Cdiv>\u003Ciframe src=\"https://player.vimeo.com/video/1098569263?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"AI Agent Generates Complete gRPC Documentation in Minutes | GitLab Duo Agent Platform Demo\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## The challenge: Understanding gRPC communication flows\n\nI was working with a project called \"Duo Workflow Executor\" that communicates with a gRPC server. Rather than spending my afternoon manually tracing through the codebase to understand the communication patterns, I decided to let Duo Agent Platform handle the heavy lifting.\n\nMy goal was simple: generate a clear diagram showing how the gRPC communication works, including what payloads are received, what actions are executed, and what responses are sent back.\n\nWorking in VS Code with the GitLab Workflow extension installed, I opened the project and crafted a specific prompt for Duo Agent Platform:\n\n\"Can you prepare a mermaid diagram that shows the gRPC connection between duo-workflow-service and this project. It should show what this project receives in gRPC payload, and what actions it executes based on the payload, and what it sends back. Study internal/services/runner/runner.go, especially the Run method, and write the mermaid output to a grpc.md file.\"\n\nDuo Agent Platform didn't just blindly execute my request — it began intelligently gathering context to create a comprehensive execution plan. The platform automatically:\n\n* Searched through relevant Go files in the project  \n* Read the specific file I mentioned (runner.go)  \n* Identified additional files that would provide necessary context  \n* Analyzed the codebase structure to understand the gRPC implementation\n\nThis contextual awareness is what sets agentic AI tools with great context apart from simple code generation tools. Instead of working in isolation, Duo Agent Platform understands the relationships between different components of your project.\n\n## Collaborative refinement\n\nRather than making assumptions, Duo Agent Platform is designed to keep a human in the loop at critical moments in task execution to deliver better results. In this scenario, the platform paused to ask clarifying questions about the level of detail I wanted. \n\n**Agent:** \"Should I include error handling details in the diagram?\" \n\n**Me:** \"Don't focus on error handling. Include all actions. Focus on gRPC payload.\"\n\nThis back-and-forth refinement is crucial. The agent isn't just executing commands — it's working with me to ensure the deliverable meets my specific requirements.\n\n## Execution and results\n\nWith the clarified requirements, Duo Agent Platform finalized its execution plan and requested confirmation before proceeding. I could have modified the approach at this stage, but the plan was perfectly aligned with what I needed.\n\nThe agent then executed each task in sequence, providing real-time progress updates. Within minutes, it had:\n\n* Analyzed all relevant files across the project  \n* Created comprehensive documentation with a detailed mermaid diagram  \n* Saved everything to the requested grpc.md file  \n* Included valuable context about outgoing payloads and connection management\n\nThe resulting documentation wasn't just a basic diagram — it was thorough, accurate, and immediately useful for understanding the system architecture.\n\n## Reviewing the output\n\nHere's the actual mermaid diagram that Duo Agent Platform created, showing the complete gRPC communication flow:\n\n```mermaid\nsequenceDiagram\nparticipant DWS as duo-workflow-service\nparticipant DWE as duo-workflow-executor\nparticipant Runner as ExecutionRunner\nparticipant ActionExecutor as Action Executor\nparticipant FileSystem as File System/Commands\n\n\nNote over DWS, DWE: gRPC Bidirectional Streaming (ExecuteWorkflow)\n\n%% Connection Establishment\nDWE->>DWS: gRPC Connection with Headers\nNote over DWE, DWS: Headers: authorization (Bearer token)\u003Cbr/>x-gitlab-authentication-type: oidc\u003Cbr/>x-gitlab-realm, x-gitlab-global-user-id\u003Cbr/>x-gitlab-oauth-token, x-gitlab-base-url\u003Cbr/>x-gitlab-instance-id, x-request-id\u003Cbr/>x-gitlab-namespace-id, x-gitlab-project-id\n\n\n%% Workflow Start Request\nDWE->>DWS: ClientEvent{StartWorkflowRequest}\nNote over DWE, DWS: StartWorkflowRequest:\u003Cbr/>- ClientVersion\u003Cbr/>- WorkflowDefinition\u003Cbr/>- Goal\u003Cbr/>- WorkflowID\u003Cbr/>- WorkflowMetadata\u003Cbr/>- ClientCapabilities[]\n\n\n%% Action Processing Loop\nloop Action Processing\nDWS->>DWE: Action Message\nNote over DWS, DWE: Action Types:\u003Cbr/>- Action_RunCommand {program, flags[], arguments[]}\u003Cbr/>- Action_RunGitCommand {command, arguments[], repositoryUrl}\u003Cbr/>- Action_RunReadFile {filepath}\u003Cbr/>- Action_RunWriteFile {filepath, contents}\u003Cbr/>- Action_RunEditFile {filepath, oldString, newString}\u003Cbr/>- Action_RunHTTPRequest {method, path, body}\u003Cbr/>- Action_ListDirectory {directory}\u003Cbr/>- Action_FindFiles {namePattern}\u003Cbr/>- Action_Grep {searchDirectory, pattern, caseInsensitive}\u003Cbr/>- Action_NewCheckpoint {}\u003Cbr/>- Action_RunMCPTool {}\n\n\nDWE->>Runner: Receive Action\nRunner->>Runner: processWorkflowActions()\nRunner->>ActionExecutor: executeAction(ctx, action)\n\nalt Action_RunCommand\n    ActionExecutor->>FileSystem: Execute Shell Command\n    Note over ActionExecutor, FileSystem: Executes: program + flags + arguments\u003Cbr/>in basePath directory\n    FileSystem-->>ActionExecutor: Command Output + Exit Code\n\nelse Action_RunReadFile\n    ActionExecutor->>FileSystem: Read File\n    Note over ActionExecutor, FileSystem: Check gitignore rules\u003Cbr/>Read file contents\n    FileSystem-->>ActionExecutor: File Contents\n\nelse Action_RunWriteFile\n    ActionExecutor->>FileSystem: Write File\n    Note over ActionExecutor, FileSystem: Check gitignore rules\u003Cbr/>Create/overwrite file\n    FileSystem-->>ActionExecutor: Success/Error Message\n\nelse Action_RunEditFile\n    ActionExecutor->>FileSystem: Edit File\n    Note over ActionExecutor, FileSystem: Read → Replace oldString with newString → Write\u003Cbr/>Check gitignore rules\n    FileSystem-->>ActionExecutor: Edit Result Message\n\nelse Action_RunGitCommand\n    ActionExecutor->>FileSystem: Execute Git Command \n    Note over ActionExecutor, FileSystem: Git operations with authentication\u003Cbr/>Uses provided git config\n    FileSystem-->>ActionExecutor: Git Command Output\n\nelse Action_RunHTTPRequest\n    ActionExecutor->>DWS: HTTP Request to GitLab API\n    Note over ActionExecutor, DWS: Method: GET/POST/PUT/DELETE\u003Cbr/>Path: API endpoint\u003Cbr/>Body: Request payload\u003Cbr/>Headers: Authorization\n    DWS-->>ActionExecutor: HTTP Response\n\nelse Action_ListDirectory\n    ActionExecutor->>FileSystem: List Directory Contents\n    Note over ActionExecutor, FileSystem: Respect gitignore rules\n    FileSystem-->>ActionExecutor: Directory Listing\n\nelse Action_FindFiles\n    ActionExecutor->>FileSystem: Find Files by Pattern\n    Note over ActionExecutor, FileSystem: Recursive search with name pattern\u003Cbr/>Respect gitignore rules\n    FileSystem-->>ActionExecutor: File Paths List\n\nelse Action_Grep\n    ActionExecutor->>FileSystem: Search Text Pattern\n    Note over ActionExecutor, FileSystem: Recursive text search\u003Cbr/>Case sensitive/insensitive option\n    FileSystem-->>ActionExecutor: Search Results\n\nelse Action_NewCheckpoint/Action_RunMCPTool\n    ActionExecutor->>ActionExecutor: No-op Action\n    Note over ActionExecutor: Returns empty success result\nend\n\n\nActionExecutor-->>Runner: Action Result (string)\n\nalt Result Size Check\n    Runner->>Runner: Check if result > 4MB\n    Note over Runner: If result exceeds MaxMessageSize (4MB)\u003Cbr/>Replace with error message about size limit\nend\n\n\nRunner->>DWE: ActionResponse\nDWE->>DWS: ClientEvent{ActionResponse}\nNote over DWE, DWS: ActionResponse:\u003Cbr/>- RequestID (matches Action.RequestID)\u003Cbr/>- Response (execution result string)\nend\n\n\n%% Workflow Completion\nDWE->>DWS: CloseSend()\nNote over DWE, DWS: Signal end of workflow execution\n\n\n%% Analytics and Cleanup\nRunner->>Runner: Send Analytics Event (Finish)\nDWE->>DWE: Token Revocation (if enabled)\nDWE->>DWS: Close gRPC Connection\n```\n\nThis diagram reveals several important architectural insights that would have taken considerable time to extract manually:\n\n* **Bidirectional communication:** The workflow executor both initiates requests and responds to service actions. \n* **Rich payload structure:** Each action type has specific parameters and expected responses.  \n* **Multiple integration points:** The executor interacts with local filesystem, Git repositories, and GitLab APIs.  \n* **Comprehensive action set:** Nine different action types handle everything from file operations to HTTP requests.  \n* **Proper lifecycle management:** Clear connection establishment and teardown patterns.\n\nWhat impressed me most was how the agent automatically included the detailed payload structures for each action type. This level of detail transforms the diagram from a high-level overview into actionable documentation that other developers can immediately use.