[{"data":1,"prerenderedAt":706},["ShallowReactive",2],{"/en-us/blog/accelerate-learning-with-gitlab-duo-agent-platform/":3,"navigation-en-us":33,"banner-en-us":451,"footer-en-us":468,"Halil Coban":678,"next-steps-en-us":691},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"seo":8,"config":11,"content":15,"_id":26,"_type":27,"title":28,"_source":29,"_file":30,"_stem":31,"_extension":32},"/en-us/blog/accelerate-learning-with-gitlab-duo-agent-platform","blog",false,"",{"title":9,"description":10},"Accelerate learning with GitLab Duo Agent Platform","Learn how agentic AI helped generate comprehensive gRPC documentation in minutes, not hours.",{"slug":12,"featured":13,"template":14},"accelerate-learning-with-gitlab-duo-agent-platform",true,"BlogPost",{"title":9,"description":10,"authors":16,"heroImage":18,"date":19,"category":20,"tags":21,"body":25},[17],"Halil Coban","Blog/Hero%20Images/Workflow_1800x945.png","2025-07-07","ai-ml",[20,22,23,24],"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\u003Cdiv style=\"padding:75% 0 0 0;position:relative;\">\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\n    DWS->>DWE: Action Message\n    Note 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\n    DWE->>Runner: Receive Action\n    Runner->>Runner: processWorkflowActions()\n    Runner->>ActionExecutor: executeAction(ctx, action)\n    \n    alt 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    \n    else 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    \n    else 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    \n    else 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    \n    else 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    \n    else 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    \n    else Action_ListDirectory\n        ActionExecutor->>FileSystem: List Directory Contents\n        Note over ActionExecutor, FileSystem: Respect gitignore rules\n        FileSystem-->>ActionExecutor: Directory Listing\n    \n    else 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    \n    else 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    \n    else Action_NewCheckpoint/Action_RunMCPTool\n        ActionExecutor->>ActionExecutor: No-op Action\n        Note over ActionExecutor: Returns empty success result\n    end\n\n\n    ActionExecutor-->>Runner: Action Result (string)\n    \n    alt 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\n    end\n\n\n    Runner->>DWE: ActionResponse\n    DWE->>DWS: ClientEvent{ActionResponse}\n    Note 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\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. 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