[{"data":1,"prerenderedAt":825},["ShallowReactive",2],{"/en-us/blog/introduction-to-gitlab-duo-agent-platform":3,"navigation-en-us":39,"banner-en-us":460,"footer-en-us":470,"blog-post-authors-en-us-Itzik Gan Baruch":712,"blog-related-posts-en-us-introduction-to-gitlab-duo-agent-platform":726,"blog-promotions-en-us":763,"next-steps-en-us":815},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":22,"description":25,"extension":26,"externalUrl":27,"featured":14,"heroImage":23,"isFeatured":14,"meta":28,"navigation":29,"path":30,"publishedDate":22,"rawbody":31,"seo":32,"slug":13,"stem":34,"tagSlugs":35,"tags":37,"template":15,"updatedDate":27,"__hash__":38},"blogPosts/en-us/blog/introduction-to-gitlab-duo-agent-platform.yml","Introduction to GitLab Duo Agent Platform",[7],"itzik-gan-baruch",[9],"Itzik Gan Baruch","*Welcome to Part 1 of our eight-part guide, [Getting started with GitLab Duo Agent Platform](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/), where you'll master building and deploying AI agents and workflows within your development lifecycle. Follow tutorials that take you from your first interaction to production-ready automation workflows with full customization.*\n\nGitLab Duo Agent Platform represents a fundamental shift in how developers interact with AI during the software development lifecycle. Moving beyond code into full SDLC context, GitLab Duo Agent Platform enables multiple specialized AI agents to work alongside your team, handling complex tasks asynchronously while you focus on innovation and problem-solving.\n\nGitLab Duo Agent Platform transforms traditional linear development workflows into dynamic, multi-agent collaboration systems.\n\n## What is GitLab Duo Agent Platform?\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) is an AI orchestration layer that enables:\n\n- Asynchronous collaboration between developers and specialized AI agents\n- Full SDLC context across code, issues, epics, merge requests, CI/CD pipelines, wikis, analytics, and security scans\n- Multi-agent flows where many agents collaborate in parallel on complex tasks\n- Intelligent automation that understands your organization's standards, practices, and compliance requirements\n\nThink of it as adding AI team members who can take on entire workflows, from understanding requirements to creating merge requests, while you maintain full visibility and control.\n\n> 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today!\n## Platform architecture\n\nGitLab Duo Agent Platform consists of several interconnected components working together to provide comprehensive AI assistance. The diagram below shows the *user interaction methods* with GitLab Duo Agent Platform. It illustrates the four ways users can engage with agents:\n\n\n\n\n![GitLab Duo Agent Platform architecture diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373441/k0ktrcnyuqbq3unbcvyp.png \"GitLab Duo Agent Platform architecture diagram\")\n\n### How teams interact with GitLab Duo Agent Platform\n\n**Four ways to use agents**\n\n1. **GitLab Duo Agentic Chat** β€” Open the chat panel in the GitLab UI or your IDE for interactive conversations with foundational and custom agents. Select from available AI models and get real-time help.\n\n2. **Trigger Custom Flows** β€” Mention flows in issue or merge request comments, or assign reviewers to automatically trigger Custom Flows. These run asynchronously via runner execution.\n\n3. **Trigger Foundational Flows** β€” Built and maintained by GitLab, including **[Developer](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/developer/)**, **[Code Review](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/)**, **[Fix CI/CD Pipeline](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/fix_pipeline/)**, **[Convert Jenkins to GitLab CI/CD](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/)**, and **[Software Development Flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/)**.\n4. **Trigger External Agents** β€” Assign or mention external AI agents (like Claude Code or OpenAI Codex) in issue or merge request comments to automatically trigger them. These run asynchronously via runner execution.\n\n**Where to manage and discover**\n\n- **AI Catalog** β€” Browse, create, and share agents and flows across your organization. Discover agents and flows created by GitLab and your team, then add them to your projects. You can also create and publish your own custom agents and flows for others to use.\n\n- **Automate Capabilities** β€” Your central hub for managing everything. View and manage your agents, configure and monitor flows, review all activity in sessions (including pipeline status), and set up triggers for event-based automation.\n\nLet's explore each component briefly (we'll dive deeper in subsequent posts):\n\n**GitLab Duo Agentic Chat**\n\nYour primary interface for interacting with agents. Available as a persistent panel in the GitLab UI and in your IDE. Learn more in [Part 2: Getting Started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/).\n\n![GitLab Duo Agentic Chat](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618251/gdkojstbdsruen4bo5fw.png \"GitLab Duo Agentic Chat panel in the web UI\")\n\n![GitLab Duo Agentic Chat IDE](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373438/gjojavrvjhhvglgkvxmw.png \"GitLab Duo Agentic Chat panel in VS Code\")\n\n**Agents**\n\nAgents are specialized AI-powered assistants designed to handle specific tasks throughout your development workflow. Think of them as team members with unique expertise and capabilities.\n\n| Type | Description | Where Used | Setup Required |\n|------|-------------|------------|----------------|\n| **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/)** | Maintained by GitLab for common development workflows (Security Analyst, Planner, GitLab Duo), available by default in the chat of any project| GitLab Duo Chat | No |\n| **[Custom](https://docs.gitlab.com/user/duo_agent_platform/agents/custom/)** | Created by you for team-specific needs with custom prompts and tools | GitLab Duo Chat | Yes |\n| **[External](https://docs.gitlab.com/user/duo_agent_platform/agents/external/)** | External AI providers (Claude, OpenAI) triggered via mentions or assignments | @mentions, assignments | Optional |\n### About external agents\nExternal agents run in the background on GitLab platform compute when triggered by mentions (e.g., `@ai-codex`) or assignments in issues and merge requests. Unlike foundational and custom agents that use synchronous feedback loops, external agents execute asynchronously, enabling powerful automation with specialized AI providers.\n### What makes agents powerful\n- **Specialized prompts**: Each agent has a unique system prompt that defines its expertise, behavior, and communication style.\n- **Access to tools**: Agents can read files, access issues/MRs/epics, search code, analyze CI/CD job logs and vulnerability reports, and more based on their configuration.\n- **Project context:** Access to issues, merge requests, code, CI/CD pipelines, and security vulnerabilities.\n\nLearn more in [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/). Discover how to create custom agents, integrate external AI providers, and configure agent prompts and tools for your team's specific needs.\n\n**Flows**\n\nFlows are multi-step workflows that combine multiple actions to solve complex problems. Unlike agents that respond to questions, flows execute complete workflows autonomously via runner execution.\n\n| Type | Description | Where Triggered | Setup Required |\n|------|-------------|-----------------|----------------|\n| **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/)** | Maintained by GitLab for common development workflows (Developer, Fix Pipeline, Convert Jenkins to GitLab CI/CD, Software Development) | You invoke using dedicated UI action buttons, or using the IDE extension Flows tab| No |\n| **[Custom](https://docs.gitlab.com/user/duo_agent_platform/flows/custom/)** | User-defined workflows you create, tailored to your needs | Mentions in issues/MRs, assignment | Yes |\n### What makes flows powerful\n- **Multi-step execution**: Combine multiple operations into a single workflow\n- **Asynchronous processing**: Run in background while you continue working\n- **Full pipeline access**: Execute via runner execution with complete project context\n- **Event-driven**: Automatically triggered by GitLab events\n\nLearn more in [Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/), including multi-agent workflows.