\n\n## Looking ahead\n\nThis demonstration represents just one use case for GitLab Duo Agent Platform. The same contextual understanding and collaborative approach that made documentation generation seamless can be applied to:\n\n* **Code reviews:** Agents can analyze merge requests with full project context  \n* **Testing:** Generate comprehensive test suites based on actual usage patterns  \n* **Debugging:** Trace issues across multiple services and components  \n* **Security scanning:** Identify vulnerabilities with understanding of your specific architecture  \n* **CI/CD optimization:** Improve pipeline performance based on historical data\n\nGitLab Duo Agent Platform will enter public beta soon so [join the wait list today](https://about.gitlab.com/gitlab-duo-agent-platform/).\n\nStay tuned to the [GitLab Blog](https://about.gitlab.com/blog/) and social channels for additional updates. GitLab Duo Agent Platform is evolving rapidly with specialized agents, custom workflows, and community-driven extensions on the roadmap.\n\n## Learn more\n\n- [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n- [GitLab Duo Agent Platform: What’s next for intelligent DevSecOps](https://about.gitlab.com/blog/gitlab-duo-agent-platform-what-is-next-for-intelligent-devsecops/)\n- [What is agentic AI?](https://about.gitlab.com/topics/agentic-ai/)\n- [From vibe coding to agentic AI: A roadmap for technical leaders](https://about.gitlab.com/the-source/ai/from-vibe-coding-to-agentic-ai-a-roadmap-for-technical-leaders/)\n","yml",{},"/en-us/blog/accelerate-learning-with-gitlab-duo-agent-platform",{"title":15,"description":16},"en-us/blog/accelerate-learning-with-gitlab-duo-agent-platform",[22,23,24],"oTxxd_uveICv7yIdaDe6qSv4fTMSIlNjuT2ucfS4L5A",{"data":34},{"logo":35,"freeTrial":40,"sales":45,"login":50,"items":55,"search":363,"minimal":394,"duo":413,"pricingDeployment":423},{"config":36},{"href":37,"dataGaName":38,"dataGaLocation":39},"/","gitlab logo","header",{"text":41,"config":42},"Get free trial",{"href":43,"dataGaName":44,"dataGaLocation":39},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":46,"config":47},"Talk to sales",{"href":48,"dataGaName":49,"dataGaLocation":39},"/sales/","sales",{"text":51,"config":52},"Sign 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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.",[705],"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.",[710,711],"AI/ML","DevOps platform",{"featured":713,"template":13,"slug":714},false,"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":716,"config":725},{"title":717,"description":718,"heroImage":719,"authors":720,"date":722,"body":723,"category":9,"tags":724},"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",[721],"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/)",[710,24],{"featured":12,"template":13,"slug":726},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":728,"config":738},{"title":729,"description":730,"authors":731,"category":9,"tags":733,"date":735,"heroImage":736,"body":737},"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.",[732],"Regnard Raquedan",[710,734,102,550],"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":713,"template":13,"slug":739},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":741},[742,755,766],{"id":743,"categories":744,"header":745,"text":746,"button":747,"image":752},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":748,"config":749},"Get your AI maturity score",{"href":750,"dataGaName":751,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":753},{"src":754},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":756,"categories":757,"header":758,"text":746,"button":759,"image":763},"devops-modernization",[23,553],"Are you just managing tools or shipping innovation?",{"text":760,"config":761},"Get your DevOps maturity score",{"href":762,"dataGaName":751,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":764},{"src":765},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":767,"categories":768,"header":769,"text":746,"button":770,"image":774},"security-modernization",[24],"Are you trading speed for security?",{"text":771,"config":772},"Get your security maturity score",{"href":773,"dataGaName":751,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":775},{"src":776},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":778,"blurb":779,"button":780,"secondaryButton":785},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":781,"config":782},"Get your free trial",{"href":783,"dataGaName":44,"dataGaLocation":784},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":489,"config":786},{"href":48,"dataGaName":49,"dataGaLocation":784},1772652062310]