[{"data":1,"prerenderedAt":816},["ShallowReactive",2],{"/en-us/blog/what-is-a-large-language-model-llm":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Itzik Gan Baruch":698,"blog-related-posts-en-us-what-is-a-large-language-model-llm":712,"blog-promotions-en-us":754,"next-steps-en-us":806},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/what-is-a-large-language-model-llm.yml","What Is A Large Language Model Llm",[7],"itzik-gan-baruch",null,"ai-ml",{"slug":11,"featured":12,"template":13},"what-is-a-large-language-model-llm",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"What is a large language model (LLM)?","Learn how large language models work, their applications, and their impact on the DevSecOps world.",[18],"Itzik Gan Baruch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749660057/Blog/Hero%20Images/LLM.jpg","2025-05-29","Large language models (LLMs) are revolutionizing DevOps and DevSecOps approaches by simplifying complex tasks, such as code creation, log analysis, and vulnerability detection.\n\nIn this article, you will learn how LLMs work, their practical applications, and the main challenges to overcome in order to fully harness their potential.\n\n## What is an LLM?\n\nLLMs are artificial intelligence (AI) systems that can process and generate text autonomously. They are trained by analyzing vast amounts of data from a variety of sources, enabling them to master the linguistic structures, contextual relationships, and nuances of language.\n\nLLMs are a major breakthrough in the field of AI. Their ability to process, generate, and interpret text relies on sophisticated machine learning and natural language processing (NLP) techniques. These systems do not just process individual words; they analyze complex sequences to capture the overall meaning, subtle contexts, and linguistic nuances.\n\n## How do LLMs work?\n\nTo better understand how they work, let's explore some of the key features of large language models.\n\n### Supervised and unsupervised learning\n\nLLMs are trained using two complementary approaches: supervised learning and unsupervised learning. These two approaches to machine learning maximize their ability to analyze and generate text.\n\n* **Supervised learning** relies on labeled data, where each input is associated with an expected output. The model learns to associate these inputs with the correct outputs by adjusting its internal parameters to reduce prediction errors. Through this approach, the model acquires precise knowledge about specific tasks, such as text classification or named entity recognition.\n\n* **Unsupervised learning (or machine learning)**, on the other hand, does not require labeled data. The model explores large volumes of text to discover hidden structures and identify semantic relationships. The model is therefore able to learn recurring patterns, implicit grammatical rules in the text, and contextualization of sentences and concepts. This method allows LLMs to be trained on large corpora of data, greatly accelerating their progress without direct human action.\n\nBy combining these two approaches, large language models gain the advantages of both precise, human-guided learning and unlimited autonomous exploration. This complementarity allows them to develop rapidly, while continuously improving their ability to understand and generate text coherently and contextually.\n\n### Learning based on a large volume of data\n\nLLMs are trained on billions of sentences from a variety of sources, such as news articles, online forums, technical documentation, scientific studies, and more. This variety of sources allows them to acquire a broad and nuanced understanding of natural language, ranging from everyday expressions to specialized terminology.\n\nThe richness of the data used is a key factor in LLMs' performance. Each source brings different writing styles, cultural contexts, and levels of technicality. \n\nFor example:\n\n* **News articles** to master informative and factual language \n* **Online forums** to understand specialized communities' informal conversations and technical language  \n* **Technical documentation and scientific studies** to assimilate complex concepts and specific terminology, particularly in areas such as DevOps and DevSecOps\n\nThis diversity of content allows LLMs to recognize complex linguistic structures, interpret sentences in different contexts, and adapt to highly technical domains. In DevSecOps, this means understanding commands, configurations, security protocols, and even concepts related to the development and maintenance of computer systems.\n\nWith this large-scale training, LLMs can accurately answer complex questions, write technical documentation, or identify vulnerabilities in computer systems.\n\n### Neural network architecture and \"deep learning\"\n\nLLMs are based on advanced neural network architectures. These networks are specially designed to process large sequences of text while maintaining an accurate understanding of the context. This deep learning-based training is a major asset in the field of NLP.\n\nThe best-known of these structures is the architecture of sequence-to-sequence models (transformers). This architecture has revolutionized NLP with its ability to simultaneously analyze all parts of a text, unlike sequential approaches that process words one by one.