\n\n## Agents vs. flows: What's the difference?\n\nUnderstanding when to use an agent vs. a flow is key to working effectively with GitLab Duo Agent Platform.\n\n| Aspect | Agents (Interactive in Chat) | Flows (Automated on Platform) |\n|--------|------------------------------|-------------------------------|\n| **Purpose** | Interactive work, quick iterations, conversational guidance | Complex multi-step tasks, background automation, event-driven workflows |\n| **Where** | GitLab Duo Chat (Web UI, IDEs) | Issues, Merge Requests, UI action buttons |\n| **How** | Real-time conversation with ability to take actions | Triggered by events or button clicks |\n| **Execution** | Interactive, runs immediately in chat context | Asynchronous via runner execution |\n| **Example** | \"Refactor this function\" (agent modifies code), \"Create tests\" (agent generates test file) | \"Generate MR for issue #123\" (flow creates branch, commits, opens MR) |\n### Quick decision guide\n- Working interactively or want instant feedback? β†’ Use chat\n- Need background automation, MR review, or complex multi-file tasks? β†’ Use flow\n### Key insight\n\nBoth agents and flows can take actions and create code. The main difference is how they interact and run: Agents communicate interactively in your chat interface, while flows run asynchronously in the background on platform compute.\n\n#### AI Catalog\n\nA centralized library where you can browse, discover, create, and share agents and flows across your organization, detailed in [Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/).\n\n![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618250/sdtnio4rrbmwlh4iia4l.png \"AI Catalog\")\n\n#### Automate capabilities\n\nYour hub for managing agent and flow workflows:\n\n- **Agents**: View and manage agents in your project, detailed in [Part 3](/blog/understanding-agents-foundational-custom-external/).\n- **Flows**: View, create, and manage flows in your project, detailed in [Part 4](/blog/understanding-flows-multi-agent-workflows/).\n- **Sessions**: Agent activity logs\n- **Triggers**: Event-based automation management for flows in your project\n\n## Understanding sessions\n\nEvery agent and flow execution creates a session that logs agentic activities. Sessions provide full transparency into what happened, including agent reasoning, execution details, tool calling, outputs, and the complete decision trail.\n\n![Sessions Monitoring](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618251/jpqv5frskvgzz6fnmvjl.png \"Sessions overview showing execution status and progress\")\n\n\n\n\nTo view sessions: Navigate to your project > **Automate** > **Sessions**. From there, you can access the pipeline console to see detailed execution logs.\n\n## Model selection\n\nOne of the powerful features of GitLab Duo Agent Platform is the ability to choose which AI model powers your conversation.\n\n**Available in:** GitLab 18.4 and later\n\n**How to select:**\n\n1. Open GitLab Duo Agentic Chat.\n2. Look for the model dropdown.\n3. Click to see available models.\n4. Select the model best suited for your task.\n\n**Note:** Model selection is currently available in the Web UI only. IDE integration uses the default model selected for your group.\n\n## Your first agent interaction\n\nLet's walk through a simple first interaction with GitLab Duo Agentic Chat:\n\n### Example 1: Understanding your project (Agent)\n\n**Scenario:** You've just joined a project and need to understand its structure and architecture.\n\n**Steps:**\n\n1. Open GitLab Duo Chat panel (click Duo icon in top-right).\n2. Ensure Agentic mode (Beta) is toggled on.\n3. Select the Duo Agent (default).\n4. Type: \"Give me an overview of this project's architecture.\"\n5. Press **Enter**.\n\n**What happens:**\n\nThe agent:\n- Analyzes your repository structure\n- Reviews your README, code organization, and documentation\n- Provides a comprehensive overview with key components\n\nYou can ask follow-up questions for clarification.\n\n![Chat showing architecture overview](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373438/rvdxbupzh8bupt674kyc.png \"Chat showing Architecture Overview\")\n\n\n\n\n### Example 2: Generating a merge request (Flow)\n\n**Scenario:** You have an issue that needs to be resolved with code changes.\n\n**Steps:**\n\n1. Open the issue in GitLab.\n2. Click **Generate MR with Duo** button.\n3. An agent session starts.\n4. Within a few minutes, an MR is created with:\n\n - Code changes across multiple files\n\n - A descriptive commit message\n\n - An explanation of changes in MR description\n\n\n**What happens:**\n\nThe Developer Flow:\n- Analyzes the issue\n- Understands repository structure, design patterns, and SDLC context\n- Makes appropriate code changes\n- Opens a ready-to-review MR\n\n\n\n\n![Issue with Generate MR with Duo button](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373443/gq57mpgyftvru1fyqh4o.png \"Issue with Generate MR with Duo button\")\n\n\n\n\n## Common questions\n\n**Q: Are my conversations with agents private?**\n\nA: Yes. Conversations follow GitLab's standard privacy and security models. [Learn more.](https://docs.gitlab.com/user/gitlab_duo/data_usage/)\n\n**Q: Can I use GitLab Duo Agent Platform with self-hosted models?**\n\nA: Yes, starting with GitLab 18.8, it requires additional setup. See [GitLab documentation](https://docs.gitlab.com/administration/gitlab_duo_self_hosted/configure_duo_features/#configure-access-to-the-gitlab-duo-agent-platform).\n\n## What's next?\n\nNow that you understand the basics of GitLab Duo Agent Platform, you're ready to dive deeper into each component:\n\n- **[Part 2: Getting started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/)** β€” Master the persistent chat panel, learn model selection strategies, understand agent switching, and use chat effectively across Web UI and all supported IDEs.\n\n- **[Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/)** β€” Explore foundational agents built by GitLab, create custom agents with specialized prompts for your team's workflows, and integrate external CLI agents from providers like Claude Code and OpenAI Codex.\n\n- **[Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/)** β€” Discover how flows orchestrate multiple agents to solve complex problems, create custom YAML-defined workflows, and leverage external AI providers for automated pipeline execution.\n\n- **[Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/)** β€” Browse the centralized repository to discover agents and flows created by GitLab and the community, add them to your projects, and publish your own solutions for others to use.\n\n- **[Part 6: Monitor, manage, and automate AI workflows](/blog/monitor-manage-automate-ai-workflows/)** β€” Monitor all agent and flow activity through sessions, set up event-driven triggers to automate workflows, and manage your entire GitLab Duo Agent Platform ecosystem from one central location.\n\n- **[Part 7: Model Context Protocol integration](/blog/duo-agent-platform-with-mcp/)** β€” Extend GitLab Duo's capabilities by connecting to external tools like Jira, Slack, and AWS through the open MCP standard, and enable external AI tools to access your GitLab data.\n- **[Part 8: Customizing GitLab Duo Agent Platform](/blog/customizing-gitlab-duo-chat-rules-prompts-workflows/)** - Configure custom chat rules, create system prompts for agents, set up agent tools, integrate external systems with MCP, and customize flows for your team's specific needs.