[{"data":1,"prerenderedAt":827},["ShallowReactive",2],{"/en-us/blog/ai-in-action-hackathon-celebrating-the-gitlab-innovations":3,"navigation-en-us":40,"banner-en-us":460,"footer-en-us":470,"blog-post-authors-en-us-Nick Veenhof":711,"blog-related-posts-en-us-ai-in-action-hackathon-celebrating-the-gitlab-innovations":726,"blog-promotions-en-us":765,"next-steps-en-us":817},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":24,"description":17,"extension":25,"externalUrl":26,"featured":15,"heroImage":19,"isFeatured":15,"meta":27,"navigation":15,"path":28,"publishedDate":24,"rawbody":29,"seo":30,"slug":14,"stem":33,"tagSlugs":34,"tags":38,"template":13,"updatedDate":26,"__hash__":39},"blogPosts/en-us/blog/ai-in-action-hackathon-celebrating-the-gitlab-innovations.md","AI in Action Hackathon:  Celebrating the GitLab innovations ",[7],"nick-veenhof",[9],"Nick Veenhof","The AI in Action Hackathon offered a compelling opportunity for developers to explore artificial intelligence. Running from May 6 to June 17, 2025, participants developed AI solutions and competed for a $50,000 prize pool. You can find more details about the contest and [explore the projects](https://ai-in-action.devpost.com/project-gallery).\n\nThis hackathon stood out because of a unique collaborative effort, bringing together Google Cloud, MongoDB, and GitLab. The aim was to cultivate an environment for AI development by combining Google Cloud's AI and cloud tools, MongoDB's intelligent data platform for AI, and GitLab's intelligent DevSecOps platform to ship more secure software faster with AI. This partnership allowed developers to integrate these powerful tools, reflecting real-world project dynamics.\n\nThis initiative sought to propel the developer community's growth, and collaboratively shape the future of DevSecOps. GitLab's specific focus in this hackathon was to inspire the creation of AI-enabled applications leveraging both GitLab and Google Cloud. Submissions were encouraged to include contributions to GitLab's product or develop functional components for the [GitLab CI/CD Catalog](https://gitlab.com/explore/catalog). \n\nUltimately, the AI in Action Hackathon became a vibrant stage for developer innovation. It ignited fresh ideas and equipped participants with tangible gains, including new skills, impactful projects for their portfolios, and new professional connections.\n## Meet the winners: AI in action with GitLab\n\nCongratulations to all participants, and specifically to the contest winners. Here's a highlight of the projects that stood out for their deep GitLab integration.\n\n**[Pipeline Doctor: Proactive health for your CI/CD](https://devpost.com/software/pipeline-doctor)**\n*\"As a software engineer, I frequently run into failed GitLab pipelines, often accompanied by cryptic and overwhelming logs. Pinpointing the root cause feels like searching for a needle in a haystack. Debugging becomes even more time-consuming when I have to rely on SREs for support.\" - the project's author*\n\nPipeline Doctor addresses this by using AI for advanced root cause analysis, swiftly diagnosing pipeline anomalies. It analyzes logs and changes to pinpoint errors, and could even explain security issues or predict bottlenecks. This means substantial productivity gains for developers, reclaiming time from troubleshooting to focus on new features. It also makes pipelines more reliable, aligning with goals for 80% faster CI builds and 90% less system maintenance. This project signifies a shift from reactive troubleshooting to proactive health monitoring. \n\nA truly impressive step towards more resilient pipelines.\n\n**[Agentic CICD: The future of automated DevSecOps](https://devpost.com/software/agentic-cicd)**\n\n*\"What if AI agents could handle most of the DevOps workload?”- the project’s author*\n\nAgentic CICD is set to profoundly elevate DevSecOps practices by automating code reviews, suggesting intelligent fixes, and optimizing testing and deployment decisions. These agents can evaluate real-time metrics, automate releases, and even initiate rollbacks without immediate human intervention, creating a self-improving feedback loop. This approach also enhances security by proactively identifying risks. The advantages for development teams are tangible: increased productivity, consistently higher software quality, and improved operational efficiency, accelerating development cycles and time-to-market. Agentic CICD cultivates a pipeline capable of *self-healing* and *self-optimization*, amplifying developer capabilities by automating routine tasks and providing intelligent insights. \n\nThis project truly showcases the next generation of intelligent automation.\n\n**[Agent Anansi: Your intelligent companion in GitLab](https://devpost.com/software/devgenius)**\n\n*“As someone deeply passionate about DevOps and AI, I was frustrated by the fragmented and reactive nature of traditional CI/CD workflows. While automation is widespread, intelligence is often lacking.