\n\nSequence-to-sequence models excel at processing long texts. For example, in a conversation or a detailed technical document, they are able to link distant information in the text to produce precise and well-reasoned answers. This context management is essential in a DevSecOps approach, where instructions can be complex and spread over multiple lines of code or configuration steps.\n\n### Predictive text generation\n\nWhen the user submits a text, query, or question, an LLM uses its predictive ability to generate the most likely sequence, based on the context provided.\n\nThe model analyzes each word, studies grammatical and semantic relationships, and then selects the most suitable terms to produce a coherent and informative text. This approach makes it possible to generate precise, detailed responses adapted to the expected tone.\n\nIn DevSecOps environments, this capability becomes particularly useful for:\n\n* **Coding assistance:** generation of code blocks or scripts adapted to specific configurations\n* **Technical problem solving:** proposing solutions based on descriptions of bugs or errors\n* **Drafting technical documentation:** automatic creation of guides, manuals, or instructions\n\nPredictive text generation thus makes it possible to automate many repetitive tasks and speed up technical teams' work.\n\n## Applications of large language models in a DevSecOps approach\n\nWith the rise of automation, LLMs have become indispensable allies for technical teams. Their ability to understand and generate text contextually enables them to effectively operate in complex environments such as [DevSecOps](https://about.gitlab.com/topics/devsecops/).\n\nWith their analytical power and ability to adapt to specific needs, these models offer tailored solutions to streamline processes and lighten technical teams' workload.\n\nDevelopment teams can leverage LLMs to automatically transform functional specifications into source code. \n\nWith this capability, they can perform the following actions:\n- generate complex automation scripts\n- create CI/CD pipelines tailored to specific business processes\n- produce customized security patches\n- generate code explanation and create documentation\n- refactor code by improving code structure and readability without changing functionality\n- generate tests\n\nBy relying on LLMs, teams are able to accelerate the development of their software while reducing the risk of human error.\n\n### Improved documentation and knowledge sharing\n\nThese powerful tools make it easy to create customized user manuals, API descriptions, and tutorials that are perfectly tailored to each user's level of expertise. By leveraging existing knowledge bases, LLMs create contextual answers to frequently asked questions. This enhances knowledge sharing within teams, speeds up onboarding of new members, and helps centralize best practices.\n\n### Incident management and troubleshooting\nDuring an incident, LLMs play a crucial role in analyzing logs and [trace files](https://docs.gitlab.com/ee/development/tracing.html) in real time. Thanks to their ability to cross-reference information from multiple sources, they identify anomalies and propose solutions based on similar past incidents. This approach significantly reduces diagnosis time. In addition, LLMs can automate the creation of detailed incident reports and recommend specific corrective actions.\n\n### Creating and improving CI/CD pipelines\n\nLLMs are revolutionizing the configuration of [CI/CD pipelines](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/). They can not only help create pipelines, but also automate this process and suggest optimal configurations based on industry standards. By adapting workflows to your specific needs, they ensure perfect consistency between different development environments. Automated testing is enhanced by relevant suggestions, limiting the risk of failure. LLMs also continuously monitor the efficiency of pipelines and adjust processes to ensure smooth and uninterrupted rollout.\n\n### Security and compliance\n\nIn a DevSecOps environment, large language models become valuable allies for security and compliance. They parse the source code for potential vulnerabilities and generate detailed patch recommendations. LLMs can also monitor the application of security standards in real time, produce comprehensive compliance reports, and automate the application of security patches as soon as a vulnerability is identified. This automation enhances overall security and ensures consistent compliance with legal and industry requirements.\n\n## What are the benefits of large language models?\n\nLLMs are radically reshaping DevOps and DevSecOps approaches, bringing substantial improvements in productivity, security, and software quality. By integrating with existing workflows, LLMs are disrupting traditional approaches by automating complex tasks and providing innovative solutions.\n\n### Improved productivity and efficiency\n\nLLMs play a central role in improving technical teams' productivity and efficiency. By automating a wide range of repetitive tasks, they free development teams from routine operations, allowing them to focus on strategic activities with higher added value.