\n\n## Resources\n\n- [GitLab Duo Agent Platform documentation](https://docs.gitlab.com/user/duo_agent_platform/)\n- [GitLab Duo Agent Platform site](https://about.gitlab.com/gitlab-duo-agent-platform/)\n- [GitLab Community Forum](https://forum.gitlab.com/)\n\n---\n\n**Next:** [Part 2: Getting started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/)","ai-ml",{"slug":13,"featured":14,"template":15},"introduction-to-gitlab-duo-agent-platform",false,"BlogPost",{"tags":17,"category":11,"date":22,"heroImage":23,"authors":24,"description":25,"title":5,"body":10},[18,19,20,21],"AI/ML","product","features","tutorial","2026-01-14","https://res.cloudinary.com/about-gitlab-com/image/upload/v1765809212/noh0mdfn9o94ry9ykura.png",[9],"Learn the basics of GitLab Duo Agent Platform and complete your first agent interaction.","yml",null,{},true,"/en-us/blog/introduction-to-gitlab-duo-agent-platform","seo:\n noIndex: false\n title: 'Introduction to GitLab Duo Agent Platform'\n description: Learn the basics of GitLab Duo Agent Platform and complete your first agent interaction.\n ogImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1752678395/impw8no5tbskr6k2afgu.jpg\ncontent:\n tags:\n - AI/ML\n - product\n - features\n - tutorial\n category: ai-ml\n date: 2026-01-14\n heroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1765809212/noh0mdfn9o94ry9ykura.png\n authors:\n - Itzik Gan Baruch\n description: Learn the basics of GitLab Duo Agent Platform and complete your first agent interaction.\n title: 'Introduction to GitLab Duo Agent Platform'\n body: >-\n *Welcome to Part 1 of our eight-part guide, [Getting started with GitLab Duo Agent Platform](/blog/gitlab-duo-agent-platform-complete-getting-started-guide/), where you'll master building and deploying AI agents and workflows within your development lifecycle. Follow tutorials that take you from your first interaction to production-ready automation workflows with full customization.*\n\n\n GitLab Duo Agent Platform represents a fundamental shift in how developers interact with AI during the software development lifecycle. Moving beyond code into full SDLC context, GitLab Duo Agent Platform enables multiple specialized AI agents to work alongside your team, handling complex tasks asynchronously while you focus on innovation and problem-solving.\n\n\n GitLab Duo Agent Platform transforms traditional linear development workflows into dynamic, multi-agent collaboration systems.\n\n\n ## What is GitLab Duo Agent Platform?\n\n\n [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) is an AI orchestration layer that enables:\n\n\n - Asynchronous collaboration between developers and specialized AI agents\n\n - Full SDLC context across code, issues, epics, merge requests, CI/CD pipelines, wikis, analytics, and security scans\n\n - Multi-agent flows where many agents collaborate in parallel on complex tasks\n\n - Intelligent automation that understands your organization's standards, practices, and compliance requirements\n\n\n Think of it as adding AI team members who can take on entire workflows, from understanding requirements to creating merge requests, while you maintain full visibility and control.\n\n\n > 🎯 Try [**GitLab Duo Agent Platform**](https://about.gitlab.com/gitlab-duo-agent-platform/) today!\n\n ## Platform architecture\n\n\n GitLab Duo Agent Platform consists of several interconnected components working together to provide comprehensive AI assistance. The diagram below shows the *user interaction methods* with GitLab Duo Agent Platform.\n It illustrates the four ways users can engage with agents:\n\n\n\n\n\n ![GitLab Duo Agent Platform architecture diagram](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373441/k0ktrcnyuqbq3unbcvyp.png \"GitLab Duo Agent Platform architecture diagram\")\n\n\n ### How teams interact with GitLab Duo Agent Platform\n\n\n **Four ways to use agents**\n\n\n 1. **GitLab Duo Agentic Chat** β€” Open the chat panel in the GitLab UI or your IDE for interactive conversations with foundational and custom agents. Select from available AI models and get real-time help.\n\n\n 2. **Trigger Custom Flows** β€” Mention flows in issue or merge request comments, or assign reviewers to automatically trigger Custom Flows. These run asynchronously via runner execution.\n\n\n 3. **Trigger Foundational Flows** β€” Built and maintained by GitLab, including **[Developer](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/developer/)**, **[Code Review](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/code_review/)**, **[Fix CI/CD Pipeline](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/fix_pipeline/)**, **[Convert Jenkins to GitLab CI/CD](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/)**, and **[Software Development Flow](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/convert_to_gitlab_ci/)**.\n\n 4. **Trigger External Agents** β€” Assign or mention external AI agents (like Claude Code or OpenAI Codex) in issue or merge request comments to automatically trigger them. These run asynchronously via runner execution.\n\n\n **Where to manage and discover**\n\n\n - **AI Catalog** β€” Browse, create, and share agents and flows across your organization. Discover agents and flows created by GitLab and your team, then add them to your projects. You can also create and publish your own custom agents and flows for others to use.\n\n\n - **Automate Capabilities** β€” Your central hub for managing everything. View and manage your agents, configure and monitor flows, review all activity in sessions (including pipeline status), and set up triggers for event-based automation.\n\n\n Let's explore each component briefly (we'll dive deeper in subsequent posts):\n\n\n **GitLab Duo Agentic Chat**\n\n\n Your primary interface for interacting with agents. Available as a persistent panel in the GitLab UI and in your IDE. Learn more in [Part 2: Getting Started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/).\n\n\n ![GitLab Duo Agentic Chat](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618251/gdkojstbdsruen4bo5fw.png \"GitLab Duo Agentic Chat panel in the web UI\")\n\n\n ![GitLab Duo Agentic Chat IDE](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373438/gjojavrvjhhvglgkvxmw.png \"GitLab Duo Agentic Chat panel in VS Code\")\n\n\n **Agents**\n\n\n Agents are specialized AI-powered assistants designed to handle specific tasks throughout your development workflow. Think of them as team members with unique expertise and capabilities.\n\n\n | Type | Description | Where Used | Setup Required |\n\n |------|-------------|------------|----------------|\n\n | **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/agents/foundational_agents/)** | Maintained by GitLab for common development workflows (Security Analyst, Planner, GitLab Duo), available by default in the chat of any project| GitLab Duo Chat | No |\n\n | **[Custom](https://docs.gitlab.com/user/duo_agent_platform/agents/custom/)** | Created by you for team-specific needs with custom prompts and tools | GitLab Duo Chat | Yes |\n\n | **[External](https://docs.gitlab.com/user/duo_agent_platform/agents/external/)** | External AI providers (Claude, OpenAI) triggered via mentions or assignments | @mentions, assignments | Optional |\n\n ### About external agents\n\n External agents run in the background on GitLab platform compute when triggered by mentions (e.g., `@ai-codex`) or assignments in issues and merge requests. Unlike foundational and custom agents that use synchronous feedback loops, external agents execute asynchronously, enabling powerful automation with specialized AI providers.\n\n ### What makes agents powerful\n\n - **Specialized prompts**: Each agent has a unique system prompt that defines its expertise, behavior, and communication style.\n\n - **Access to tools**: Agents can read files, access issues/MRs/epics, search code, analyze CI/CD job logs and vulnerability reports, and more based on their configuration.\n\n - **Project context:** Access to issues, merge requests, code, CI/CD pipelines, and security vulnerabilities.\n\n\n Learn more in [Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/). Discover how to create custom agents, integrate external AI providers, and configure agent prompts and tools for your team's specific needs.