“ -  the project's author*\n\nAgent Anansi, a name evoking the clever and resourceful spider from folklore, appears to be a versatile AI agent designed to enhance various GitLab workflows beyond the confines of CI/CD. GitLab's broader vision for AI agents includes systems that mirror familiar team roles and serve as foundational building blocks for highly customized agents. This intelligent companion is poised to enhance GitLab workflows by automating repetitive tasks like issue categorization, optimizing search functions, and performing intelligent data analysis. Similar to GitLab Duo's Chat Agent, Anansi could process natural language requests for information or debugging assistance. A compelling application could be an \"AI mentor\" suggesting personalized learning paths. The overall impact on collaboration and efficiency would be substantial, improving developer experience by minimizing manual tasks and reducing context-switching. It would also enhance collaboration by providing instant access to documentation and enabling direct actions through intelligent interaction. Agent Anansi functions as a personalized productivity co-pilot, moving beyond generic tool assistance to a truly personalized experience that increases individual developer efficiency and reduces cognitive load. \n\nA fantastic example of AI making daily development work smarter and more intuitive.\n\n## The power of partnership: Google Cloud, MongoDB, and GitLab fuel innovation\n\nThe AI in Action Hackathon underscored the potency of strategic partnerships in driving innovation. Google Cloud served as a foundational pillar, providing its advanced AI tools, machine learning capabilities, and extensive cloud computing resources as the bedrock for all hackathon projects. MongoDB offered the indispensable intelligent data layer, and GitLab provided the DevSecOps platform essential for building, securing, and deploying these sophisticated AI-enabled applications. Participants were granted access to these powerful tools through free trials or credits, reducing the barriers for experimentation.\n\nThe collaborative synergy among these partners was unmistakable in the multipartner structure of the hackathon. This environment allowed participants to explore a wide array of technologies and integration possibilities, enabling them to create innovative projects that addressed real-world problems. \n\n## Getting to know GitLab's Duo Agent Platform\n\nGitLab is reimagining software development, charting a future where humans and AI collaborate seamlessly. [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) allows users to build, customize, and connect AI agents to match their workflow. Developers are empowered to focus on strategic, creative challenges, as AI agents adeptly manage routine tasks such as providing project status updates, bug fixes, and code reviews concurrently.\n\n[Duo Agent Platform is now in public beta](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/) for GitLab Premium and Ultimate customers on GitLab.com and self-managed environments.\n\n[AI agents](https://about.gitlab.com/topics/agentic-ai/) on the platform leverage comprehensive context from your GitLab projects, code, and requirements. They can also interoperate with other applications or data sources for expanded context and actionable assistance. The platform delivers extensible, customizable agentic AI: Users can create and customize agents and agentic flows that understand their specific work processes and organizational needs. Custom rules can be defined in natural language, ensuring agents perform precisely as configured. A catalog for custom skills, agents, and flows is also planned for future release.\n\nDuo Agent Platform is seamlessly integrated into your workflow, available in your IDE (Integrated Development Environment) or GitLab’s web UI. It currently supports VS Code and the JetBrains family of IDEs, with Visual Studio support planned. This ability to set custom rules for agents, such as specific formatting for code or adherence to language versions, is poised to accelerate reviews and enable swifter deployment of consistent, secure code.\n\nTo get started, GitLab.com customers need to activate GitLab Duo beta features for their group, while self-managed customers need to enable these features for their GitLab Self-Managed instance. For those who are not yet GitLab customers, [a GitLab Ultimate trial](https://about.gitlab.com/free-trial/devsecops/), including Duo Agent Platform, is available at no cost.\n\n## Join the AI revolution: What's next for developers\n\nThe AI in Action Hackathon vividly showcased the transformative potential of artificial intelligence when applied to real-world software development challenges. For developers inspired by these breakthroughs, the journey into AI-powered DevSecOps has just started. Users are encouraged to explore and harness the power of [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), which is engineered to substantially elevate productivity, enhance operational efficiency, and reduce security risks across the software development lifecycle. GitLab Duo offers a suite of integrated features, including intelligent Code Suggestions, an interactive Chat agent, AI-assisted Root Cause Analysis for CI/CD failures, and clear explanations for security vulnerabilities — all directly accessible within the platform.\n\nBeyond utilizing these powerful tools, developers are invited to contribute actively to the vibrant [GitLab community](https://about.gitlab.com/community/). This hackathon is an integral part of GitLab's broader community engagement initiative, which encourages contributions to [GitLab's open source community](https://about.gitlab.com/community/). By contributing, developers can directly shape the platform that millions use to deliver software faster and more securely. As a testament to GitLab's commitment to its community, contributors benefit from the very AI-powered tools, such as GitLab Duo, that they help build. Furthermore, GitLab recognizes and rewards community contributions through various programs, including the monthly Notable Contributor initiative and special recognition for Hackathon winners.