\n\nIn addition, LLMs act as intelligent technical assistants capable of instantly providing relevant code snippets, tailored to the specific context of each project. In this way, they significantly reduce research time by offering ready-to-use solutions to assist teams in their work. This targeted assistance speeds up problem solving and reduces disruptions in workflows.\nAs a result, productivity increases and projects move forward more quickly. Technical teams can take on more tasks without compromising the quality of deliverables.\n\n### Improved code quality and security\n\nThe use of large language models in software development is a major lever for improving both code quality and application security. With their advanced analytical capabilities, LLMs can scan source code line by line and instantly detect syntax errors, logical inconsistencies, and potential vulnerabilities. Their ability to recognize defective code allows them to recommend appropriate fixes that comply with industry best practices.\n\nLLMs also play a key preventive role. They excel at identifying complex security flaws that are often difficult for humans to detect. By analyzing dependencies, they can flag obsolete or vulnerable libraries and recommend more secure, up-to-date versions. This approach contributes to maintaining a secure environment that complies with current security standards.\n\nBeyond fixing existing errors, LLMs offer improvements by suggesting optimized coding practices and project structures. They can generate code that meets the most advanced security standards from the earliest stages of development.\n\n### Accelerating development lifecycles\n\nLarge language models play a key role in accelerating software development lifecycles by automating key tasks that would otherwise tie up valuable human resources. Complex and repetitive tasks, such as writing functions, creating unit tests, or implementing standard components, are automated in a matter of moments.\n\nLLMs also speed up the validation phase with their ability to suggest complete and appropriate test cases. They ensure broader test coverage in less time, reducing the risk of errors and enabling early detection of anomalies. This preventive approach shortens the correction cycle and limits delays related to code quality issues.\n\nBy simplifying technical tasks and providing fast and tailored solutions, large language models enable businesses to respond to market demands in a more agile way. This acceleration of the development lifecycle results in more frequent updates, faster iterations, and a better ability to adapt products to users' changing needs.\n\nDevelopment lifecycles are becoming shorter, providing a critical strategic advantage in an increasingly demanding technology landscape.\n\n## What are the challenges of using LLMs?\n\nDespite their many benefits, large language models have certain limitations that require careful management. Their effectiveness depends heavily on the quality of the data used during their training and regular updates to their knowledge bases. In addition, issues related to algorithmic bias, data security, and privacy can arise, exposing companies to operational and legal risks. Rigorous human oversight remains essential in order to ensure the reliability of results, maintain regulatory compliance, and prevent critical errors.\n\n### Data privacy and security\n\nTraining LLMs relies on large volumes of data, often from diverse sources, raising questions about the protection of confidential information. Sensitive data shared with cloud platforms can therefore be exposed to potential breaches. This is of particular concern to companies operating in regulated sectors. \n\nIn Europe, where strict regulations like GDPR govern data management, many companies are reluctant to transfer their information to external services. Regulatory requirements, coupled with the fear of unauthorized exploitation of sensitive data, have led some companies to opt for self-hosted solutions to maintain complete control over their systems.\n\nProviders like GitLab have put in place robust security guarantees, such as intentional non-retention of personal data and end-to-end encryption. However, this may not be enough for the most demanding customers, who prefer complete control of their environments. Implementing hybrid or on-premises solutions then becomes a strategic necessity to meet the security requirements of certain companies.\n\nLearn more about GitLab Duo Self-Hosted by clicking on the image below to access our product tour.\n\n[![GitLab Duo Self-Hosted tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749673815/Blog/Content%20Images/Screenshot_2025-05-29_at_8.29.30%C3%A2__AM.png)](https://gitlab.navattic.com/gitlab-duo-self-hosted)\n\n### Accuracy and reliability\n\nAlthough large language models are capable of producing impressive results, their performance is not infallible. They can produce incorrect, incomplete, or inconsistent answers. This inaccuracy becomes particularly problematic in the context of critical tasks such as generating security code or analyzing sensitive data.