\n\n\n **Flows**\n\n\n Flows are multi-step workflows that combine multiple actions to solve complex problems. Unlike agents that respond to questions, flows execute complete workflows autonomously via runner execution.\n\n\n | Type | Description | Where Triggered | Setup Required |\n\n |------|-------------|-----------------|----------------|\n\n | **[Foundational](https://docs.gitlab.com/user/duo_agent_platform/flows/foundational_flows/)** | Maintained by GitLab for common development workflows (Developer, Fix Pipeline, Convert Jenkins to GitLab CI/CD, Software Development) | You invoke using dedicated UI action buttons, or using the IDE extension Flows tab| No |\n\n | **[Custom](https://docs.gitlab.com/user/duo_agent_platform/flows/custom/)** | User-defined workflows you create, tailored to your needs | Mentions in issues/MRs, assignment | Yes |\n\n ### What makes flows powerful\n\n - **Multi-step execution**: Combine multiple operations into a single workflow\n\n - **Asynchronous processing**: Run in background while you continue working\n\n - **Full pipeline access**: Execute via runner execution with complete project context\n\n - **Event-driven**: Automatically triggered by GitLab events\n\n\n Learn more in [Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/), including multi-agent workflows.\n\n\n ## Agents vs. flows: What's the difference?\n\n\n Understanding when to use an agent vs. a flow is key to working effectively with GitLab Duo Agent Platform.\n\n\n | Aspect | Agents (Interactive in Chat) | Flows (Automated on Platform) |\n\n |--------|------------------------------|-------------------------------|\n\n | **Purpose** | Interactive work, quick iterations, conversational guidance | Complex multi-step tasks, background automation, event-driven workflows |\n\n | **Where** | GitLab Duo Chat (Web UI, IDEs) | Issues, Merge Requests, UI action buttons |\n\n | **How** | Real-time conversation with ability to take actions | Triggered by events or button clicks |\n\n | **Execution** | Interactive, runs immediately in chat context | Asynchronous via runner execution |\n\n | **Example** | \"Refactor this function\" (agent modifies code), \"Create tests\" (agent generates test file) | \"Generate MR for issue #123\" (flow creates branch, commits, opens MR) |\n\n ### Quick decision guide\n\n - Working interactively or want instant feedback? β†’ Use chat\n\n - Need background automation, MR review, or complex multi-file tasks? β†’ Use flow\n\n ### Key insight\n\n\n Both agents and flows can take actions and create code. The main difference is how they interact and run: Agents communicate interactively in your chat interface, while flows run asynchronously in the background on platform compute.\n\n\n #### AI Catalog\n\n\n A centralized library where you can browse, discover, create, and share agents and flows across your organization, detailed in [Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/).\n\n\n ![AI Catalog](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618250/sdtnio4rrbmwlh4iia4l.png \"AI Catalog\")\n\n\n #### Automate capabilities\n\n\n Your hub for managing agent and flow workflows:\n\n\n - **Agents**: View and manage agents in your project, detailed in [Part 3](/blog/understanding-agents-foundational-custom-external/).\n\n - **Flows**: View, create, and manage flows in your project, detailed in [Part 4](/blog/understanding-flows-multi-agent-workflows/).\n\n - **Sessions**: Agent activity logs\n\n - **Triggers**: Event-based automation management for flows in your project\n\n\n ## Understanding sessions\n\n\n Every agent and flow execution creates a session that logs agentic activities. Sessions provide full transparency into what happened, including agent reasoning, execution details, tool calling, outputs, and the complete decision trail.\n\n\n ![Sessions Monitoring](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767618251/jpqv5frskvgzz6fnmvjl.png \"Sessions overview showing execution status and progress\")\n\n\n\n\n\n To view sessions: Navigate to your project > **Automate** > **Sessions**. From there, you can access the pipeline console to see detailed execution logs.\n\n\n ## Model selection\n\n\n One of the powerful features of GitLab Duo Agent Platform is the ability to choose which AI model powers your conversation.\n\n\n **Available in:** GitLab 18.4 and later\n\n\n **How to select:**\n\n\n 1. Open GitLab Duo Agentic Chat.\n\n 2. Look for the model dropdown.\n\n 3. Click to see available models.\n\n 4. Select the model best suited for your task.\n\n\n **Note:** Model selection is currently available in the Web UI only. IDE integration uses the default model selected for your group.\n\n\n ## Your first agent interaction\n\n\n Let's walk through a simple first interaction with GitLab Duo Agentic Chat:\n\n\n ### Example 1: Understanding your project (Agent)\n\n\n **Scenario:** You've just joined a project and need to understand its structure and architecture.\n\n\n **Steps:**\n\n\n 1. Open GitLab Duo Chat panel (click Duo icon in top-right).\n\n 2. Ensure Agentic mode (Beta) is toggled on.\n\n 3. Select the Duo Agent (default).\n\n 4. Type: \"Give me an overview of this project's architecture.\"\n\n 5. Press **Enter**.\n\n\n **What happens:**\n\n\n The agent:\n\n - Analyzes your repository structure\n\n - Reviews your README, code organization, and documentation\n\n - Provides a comprehensive overview with key components\n\n\n You can ask follow-up questions for clarification.\n\n\n ![Chat showing architecture overview](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373438/rvdxbupzh8bupt674kyc.png \"Chat showing Architecture Overview\")\n\n\n\n\n\n ### Example 2: Generating a merge request (Flow)\n\n\n **Scenario:** You have an issue that needs to be resolved with code changes.\n\n\n **Steps:**\n\n\n 1. Open the issue in GitLab.\n\n 2. Click **Generate MR with Duo** button.\n\n 3. An agent session starts.\n\n 4. Within a few minutes, an MR is created with:\n\n - Code changes across multiple files\n\n - A descriptive commit message\n\n - An explanation of changes in MR description\n\n\n **What happens:**\n\n\n The Developer Flow:\n\n - Analyzes the issue\n\n - Understands repository structure, design patterns, and SDLC context\n\n - Makes appropriate code changes\n\n - Opens a ready-to-review MR\n\n\n\n\n\n ![Issue with Generate MR with Duo button](https://res.cloudinary.com/about-gitlab-com/image/upload/v1765373443/gq57mpgyftvru1fyqh4o.png \"Issue with Generate MR with Duo button\")\n\n\n\n\n\n ## Common questions\n\n\n **Q: Are my conversations with agents private?**\n\n\n A: Yes. Conversations follow GitLab's standard privacy and security models. [Learn more.](https://docs.gitlab.com/user/gitlab_duo/data_usage/)\n\n\n **Q: Can I use GitLab Duo Agent Platform with self-hosted models?**\n\n\n A: Yes, starting with GitLab 18.8, it requires additional setup. See [GitLab documentation](https://docs.gitlab.com/administration/gitlab_duo_self_hosted/configure_duo_features/#configure-access-to-the-gitlab-duo-agent-platform).\n\n\n ## What's next?\n\n\n Now that you understand the basics of GitLab Duo Agent Platform, you're ready to dive deeper into each component:\n\n\n - **[Part 2: Getting started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/)** β€” Master the persistent chat panel, learn model selection strategies, understand agent switching, and use chat effectively across Web UI and all supported IDEs.\n\n\n - **[Part 3: Understanding agents](/blog/understanding-agents-foundational-custom-external/)** β€” Explore foundational agents built by GitLab, create custom agents with specialized prompts for your team's workflows, and integrate external CLI agents from providers like Claude Code and OpenAI Codex.\n\n\n - **[Part 4: Understanding flows](/blog/understanding-flows-multi-agent-workflows/)** β€” Discover how flows orchestrate multiple agents to solve complex problems, create custom YAML-defined workflows, and leverage external AI providers for automated pipeline execution.