\n\nThe AI in Action Hackathon showcased how a robust trust infrastructure, combined with emerging AI use cases, is forging a path toward a more trustworthy and efficient digital future. GitLab is dedicated to accelerating the monthly delivery of potent new AI features, with a clear strategic trajectory toward becoming a premier agent orchestration platform. GitLab is poised to empower users to craft, tailor, and disseminate complex agent flows, enabling highly automated and intelligent workflows. The landscape of software development is rapidly transforming, becoming progressively autonomous, adaptive, and AI-driven.\n\nI can’t wait to see what you will build next with GitLab!","ai-ml",{"template":13,"slug":14,"featured":15},"BlogPost","ai-in-action-hackathon-celebrating-the-gitlab-innovations",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":24,"body":10},"Uncover breakthroughs from this AI development showcase that combined Google Cloud, MongoDB, and GitLab.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664458/Blog/Hero%20Images/Gartner_AI_Code_Assistants_Blog_Post_Cover_Image_1800x945.png",[21,22,23],"open source","AI/ML","CI/CD","2025-08-05","md",null,{},"/en-us/blog/ai-in-action-hackathon-celebrating-the-gitlab-innovations","---\nseo:\n  config:\n    noIndex: false\n  title: 'AI in Action Hackathon:  Celebrating the GitLab innovations '\n  description: Uncover breakthroughs from this AI development showcase that\n    combined Google Cloud, MongoDB, and GitLab.\ntitle: 'AI in Action Hackathon:  Celebrating the GitLab innovations '\ndescription: Uncover breakthroughs from this AI development showcase that combined Google Cloud, MongoDB, and GitLab.\nauthors:\n  - Nick Veenhof\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664458/Blog/Hero%20Images/Gartner_AI_Code_Assistants_Blog_Post_Cover_Image_1800x945.png\ntags:\n  - open source\n  - AI/ML\n  - CI/CD\ncategory: ai-ml\ndate: '2025-08-05'\nslug: ai-in-action-hackathon-celebrating-the-gitlab-innovations\nfeatured: true\ntemplate: BlogPost\n---\n\nThe AI in Action Hackathon offered a compelling opportunity for developers to explore artificial intelligence. Running from May 6 to June 17, 2025, participants developed AI solutions and competed for a $50,000 prize pool. You can find more details about the contest and [explore the projects](https://ai-in-action.devpost.com/project-gallery).\n\nThis hackathon stood out because of a unique collaborative effort, bringing together Google Cloud, MongoDB, and GitLab. The aim was to cultivate an environment for AI development by combining Google Cloud's AI and cloud tools, MongoDB's intelligent data platform for AI, and GitLab's intelligent DevSecOps platform to ship more secure software faster with AI. This partnership allowed developers to integrate these powerful tools, reflecting real-world project dynamics.\n\nThis initiative sought to propel the developer community's growth, and collaboratively shape the future of DevSecOps. GitLab's specific focus in this hackathon was to inspire the creation of AI-enabled applications leveraging both GitLab and Google Cloud. Submissions were encouraged to include contributions to GitLab's product or develop functional components for the [GitLab CI/CD Catalog](https://gitlab.com/explore/catalog). \n\nUltimately, the AI in Action Hackathon became a vibrant stage for developer innovation. It ignited fresh ideas and equipped participants with tangible gains, including new skills, impactful projects for their portfolios, and new professional connections.\n## Meet the winners: AI in action with GitLab\n\nCongratulations to all participants, and specifically to the contest winners. Here's a highlight of the projects that stood out for their deep GitLab integration.\n\n**[Pipeline Doctor: Proactive health for your CI/CD](https://devpost.com/software/pipeline-doctor)**\n*\"As a software engineer, I frequently run into failed GitLab pipelines, often accompanied by cryptic and overwhelming logs. Pinpointing the root cause feels like searching for a needle in a haystack. Debugging becomes even more time-consuming when I have to rely on SREs for support.\" - the project's author*\n\nPipeline Doctor addresses this by using AI for advanced root cause analysis, swiftly diagnosing pipeline anomalies. It analyzes logs and changes to pinpoint errors, and could even explain security issues or predict bottlenecks. This means substantial productivity gains for developers, reclaiming time from troubleshooting to focus on new features. It also makes pipelines more reliable, aligning with goals for 80% faster CI builds and 90% less system maintenance. This project signifies a shift from reactive troubleshooting to proactive health monitoring. \n\nA truly impressive step towards more resilient pipelines.\n\n**[Agentic CICD: The future of automated DevSecOps](https://devpost.com/software/agentic-cicd)**\n\n*\"What if AI agents could handle most of the DevOps workload?”