\n\nIn addition, LLMs operate on the basis of probabilistic models, which means that they do not truly \"understand\" the content they process, but produce predictions based on statistical probabilities. This can lead to technically incorrect or even dangerous recommendations when used without human validation.\n\nTo avoid these pitfalls, it is essential to maintain constant oversight and establish rigorous validation processes. The results provided by LLMs must always be reviewed by humans before being integrated into critical systems.\n\nA strategy of regular model updates, combined with proactive human oversight, can reduce errors and gradually improve the reliability of results.\n\n## How GitLab uses LLMs for GitLab Duo features\n\n[GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) harnesses the power of large language models to transform DevSecOps processes by integrating AI-powered capabilities throughout the software development lifecycle. This approach aims to improve productivity, strengthen security, and automate complex tasks so that development teams can focus on high added-value tasks.\n\n### AI-assisted software development\n\nGitLab Duo provides continuous support throughout the software development lifecycle with real-time recommendations. Development teams can automatically generate unit tests, get detailed explanations of complex code segments, and benefit from suggestions to improve the quality of their code.\n\n### Proactive CI/CD failure analysis\n\nOne of the key features of GitLab Duo is its assistance in analyzing CI/CD job failures. With LLM and AI, teams are able to quickly identify sources of errors in their continuous integration and deployment pipelines. \n\n### Enhanced code security\n\nGitLab Duo incorporates AI-based security features. The system detects vulnerabilities in the source code and proposes detailed patches to reduce the risks. Teams receive clear explanations of the nature of the vulnerabilities identified and can apply automated patches via [merge requests](https://docs.gitlab.com/ee/user/project/merge_requests/) generated directly by GitLab Duo. This feature helps secure development without slowing down development lifecycles.\n\nLearn more about GitLab Duo Vulnerability Explanation and Resolution by clicking on the image below to access our product tour.\n\n[![Vulnerability report interactive tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1749673816/Blog/Content%20Images/Screenshot_2025-05-29_at_8.32.15%C3%A2__AM.png)](https://gitlab.navattic.com/ve-vr-short)\n\n### Key features of GitLab Duo\n\n* [GitLab Duo Chat](https://about.gitlab.com/blog/10-best-practices-for-using-ai-powered-gitlab-duo-chat/): This conversational feature processes and generates text and code intuitively. It allows users to quickly search for relevant information in large volumes of text, including in tickets, [epics](https://docs.gitlab.com/ee/user/group/epics/), source code, and [GitLab documentation](https://docs.gitlab.com/).\n\n* [GitLab Duo Self-Hosted](https://about.gitlab.com/blog/gitlab-duo-self-hosted-enterprise-ai-built-for-data-privacy/): GitLab Duo Self-Hosted allows companies with strict data privacy requirements to benefit from GitLab Duo's AI capabilities with flexibility in choosing deployment and LLMs from a list of supported options.\n\n* [GitLab Duo Code Suggestions](https://docs.gitlab.com/user/project/repository/code_suggestions/): Development teams benefit from automated code suggestions, allowing them to write secure code faster. Repetitive and routine coding tasks are automated, significantly speeding up software development lifecycles.\n\nGitLab Duo is not limited to these features. 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Whether it's automating testing, improving collaboration between teams, or strengthening project security, GitLab Duo is a complete solution for smart and efficient DevSecOps processes.\n\nLearn more about GitLab Duo Enterprise by clicking on the image below to access our product tour. \n\n[![GitLab Duo Enterprise interactive 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Copilot's new policy for AI training is a governance wake-up call","Learn what GitHub's Copilot policy change means for regulated industries, and why GitLab's commitment to customer data privacy matters.",[718],"Allie Holland","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776347152/unw3mzatkd5xyfbzcnni.png","2026-04-20","GitHub recently [announced](https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/) a significant change to how it handles data from Copilot users. Starting April 24, 2026, interaction data from Copilot Free, Pro, and Pro+ users, including inputs, outputs, code snippets, and associated context, will be used to train AI models by default, unless users actively opt out. Copilot Business and Enterprise customers are exempt under existing contract terms.