\n\n\n - **[Part 5: AI Catalog](/blog/ai-catalog-discover-and-share-agents/)** β€” Browse the centralized repository to discover agents and flows created by GitLab and the community, add them to your projects, and publish your own solutions for others to use.\n\n\n - **[Part 6: Monitor, manage, and automate AI workflows](/blog/monitor-manage-automate-ai-workflows/)** β€” Monitor all agent and flow activity through sessions, set up event-driven triggers to automate workflows, and manage your entire GitLab Duo Agent Platform ecosystem from one central location.\n\n\n - **[Part 7: Model Context Protocol integration](/blog/duo-agent-platform-with-mcp/)** β€” Extend GitLab Duo's capabilities by connecting to external tools like Jira, Slack, and AWS through the open MCP standard, and enable external AI tools to access your GitLab data.\n\n - **[Part 8: Customizing GitLab Duo Agent Platform](/blog/customizing-gitlab-duo-chat-rules-prompts-workflows/)** - Configure custom chat rules, create system prompts for agents, set up agent tools, integrate external systems with MCP, and customize flows for your team's specific needs.\n\n\n ## Resources\n\n\n - [GitLab Duo Agent Platform documentation](https://docs.gitlab.com/user/duo_agent_platform/)\n\n - [GitLab Duo Agent Platform site](https://about.gitlab.com/gitlab-duo-agent-platform/)\n\n - [GitLab Community Forum](https://forum.gitlab.com/)\n\n\n ---\n\n\n **Next:** [Part 2: Getting started with GitLab Duo Agentic Chat](/blog/getting-started-with-gitlab-duo-agentic-chat/)\n\nconfig:\n slug: introduction-to-gitlab-duo-agent-platform\n featured: false\n template: 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will train on your data: Opt out with GitLab","Learn why Atlassian's latest move is a threat to data governance and how GitLab's approach helps ensure your customers' data stays private and protected.",[732],"Jessica Hurwitz","2026-05-04","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773866173/vte9qh8rriznvyclhkes.png","Starting August 17, 2026, Atlassian will begin collecting customer metadata and in-app content from Jira, Confluence, and other cloud products to train its AI offerings, including Rovo and Rovo Dev. This announcement comes after [GitHub recently changed its Copilot data usage policy](https://about.gitlab.com/blog/github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call/). **Taken together, these changes suggest opt-out-by-default is becoming the industry norm. GitLab takes the opposite position: no data collection, no AI training on customer data, no matter what tier you're on.**\n\n[Atlassian's change](https://www.atlassian.com/trust/ai/data-contribution) is enabled by default for all cloud customers and affects roughly 300,000 organizations. For customers on the Free, Standard, and Premium tiers, metadata collection is mandatory and cannot be turned off. Only Enterprise-tier customers have the option to opt out. This policy change deserves a close read if your engineering, IT, and program management teams run on Atlassian because they are most exposed by this change β€” and least likely to have been consulted before it happened.\n\nAlthough the underlying governance questions are the same for both Atlassian and GitHub's changes, the data at risk is different. Where GitHub's change concerned source code and developer interactions, Atlassian's reaches into project plans, internal documentation, workflow configurations, and operational metadata across Jira, Confluence, and the broader Atlassian stack. **For organizations that rely on these tools as their system of record for how work gets planned and delivered, the implications run deep.**\n\n## What changed and what it means for your data\n\nAtlassian will collect two categories of information: \n\n- **Metadata:** de-identified operational signals like story points, sprint dates, and SLA values, including data from its Teamwork Graph and connected third-party apps \n- **In-app content:** user-generated material such as Confluence page content, Jira issue titles, descriptions, and comments\n\nAtlassian says it will apply de-identification and aggregation before training. Collected data may be retained for up to seven years, with in-app data removed within 30 days of opt-out and models retrained within 90 days.\n\nThere are some exclusions: Customers using customer-managed encryption keys, Atlassian Government Cloud, Isolated Cloud, or those with HIPAA requirements are carved out from collection. But for the vast majority of Atlassian's cloud customer base, data collection will start unless you pay for the Enterprise tier and actively flip the switch.\n\nThis reverses Atlassian's prior stated position that customer data would not be used to train or improve AI services. Organizations that adopted Jira and Confluence to manage their most sensitive planning workflows, sprint boards, security tickets, incident postmortems, and internal documentation will soon be contributing that content to Atlassian's AI training pipeline, without ever being asked.\n\n## The governance gap in \"opt-out by default\"\n\nOpt-out-by-default data collection for AI training is an emerging pattern across the software industry. It raises the same set of questions every time: How does this interact with existing data processing agreements? Does the vendor's definition of \"metadata\" match what your legal and security teams would consider non-sensitive data?\n\n**For many organizations, the answer to these questions is \"we don't know.\"** \n\nWhen a vendor changes its data practices through a terms-of-service update, the burden falls on the customer to notice, evaluate the implications, and act within the window the vendor provides. \n\nThe mandatory nature of metadata collection on Free, Standard, and Premium tiers makes this more acute. The only exit is upgrading to Enterprise, which requires a minimum of 801 users and custom pricing that would represent a significant cost jump for teams that aren't there yet. Data protection, in other words, is now a purchasing decision.\n\nThe tiered structure also introduces a subtler problem. Metadata like story points, sprint velocity, SLA metrics, and task classifications may seem innocuous in isolation, but in aggregate they reveal project structure, team performance patterns, and delivery cadence. For organizations in competitive industries, that operational intelligence has real value, and \"de-identified\" does not necessarily mean \"non-sensitive\" once patterns are reconstructable at scale.\n\n## Why this matters more for Atlassian-stack organizations\n\nIn Atlassian-based organizations, Jira has been the center how teams plan, track, and deliver work. It’s the source of truth for sprint planning, bug tracking, release management, portfolio coordination, and cross-functional project execution. \n\nIn regulated industries like financial services, public sector and manufacturing, Jira and Confluence together hold sensitive operational data that may be subject to compliance requirements. The risk compounds for organizations that have expanded beyond Jira into the broader Atlassian ecosystem.\n\nWhen you run Jira, Confluence, Bitbucket, and Bamboo together, the surface area of data now feeding into AI training spans your project plans, internal documentation, source code metadata, and CI/CD configurations β€” each of which security and compliance teams would want to review before sharing with a vendor's training pipeline.\n\nAtlassian’s Teamwork Graph connectors add another dimension for customers who have integrated third-party tools, such as Slack, Figma, Google Drive, Salesforce, and ServiceNow, into their environment. Teamwork Graph connectors index relationship and activity signals from these connected apps, which means the metadata Atlassian collects will not be limited to what lives inside Atlassian products. For security and compliance teams accustomed to evaluating data flows on a per-vendor basis, this cross-platform reach complicates the assessment considerably.