- the project’s author*\n\nAgentic CICD is set to profoundly elevate DevSecOps practices by automating code reviews, suggesting intelligent fixes, and optimizing testing and deployment decisions. These agents can evaluate real-time metrics, automate releases, and even initiate rollbacks without immediate human intervention, creating a self-improving feedback loop. This approach also enhances security by proactively identifying risks. The advantages for development teams are tangible: increased productivity, consistently higher software quality, and improved operational efficiency, accelerating development cycles and time-to-market. Agentic CICD cultivates a pipeline capable of *self-healing* and *self-optimization*, amplifying developer capabilities by automating routine tasks and providing intelligent insights. \n\nThis project truly showcases the next generation of intelligent automation.\n\n**[Agent Anansi: Your intelligent companion in GitLab](https://devpost.com/software/devgenius)**\n\n*“As someone deeply passionate about DevOps and AI, I was frustrated by the fragmented and reactive nature of traditional CI/CD workflows. While automation is widespread, intelligence is often lacking.“ -  the project's author*\n\nAgent Anansi, a name evoking the clever and resourceful spider from folklore, appears to be a versatile AI agent designed to enhance various GitLab workflows beyond the confines of CI/CD. GitLab's broader vision for AI agents includes systems that mirror familiar team roles and serve as foundational building blocks for highly customized agents. This intelligent companion is poised to enhance GitLab workflows by automating repetitive tasks like issue categorization, optimizing search functions, and performing intelligent data analysis. Similar to GitLab Duo's Chat Agent, Anansi could process natural language requests for information or debugging assistance. A compelling application could be an \"AI mentor\" suggesting personalized learning paths. The overall impact on collaboration and efficiency would be substantial, improving developer experience by minimizing manual tasks and reducing context-switching. It would also enhance collaboration by providing instant access to documentation and enabling direct actions through intelligent interaction. Agent Anansi functions as a personalized productivity co-pilot, moving beyond generic tool assistance to a truly personalized experience that increases individual developer efficiency and reduces cognitive load. \n\nA fantastic example of AI making daily development work smarter and more intuitive.\n\n## The power of partnership: Google Cloud, MongoDB, and GitLab fuel innovation\n\nThe AI in Action Hackathon underscored the potency of strategic partnerships in driving innovation. Google Cloud served as a foundational pillar, providing its advanced AI tools, machine learning capabilities, and extensive cloud computing resources as the bedrock for all hackathon projects. MongoDB offered the indispensable intelligent data layer, and GitLab provided the DevSecOps platform essential for building, securing, and deploying these sophisticated AI-enabled applications. Participants were granted access to these powerful tools through free trials or credits, reducing the barriers for experimentation.\n\nThe collaborative synergy among these partners was unmistakable in the multipartner structure of the hackathon. This environment allowed participants to explore a wide array of technologies and integration possibilities, enabling them to create innovative projects that addressed real-world problems. \n\n## Getting to know GitLab's Duo Agent Platform\n\nGitLab is reimagining software development, charting a future where humans and AI collaborate seamlessly. [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) allows users to build, customize, and connect AI agents to match their workflow. Developers are empowered to focus on strategic, creative challenges, as AI agents adeptly manage routine tasks such as providing project status updates, bug fixes, and code reviews concurrently.\n\n[Duo Agent Platform is now in public beta](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/) for GitLab Premium and Ultimate customers on GitLab.com and self-managed environments.\n\n[AI agents](https://about.gitlab.com/topics/agentic-ai/) on the platform leverage comprehensive context from your GitLab projects, code, and requirements. They can also interoperate with other applications or data sources for expanded context and actionable assistance. The platform delivers extensible, customizable agentic AI: Users can create and customize agents and agentic flows that understand their specific work processes and organizational needs. Custom rules can be defined in natural language, ensuring agents perform precisely as configured. A catalog for custom skills, agents, and flows is also planned for future release.\n\nDuo Agent Platform is seamlessly integrated into your workflow, available in your IDE (Integrated Development Environment) or GitLab’s web UI. It currently supports VS Code and the JetBrains family of IDEs, with Visual Studio support planned. This ability to set custom rules for agents, such as specific formatting for code or adherence to language versions, is poised to accelerate reviews and enable swifter deployment of consistent, secure code.\n\nTo get started, GitLab.com customers need to activate GitLab Duo beta features for their group, while self-managed customers need to enable these features for their GitLab Self-Managed instance. For those who are not yet GitLab customers, [a GitLab Ultimate trial](https://about.gitlab.com/free-trial/devsecops/), including Duo Agent Platform, is available at no cost.