\n\nFor organizations in regulated industries, including finance, healthcare, defense, and public sector, the policy shift raises questions that go beyond individual developer preferences. It forces a harder look at a question that engineering and security leaders should be asking every AI vendor in their stack: Do you train on our code? \n\nGitLab's answer is no. GitLab does not train AI models on customer code at any tier, and AI vendors are contractually prohibited from using customer inputs or outputs for their own purposes. The [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) makes that commitment auditable: a single location documenting which models power which features, how data is handled, subprocessor relationships, and data retention periods. The GitLab AI Transparency Center also lists the compliance status of each feature, including confirmation that GitLab's current AI features do not qualify as high-risk systems under the EU AI Act. It's a standard GitLab CEO Bill Staples has consistently [reiterated](https://www.linkedin.com/posts/williamstaples_gitlab-1810-agentic-ai-now-open-to-even-activity-7443280763715985408-aHxf?utm_source=share&utm_medium=member_desktop&rcm=ACoAABsu7EUBcb_a1-JHKS9RC0B5rf8Ye-5XM60) and one reflected in GitLab's mission and [Trust Center](https://trust.gitlab.com/).\n\n## What the policy change actually means\n\nGitHub's announcement also specifies that the data may be shared with GitHub affiliates, including Microsoft, for AI development purposes.\n\nA policy change of this nature forces organizations to re-examine their AI governance posture, audit their Copilot license tiers, and confirm that the right controls are configured across their teams.\n\n## Why AI governance matters in regulated environments\n\nSource code is often among an organization's most sensitive intellectual property. It may contain references to internal systems, reflect proprietary business logic, or touch data flows governed by strict retention and access policies. When that code passes through an AI assistant, questions about training data usage, model vendor relationships, and data residency become compliance concerns.\n\nThe exposure is particularly acute for financial services firms that have invested in proprietary algorithms, fraud detection logic, credit risk models, underwriting rules, trading strategies. When AI tooling processes that code and uses it to train models serving competitors, vendor data practices become an IP concern.\n\nFinancial institutions operating under [the Federal Reserve's Supervisory Guidance on Model Risk Management (SR 11-7) and the](https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm) [Digital Operational Resilience Act (DORA)](https://eur-lex.europa.eu/eli/reg/2022/2554/oj/eng) are required to maintain documented, auditable oversight of third-party technology providers, including understanding how those providers handle data. Third-party AI tools used in development workflows increasingly fall within the scope of model risk oversight, and material changes to vendor data practices require updated documentation. \n\nIn the public sector, [the National Institute of Standards and Technology Special Publication 800-53 (NIST 800-53)](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) and the [Federal Information Security Modernization Act (FISMA)](https://www.cisa.gov/topics/cyber-threats-and-advisories/federal-information-security-modernization-act) establish that sensitive or classified code must never leave a controlled boundary. For U.S. Department of Defense and intelligence community environments in particular, a vendor's default data posture is an operational concern. In healthcare, [the Health Insurance Portability and Accountability Act (HIPAA)](https://www.hhs.gov/hipaa/index.html) governs how patient-adjacent data is handled by third parties, and development environments that touch clinical systems increasingly fall within that scope.\n\nAcross all of these contexts, the common thread is the same: A vendor policy that changes data usage defaults, requires individual opt-out, and offers different protections depending on account tier introduces exactly the kind of uncontrolled variable that compliance teams cannot afford.\n\n## What regulated industries actually need from AI vendors\n\nRegulated organizations have largely moved past debating whether to adopt AI in development workflows. The focus now is on doing so in a way they can defend to regulators, boards, and customers. That shift has surfaced a consistent set of requirements regardless of sector.\n\n**Contractual certainty.** Regulated firms need to know, with specificity, what happens to their data. A clear, documented, unconditional commitment is what's required, not something that varies by plan or requires action before a deadline.\n\n**Auditability.** Model risk management frameworks require organizations to understand and validate the AI systems they deploy, including the training data behind those models and the third parties involved in their development. Vendors who cannot answer these questions create documentation risk for the organizations relying on them.\n\n**Separation from vendor incentives.** When an AI vendor trains models on customer usage data, code and workflows become inputs to a system that also serves competitors. For institutions with proprietary trading logic, underwriting models, or fraud detection systems, that's a genuine IP exposure.\n\n## GitLab's position on AI data governance\n\nGitLab does not use customer code to train AI models. This commitment applies at every tier, and AI vendors are contractually prohibited from using inputs or outputs associated with GitLab customers for their own purposes.