\n\nOrganizations that are already navigating [Atlassian's push from Data Center](https://about.gitlab.com/blog/atlassian-ending-data-center-as-gitlab-maintains-deployment-choice/) and Server editions to the cloud face a compounding challenge. Adding default AI data collection to that migration path raises the stakes further: **The question is no longer just \"do we move to Atlassian Cloud?\" but \"do we move to Atlassian Cloud knowing our data will feed AI training unless we're on the most expensive tier?\"**\n\n## What regulated industries should be evaluating now\n\nThe compliance implications vary by sector, but the obligation to reassess is consistent.\n\nIn financial services, frameworks like [SR 11-7](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) and [DORA](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) require documented, auditable oversight of third-party technology providers, including how those providers handle data. In the public sector, [NIST 800-53](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and [FISMA](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) make controlling where sensitive data flows a foundational requirement. In healthcare, [HIPAA](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties. \n\nAcross the board, a material change in a vendor's data practices, such as Atlassian moving from \"we don't train on your data\" to \"we do, by default,\" triggers a documentation and risk reassessment obligation. \n\nInstitutions operating under the [EU AI Act](https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng) face an additional dimension: opt-out framing aligns with U.S. norms, while European regulators generally expect opt-in consent for data processing of this nature.\n\nIf your model risk or vendor management team documented Atlassian's data handling controls before this announcement, the question isn't whether this change triggers a reassessment obligation. It does. The question is whether your team can take action before August 17.\n\n## What to look for in your platform vendors\n\nCTOs and CISOs across regulated industries need to adopt AI in a way they can explain to regulators, boards, and customers. Because of this, GitLab operates within the following set of principles:\n\n**Unconditional data commitments, not tier-dependent protections.** Regulated organizations need to know, with specificity, what happens to their data. A commitment that varies by plan tier, or that requires action before a deadline, introduces exactly the kind of uncontrolled variable that keeps CISOs up at night.\n\n**Transparency and auditability.** Model risk management frameworks require organizations to understand the AI systems they deploy, including the training data and third parties involved. Vendors who cannot answer these questions clearly create documentation risk.\n\n**Separation between customer data and vendor AI training.** When a platform vendor trains models on customer usage data, workflows and operational patterns become inputs to a system that also serves competitors. For organizations where project structure or delivery cadence represents competitive advantage, that exposure matters.\n\n## How GitLab's approach differs\n\nGitLab doesn't train on customer data β€” at any tier, full stop. AI vendors powering GitLab Duo features are contractually prohibited from using customer inputs or outputs for their own purposes, [a commitment GitLab CEO Bill Staples](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf) has consistently reiterated.\n\n[GitLab's AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) documents exactly which models power which features, how data is handled, and what vendor commitments are in place. [GitLab's AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including any material changes to how AI vendors treat customer data. For institutions managing third-party AI risk under DORA or similar frameworks, vendor continuity and concentration are active governance concerns, and having a documented plan for both is part of what responsible AI tooling looks like.\n\nFor organizations that require AI processing to stay within their own infrastructure, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) is available with GitLab Self-Managed deployments, including support for integration with self-hosted AI models. This means prompts and code never leave the customer's environment. GitLab also provides IP indemnification for Duo-generated output, with no filters required and no activation steps needed. Where your data lives remains your choice, no matter your deployment model or subscription tier.\n\n> Whether your organization stays on Atlassian or begins evaluating alternatives, the conversation about who controls your data and how it gets used should be happening now. **The August 17 deadline is approaching, but you still have time to [try GitLab Ultimate with Duo Agent Platform for free today](https://gitlab.com/-/trials/new).**",[18,19],{"featured":29,"template":15,"slug":738},"atlassian-will-train-on-your-data-opt-out-with-gitlab",{"content":740,"config":749},{"title":741,"description":742,"authors":743,"heroImage":745,"date":746,"body":747,"category":11,"tags":748},"GitLab and Anthropic: Governed AI for enterprise development","GitLab deepens its Anthropic Claude integration, bringing governed AI, access to new models, and cloud flexibility to enterprise software development.",[744],"Stuart Moncada","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776457632/llddiylsgwuze0u1rjks.png","2026-04-28","For enterprise and public sector leaders, the tension is familiar: Software teams need to move faster with AI, while security, compliance, and regulatory expectations only get more stringent. GitLab deepens its Anthropic Claude integration so organizations get access to newly released Claude models inside GitLab’s intelligent orchestration platform where governance, compliance, and auditability already run.\n\nClaude powers capabilities across GitLab Duo Agent Platform as the default model out of the box, across a variety of use cases from code generation and review to agentic chat and vulnerability resolution. If you've used GitLab Duo, you've already experienced how Duo agents automate workflows across the entire software development lifecycle (SDLC).\n\nThis accelerates the integration of Claude’s capabilities into GitLab, broadens how enterprises can deploy them, and reinforces what makes GitLab fundamentally different as a platform for software development and engineering: governance, compliance, and auditability built into every AI interaction.\n\n> \"GitLab Duo has accelerated how our teams plan, build, and ship software. The combination of Anthropic's Claude and GitLab's platform means we're getting more capable AI without changing how we work or how it is governed.\"\n>\n> – Mans Booijink, Operations Manager, Cube\n\n## The real differentiator: Governed AI\n\nWith GitLab, governance controls and auditing are built into the SDLC. When Claude suggests a code change through the GitLab Duo Agent Platform, that suggestion flows through the same merge request process, the same approval rules, the same security scanning, and the same audit trail as every other change. AI doesn't get a shortcut around your controls. It operates within them.\n\nAs GitLab moves deeper into agentic software development, where AI autonomously handles well-defined tasks, the governance layer becomes more important. An AI agent that can open a merge request, help resolve a vulnerability, or refactor a service needs to be auditable, attributable, and subject to the same policy enforcement as a human developer. That requirement is an architectural decision GitLab made from the start, and one that grows more consequential as AI agents take on broader responsibilities.\n\n## Enterprise deployment flexibility\n\nThis also expands how organizations access the latest Claude models through GitLab. Claude is available within GitLab through Google Cloud's Vertex AI and Amazon Bedrock, which means enterprises can route AI workloads through the hyperscaler commitments and cloud governance frameworks they already have in place. No separate vendor contract. No new data residency questions. Your existing Google Cloud or AWS relationship is the on-ramp. \n\nGitLab is now also available in the [Claude Marketplace](https://claude.com/platform/marketplace), allowing customers to purchase GitLab Credits and apply them toward existing Anthropic spending commitments – consolidating AI spend and simplifying how teams discover and procure GitLab alongside their Anthropic investments.