\n\n## Join the AI revolution: What's next for developers\n\nThe AI in Action Hackathon vividly showcased the transformative potential of artificial intelligence when applied to real-world software development challenges. For developers inspired by these breakthroughs, the journey into AI-powered DevSecOps has just started. Users are encouraged to explore and harness the power of [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/), which is engineered to substantially elevate productivity, enhance operational efficiency, and reduce security risks across the software development lifecycle. GitLab Duo offers a suite of integrated features, including intelligent Code Suggestions, an interactive Chat agent, AI-assisted Root Cause Analysis for CI/CD failures, and clear explanations for security vulnerabilities — all directly accessible within the platform.\n\nBeyond utilizing these powerful tools, developers are invited to contribute actively to the vibrant [GitLab community](https://about.gitlab.com/community/). This hackathon is an integral part of GitLab's broader community engagement initiative, which encourages contributions to [GitLab's open source community](https://about.gitlab.com/community/). By contributing, developers can directly shape the platform that millions use to deliver software faster and more securely. As a testament to GitLab's commitment to its community, contributors benefit from the very AI-powered tools, such as GitLab Duo, that they help build. Furthermore, GitLab recognizes and rewards community contributions through various programs, including the monthly Notable Contributor initiative and special recognition for Hackathon winners.\n\nThe AI in Action Hackathon showcased how a robust trust infrastructure, combined with emerging AI use cases, is forging a path toward a more trustworthy and efficient digital future. GitLab is dedicated to accelerating the monthly delivery of potent new AI features, with a clear strategic trajectory toward becoming a premier agent orchestration platform. GitLab is poised to empower users to craft, tailor, and disseminate complex agent flows, enabling highly automated and intelligent workflows. 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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 — 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 — 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 — 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).**",[22,737],"product",{"featured":15,"template":13,"slug":739},"atlassian-will-train-on-your-data-opt-out-with-gitlab",{"content":741,"config":750},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"body":748,"category":11,"tags":749},"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.",[745],"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/).",[22,737,287],{"featured":15,"template":13,"slug":751},"gitlab-and-anthropic-governed-ai-for-enterprise-development",{"content":753,"config":763},{"title":754,"description":755,"authors":756,"heroImage":758,"date":759,"body":760,"category":11,"tags":761},"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.",[757],"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 — 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 — 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 — 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 — 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 — 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 • milestone: 17.10 • 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 — Pipeline fails on merge to main (opened 5 days ago)\n 2. #903 — 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 — 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 — 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) — 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",[22,737,762],"tutorial",{"featured":15,"template":13,"slug":764},"give-your-ai-agent-direct-structured-gitlab-access-with-glab-cli",{"promotions":766},[767,780,791,803],{"id":768,"categories":769,"header":770,"text":771,"button":772,"image":777},"ai-modernization",[11],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":773,"config":774},"Get your AI maturity score",{"href":775,"dataGaName":776,"dataGaLocation":250},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":781,"categories":782,"header":783,"text":771,"button":784,"image":788},"devops-modernization",[737,579],"Are you just managing tools or shipping innovation?",{"text":785,"config":786},"Get your DevOps maturity score",{"href":787,"dataGaName":776,"dataGaLocation":250},"/assessments/devops-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":792,"categories":793,"header":795,"text":771,"button":796,"image":800},"security-modernization",[794],"security","Are you trading speed for security?",{"text":797,"config":798},"Get your security maturity score",{"href":799,"dataGaName":776,"dataGaLocation":250},"/assessments/security-modernization-assessment/",{"config":801},{"src":802},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":804,"paths":805,"header":808,"text":809,"button":810,"image":815},"github-azure-migration",[806,807],"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":811,"config":812},"See how GitLab compares to GitHub",{"href":813,"dataGaName":814,"dataGaLocation":250},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":816},{"src":790},{"header":818,"blurb":819,"button":820,"secondaryButton":825},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":821,"config":822},"Get your free trial",{"href":823,"dataGaName":50,"dataGaLocation":824},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":516,"config":826},{"href":54,"dataGaName":55,"dataGaLocation":824},1777934918623]