\n\nThis is a deliberate architectural and policy choice, not a feature of a particular pricing tier. As GitLab's [post on enterprise independence](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) notes, data governance has become \"an increasingly critical factor in enterprise technology decisions, driven by a complex web of national and regional data protection laws and growing concern about control over sensitive intellectual property.\"\n\nGitLab is also cloud-neutral and model-neutral while supporting self-hosted deployments, not commercially tied to any single cloud provider or large language model (LLM). That i[ndependence matters](https://about.gitlab.com/blog/why-enterprise-independence-matters-more-than-ever-in-devsecops/) for regulated organizations evaluating vendor concentration risk. The [AI Continuity Plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) documents how vendor changes are managed, including material changes to how AI vendors treat customer data, a direct response to the governance requirements under frameworks like [DORA](https://handbook.gitlab.com/handbook/legal/dora/). \n\n## The governance gap AI teams need to close\n\nGitHub's policy update is a reminder that for organizations in regulated industries, understanding exactly how an AI tool handles data is a prerequisite for using it at all. That means asking vendors for clear, documented answers: Is our data used for model training? Who are your AI model subprocessors? What happens if a vendor changes its data practices? Can we deploy in a way that keeps all AI processing within our own infrastructure? What indemnification do you offer for AI-generated output?\n\nVendors who can answer those questions clearly, and document those answers in an auditable form, are vendors you can build on. **Those who cannot will create compliance debt every time they ship a policy update.** And when a vendor can change its data practices with 30 days notice, that's not a partnership built for regulated industries. That's a liability.\n\n> Learn more about GitLab's approach to AI governance at the [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).",[23,723],"product",{"featured":12,"template":13,"slug":725},"github-copilots-new-policy-for-ai-training-is-a-governance-wake-up-call",{"content":727,"config":739},{"title":728,"description":729,"authors":730,"body":733,"heroImage":734,"date":735,"category":9,"tags":736},"GitLab and Vertex AI on Google Cloud: Advancing agentic software development","Learn how Google Cloud customers are standardizing on GitLab and Vertex AI for foundation models, enterprise controls, and Model Garden breadth.\n",[731,732],"Regnard Raquedan","Rajesh Agadi","GitLab Duo Agent Platform is helping redefine how organizations build, secure, and deliver software. Since its general availability in January 2026, the platform is bringing agentic AI to every phase of the software development lifecycle. Duo Agent Platform is an intelligent orchestration layer where software teams, and their specialized agents plan, code, review, and remediate security vulnerabilities together.\n\nThrough this exciting partnership, [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) automates software development orchestration and lifecycle context via its integration with Vertex AI on Google Cloud, which powers the model tier for agent calls. Software teams keep working on issues, merge requests, pipelines, and security workflows while inference follows the Google Cloud posture they already defined. \n\nAdvances in Google Cloud’s Vertex AI models expand how Google Cloud customers can use GitLab Duo Agent Platform in their environment. Customers get an AI-powered DevSecOps control plane in GitLab, backed by a rapidly advancing AI infrastructure foundation in Vertex AI and Duo Agent Platform’s flexible deployment and integration options. The combination enables more capable, governed agentic workflows that operate at enterprise scale.\n\n![Conceptual illustration of the GitLab Duo Agent Platform integrated with Google Cloud's Vertex AI to power agentic software development and governed AI workflows](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776165990/b7jlux9kydafncwy8spc.png)\n\n## Agents that work across the full lifecycle\n\nMany AI tools focus on a single task: generating code faster. GitLab Duo Agent Platform goes further. It orchestrates AI agents across the entire software development lifecycle (SDLC), from planning through security review to delivery, across many teams with many projects and releases. At this scale, AI coding assistants are necessary for continuous innovation but not sufficient. \n\nSingle-purpose coding assistants rarely see the full state of a project. Backlog shape, open merge requests, failing jobs, and security findings live in GitLab, but a separate chat window in a coding assistant does not inherit that full picture of the SDLC. The gap shows up as manual handoffs, duplicate explanations to an AI that lacks context, and governance teams trying to map data flows across tools that were never designed as one system.