\n\n## Advancing an agentic future\n\nGitLab's vision for agentic software development, where AI handles defined tasks autonomously across planning, coding, testing, securing, and deploying, requires models with strong reasoning, reliability, and safety characteristics. It also requires a platform where those autonomous actions are fully governed.\n\nAgentic workflows demand models with strong reasoning, reliability, and safety characteristics, criteria that guide how GitLab selects and integrates AI model partners. And GitLab's governance framework helps ensure that as AI agents assume more advanced development work, enterprises maintain full visibility and control over what those agents do, when they do it, and how changes are tracked.\n\n## What this means for GitLab customers\n\nIf you're already using GitLab Duo Agent Platform, you'll get access to Claude models and deeper AI assistance across your software development lifecycle, all within the governance framework you already rely on.\n\nIf you're evaluating AI-powered software development platforms, you shouldn't have to choose between advanced AI capabilities and enterprise control. This strategic integration is built to deliver both.\n\n> Want to learn more about GitLab Duo Agent Platform? [Get a demo or start a free trial today](https://about.gitlab.com/gitlab-duo-agent-platform/).",[18,19,287],{"featured":29,"template":15,"slug":750},"gitlab-and-anthropic-governed-ai-for-enterprise-development",{"content":752,"config":761},{"title":753,"description":754,"authors":755,"heroImage":757,"date":758,"body":759,"category":11,"tags":760},"Give your AI agent direct, structured GitLab access with glab CLI","The GitLab CLI (glab) provides AI agents structured, reliable access to projects via the MCP, eliminating friction. This tutorial shows how you can speed up code review and issue triage.",[756],"Kai Armstrong","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-27","\nWhen teams use GitLab Duo, Claude, Cursor, and other AI assistants, more of the development workflow runs through an AI agent acting on your behalf β€” reading issues, reviewing merge requests, running pipelines, and helping you ship faster. Most developers are already using the GitLab CLI (`glab`) from the terminal to interact with GitLab. Combining the two is a natural next step.\n\n\nThe problem is that without the right tools, AI agents are essentially guessing when it comes to your GitLab projects. They might hallucinate the details of an issue they've never seen, summarize a merge request based on stale training data rather than its actual state, or require you to manually copy context from a browser tab and paste it into a chat window just to get started. Every one of those workarounds is friction: it slows you down, introduces the possibility of error, and puts a hard ceiling on what your agent can actually do on your behalf. `glab` changes that by giving agents a direct, reliable interface to your projects.\n\n\nWith `glab`, your agent fetches what it needs directly from GitLab, acts on it, and reports back β€” so you spend less time relaying information and more time on the work that matters.\n\n\nIn this tutorial, you'll learn how to use `glab` to give AI agents structured, reliable access to your GitLab projects. You'll also discover how that unlocks a faster, more capable development workflow.\n\n\n## How to connect your AI agent to GitLab through MCP\n\n\nThe most direct way to supercharge your AI workflow is to give your AI agent native access to `glab` through Model Context Protocol ([MCP](https://about.gitlab.com/topics/ai/model-context-protocol/)).\n\n\n MCP is an open standard that lets AI tools discover and use external capabilities at runtime. Once connected, your AI assistant can read issues, comment on merge requests, check pipeline status, and write back to GitLab, all without copying anything from the UI or writing a single API call yourself.\n\n\n To get started, run:\n\n\n ```shell\n # Start the glab MCP server\n glab mcp serve\n ```\n\n\n Once your MCP client is configured, your AI can answer questions like *\"What's the status of my open MRs?\"* or *\"Are there any failing pipelines on main?\"* by querying GitLab directly, not scraping the web UI, not relying on stale training data. See the [full setup docs](https://docs.gitlab.com/cli/) for configuration steps for Claude Code, Cursor, and other editors.\n\n\n One detail worth knowing: `glab` automatically adds `--output json` when invoked through MCP, for any command that supports it. Your agent gets clean, structured data without you needing to think about output formats. And because `glab` uses the official MCP SDK, it stays compatible as the\n protocol evolves.\n\n\n We've also been deliberate about *which* commands are exposed through MCP. Commands that require interactive terminal input are intentionally\n excluded, so your agent never gets stuck waiting for input that will never come. What's exposed is what actually works reliably in an agent context.\n\n\n ## Let your AI participate in code review\n\n\n Most developers have a backlog of MRs waiting for review. It's one of the most time-consuming parts of the job and one of the best places to put\n AI to work. With `glab`, your agent doesn't just observe your review queue, it can work through it with you.\n\n\n ### See exactly what still needs addressing\n\n\n Start with this:\n\n\n ```shell\n glab mr view 2677 --comments --unresolved --output json\n ```\n\n\n This input returns the full MR: metadata, description, and every\n unresolved discussion, as a single structured JSON payload. Hand that to\n your AI and it has everything it needs: which threads are open, what the\n reviewer asked for, and in what context. No tab-switching, no copy-pasting\n individual comments.\n\n\n \n ```json\n {\n \"id\": 2677,\n \"title\": \"feat: add OAuth2 support\",\n \"state\": \"opened\",\n \"author\": { \"username\": \"jdwick\" },\n \"labels\": [\"backend\", \"needs-review\"],\n \"blocking_discussions_resolved\": false,\n \"discussions\": [\n {\n \"id\": \"3107030349\",\n \"resolved\": false,\n \"notes\": [\n {\n \"author\": { \"username\": \"dmurphy\" },\n \"body\": \"This error handling will swallow panics β€” consider wrapping with recover()\",\n \"created_at\": \"2026-03-14T09:23:11.000Z\"\n }\n ]\n },\n {\n \"id\": \"3107030412\",\n \"resolved\": false,\n \"notes\": [\n {\n \"author\": { \"username\": \"sreeves\" },\n \"body\": \"Token refresh logic needs a test for the expired token case\",\n \"created_at\": \"2026-03-14T10:05:44.000Z\"\n }\n ]\n }\n ]\n }\n ```\n\n\n Instead of reading through every thread yourself, you ask your agent *\"what do I still need to fix in MR 2677?\"* and get back a prioritized summary with suggested changes. This all happens from a single command.\n\n\n ### Close the loop programmatically\n\n\n Once your AI has helped you address the feedback, it can resolve\n discussions:\n\n\n ```shell\n # List all discussions β€” structured, ready for the agent to process\n glab mr note list 456 --output json\n\n # Resolve a discussion once the feedback is addressed\n glab mr note resolve 456 3107030349\n\n # Reopen if something needs another look\n glab mr note reopen 456 3107030349\n ```\n\n\n\n ```json\n [\n {\n \"id\": 3107030349,\n \"body\": \"This error handling will swallow panics β€” consider wrapping with recover()\",\n \"author\": { \"username\": \"dmurphy\" },\n \"resolved\": false,\n \"resolvable\": true\n },\n {\n \"id\": 3107030412,\n \"body\": \"Token refresh logic needs a test for the expired token case\",\n \"author\": { \"username\": \"sreeves\" },\n \"resolved\": false,\n \"resolvable\": true\n }\n ]\n ```\n\n\n\n Note IDs are visible directly in the GitLab UI and API, no extra lookup needed. Your agent can work through the full list, verify each fix, and\n resolve as it goes.\n\n\n ## Talk to your AI about your code more effectively\n\n\n Even if you're not running an MCP server, there's a simpler shift that makes a huge difference: using `glab` to feed your AI better information.\n\n\n Think about the last time you asked an AI assistant to help triage issues or debug a failing pipeline. You probably copied some text from the GitLab UI and pasted it into the chat. Here's what your agent is actually\n working with when you do that:\n\n\n ```text\n open issues: 12 β€’ milestone: 17.10 β€’ label: bug, needs-triage ...\n ```\n\n\n Compare that to what it gets with `glab`:\n\n\n \n ```json\n [\n {\n \"iid\": 902,\n \"title\": \"Pipeline fails on merge to main\",\n \"labels\": [\"bug\", \"needs-triage\"],\n \"milestone\": { \"title\": \"17.10\" },\n \"assignees\": []\n },\n ...\n ]\n ```\n\n\n Structured, typed, complete; no ambiguity, no parsing guesswork. That's the difference between an agent that can act and one that has to ask\n follow-up questions.\n\n\n If you're using the MCP server, you get this automatically: `glab` adds `--output json` for any command that supports it. If you're working directly\n from the terminal, just add the flag yourself:\n\n\n ```shell\n # Pull open issues for triage\n glab issue list --label \"needs-triage\" --output json\n\n # Check pipeline status\n glab ci status --output json\n\n # Get full MR details\n glab mr view 456 --output json\n ```\n\n\n We've significantly expanded JSON output support in recent releases. It now covers CI status, milestones, labels, releases, schedules, cluster agents, work items, MR approvers, repo contributors, and more. If `glab` can\n retrieve it, your AI can consume it cleanly.\n\n\n ### A real workflow\n\n\n ```shell\n $ glab issue list --label \"needs-triage\" --milestone \"17.10\"\n --output json\n ```\n\n\n ```text\n Agent: I found 2 unassigned bugs in the 17.10 milestone that need triage:\n 1. #902 β€” Pipeline fails on merge to main (opened 5 days ago)\n 2. #903 β€” Auth token not refreshing on expiry (opened 4 days ago)\n Both are unassigned. Want me to draft triage notes and suggest assignees based on recent commit history?\n ```\n\n\n ## Your agent is never limited to built-in commands\n\n\n `glab`'s first-class commands cover the most common workflows, but your agent is never limited to them. Through `glab api`, it has authenticated access to the full GitLab REST and GraphQL API surface, using the same session, with no extra credentials or configuration required.\n\n\n This is a meaningful differentiator. Most CLI tools stop at what their commands expose. With `glab`, if GitLab's API supports it, your agent can do it. It's always working from a trusted, authenticated context.\n\n\n A practical example: fetching just the list of changed files in an MR before deciding which diffs to pull in full:\n\n\n ```shell\n # Get changed file paths β€” lightweight, no diff content yet\n glab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n | jq '.[].new_path'\n\n# Then fetch only the specific file your agent needs\nglab api \"/projects/$CI_PROJECT_ID/merge_requests/$CI_MERGE_REQUEST_IID/diffs?per_page=100\" \\\n| jq '.[] | select(.new_path == \"path/to/file.go\")'\n ```\n\n\n ```text\n \"internal/auth/token.go\"\n \"internal/auth/token_test.go\"\n \"internal/oauth/refresh.go\"\n ```\n\n\n For anything the REST API doesn't cover (epics, certain work item queries, complex cross-project data), `glab api graphql` gives you the full\n GraphQL interface:\n\n\n ```shell\n glab api graphql -f query='\n {\n project(fullPath: \"gitlab-org/gitlab\") {\n mergeRequest(iid: \"12345\") {\n title\n reviewers { nodes { username } }\n }\n }\n }'\n ```\n\n ```json\n{\n \"data\": {\n \"project\": {\n \"mergeRequest\": {\n \"title\": \"feat: add OAuth2 support\",\n \"reviewers\": {\n \"nodes\": [\n { \"username\": \"dmurphy\" },\n { \"username\": \"sreeves\" }\n ]\n }\n }\n }\n }\n }\n\n ```\n\n\n Your agent has a single, authenticated entry point to everything GitLab exposes without the token juggling, separate API clients, or configuration\n overhead.\n\n\n ## What's coming and your feedback\n\n\n Two improvements we're actively working on will make `glab` even more useful for agent workflows:\n\n\n **Agent-aware help text.** Today, `--help` output is written for humansvat a terminal. We're updating it to surface the non-interactive alternative\n for every interactive command, flag which commands support `--output json`, and generally make help a useful resource for agents discovering\n capabilities at runtime β€” not just humans.\n\n\n **Better machine-readable errors.** When something goes wrong today, agents get the same human-readable error messages as terminal users. We're\n changing that so errors in JSON mode return structured output, giving your agent the information it needs to handle failures gracefully, retry intelligently, or surface the right context back to you.\n\n\n Both of these are in active development. If you're already using `glab` with an AI tool, you're exactly the audience we want feedback from.\n\n\n * **What friction are you hitting?** Commands that don't behave well in agent contexts, error messages that aren't actionable, gaps in JSON output\n coverage. We want to know.\n\n * **What workflows have you unlocked?** Real usage patterns help us prioritize what to build next.\n\n\n Join the discussion in [our feedback issue](https://gitlab.com/gitlab-org/cli/-/issues/8177) β€” that's where we're shaping the roadmap for agent-friendliness, and where your input will have the most direct impact. If you've found a specific gap, [open an issue](https://gitlab.com/gitlab-org/cli/-/issues/new). If you've got a fix in mind, contributions are welcome. Visit [CONTRIBUTING.md](https://gitlab.com/gitlab-org/cli/-/blob/main/CONTRIBUTING.md) to get started.\n\n\n The GitLab CLI has always been about giving developers more control over their workflow. As AI becomes a bigger part of how we all work, that means making `glab` the best possible interface between your AI tools and your GitLab projects. We're just getting started and we'd love to build the next part with you.\n",[18,19,21],{"featured":29,"template":15,"slug":762},"give-your-ai-agent-direct-structured-gitlab-access-with-glab-cli",{"promotions":764},[765,778,789,801],{"id":766,"categories":767,"header":768,"text":769,"button":770,"image":775},"ai-modernization",[11],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":771,"config":772},"Get your AI maturity score",{"href":773,"dataGaName":774,"dataGaLocation":250},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":779,"categories":780,"header":781,"text":769,"button":782,"image":786},"devops-modernization",[19,580],"Are you just managing tools or shipping innovation?",{"text":783,"config":784},"Get your DevOps maturity score",{"href":785,"dataGaName":774,"dataGaLocation":250},"/assessments/devops-modernization-assessment/",{"config":787},{"src":788},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":790,"categories":791,"header":793,"text":769,"button":794,"image":798},"security-modernization",[792],"security","Are you trading speed for security?",{"text":795,"config":796},"Get your security maturity score",{"href":797,"dataGaName":774,"dataGaLocation":250},"/assessments/security-modernization-assessment/",{"config":799},{"src":800},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":802,"paths":803,"header":806,"text":807,"button":808,"image":813},"github-azure-migration",[804,805],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":809,"config":810},"See how GitLab compares to GitHub",{"href":811,"dataGaName":812,"dataGaLocation":250},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":814},{"src":788},{"header":816,"blurb":817,"button":818,"secondaryButton":823},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":819,"config":820},"Get your free trial",{"href":821,"dataGaName":49,"dataGaLocation":822},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":516,"config":824},{"href":53,"dataGaName":54,"dataGaLocation":822},1777934822108]