\n\nGitLab Duo Agent Platform helps close that gap by running agents and flows on the same objects engineers use every day. Vertex AI then supplies the models and services those agents call when Google Cloud is your chosen inference home, with GitLab’s AI Gateway mediating access so administrators keep a clear map of what connects to what. For instance, GitLab Duo Planner Agent analyzes backlogs, breaks epics into structured tasks, and applies prioritization frameworks to help teams decide what to build next. Security Analyst Agent triages vulnerabilities, details risks in plain language, and recommends remediation in priority order. Built-in flows connect these agents into end-to-end processes, without requiring developers to manage every handoff manually.\n\nAgentic Chat in GitLab Duo Agent Platform ties the experience together for developers. They query in natural language to get context-aware responses with multi-step reasoning that draws on the full state of a project: its issues, merge requests, pipelines, security findings, and codebase. Because GitLab serves as the system of record for the SDLC with a unified data model, GitLab Duo agents operate with lifecycle context that falls outside the reach of standalone, tool-specific AI assistants.\n\n### Amplified by Vertex AI\n\nGitLab Duo Agent Platform is designed to be model-flexible, routing different capabilities to different models based on what performs best for a given task. That architectural choice pays off on Google Cloud, where Vertex AI acts as the managed environment for foundation models and related services, providing a broad model ecosystem and managed infrastructure that helps push the platform's capabilities further.\n\nThe latest generations of AI models available through Vertex AI bring significant improvements in reasoning, tool use, and long-context understanding compared to previous iterations — the same properties that GitLab's agents rely on across many projects and teams with large, complex codebases. Longer context windows and richer tool integration in the underlying models expand what agents can accomplish in a single pass, which is especially important for workloads like deep backlog analysis or monorepo security review.\n\n[Vertex AI Model Garden](https://cloud.google.com/model-garden), with access to a wide range of foundation models, gives customers the breadth to make these choices based on performance, cost, and regulatory requirements rather than vendor lock-in.\n\nMoreover, GitLab customers can use Bring Your Own Model (BYOM) for Duo Agent Platform so approved providers and gateways land where your security model expects them. GitLab’s [18.9 launch coverage of self-hosted Duo Agent Platform and BYOM](https://about.gitlab.com/blog/agentic-ai-enterprise-control-self-hosted-duo-agent-platform-and-byom/) describes how that wiring works. With this deployment option, customers gain access to a wider set of model options they can tailor to their software development process: the right model for the right workflow, with the right guardrails.\n\nFor GitLab, the decision to build on Vertex AI was driven by the need for enterprise-grade reliability and unparalleled model breadth. Vertex AI and Model Garden completely abstract the heavy lifting of LLM hosting — meaning rapid version delivery, robust security, and strict governance are seamlessly built into the integration. Beyond offering Gemini models, Vertex AI provides global, low-latency access to a vast catalog of third-party and open-source models. \n\nCombined with Google Cloud's industry-leading approach to data privacy and model protection, Vertex AI emerged as the clear choice to power GitLab's next-generation developer experience. \n\nBy integrating Vertex AI Model Garden into its backend, GitLab supercharges its DevSecOps platform without passing any complexity on to users. Development teams are not burdened with evaluating or managing underlying LLMs; instead, they experience a streamlined, AI-assisted workflow for building their applications. \n\nGitLab completely abstracts cloud orchestration, enabling developers to focus entirely on writing great code, while Vertex AI powers the features and functionality that assist them.\n\n## What this means for customers on Google Cloud\n\nGitLab Duo Agent Platform already delivers AI agents that operate across the full software lifecycle within a single, governed system of record. On Google Cloud, it enables rapid innovation as Vertex AI continues to advance the model and infrastructure layers. \n\nFor Google Cloud customers, this integration means streamlined software delivery while maintaining strict enterprise governance. For platform engineering groups, it means normalizing which Vertex-backed models power suggestions, analysis, and remediation inside GitLab instead of cataloging dozens of client-side tools. Security programs benefit when agents propose and validate fixes in the same place developers already triage findings, cutting context switching and reducing work that would otherwise spill into unmanaged channels.\n\nFrom a cloud economics and policy angle, drawing agent inference toward Vertex from within GitLab keeps usage nearer to the agreements and controls you already run on Google Cloud, which helps avoid duplicate spend and shadow paths that bypass procurement.\n\nBecause Vertex AI is an underlying infrastructure provider for GitLab Duo Agent Platform, organizations are enabled to dramatically lift developer productivity without the overhead and risk of managing fragmented AI toolchains. Teams stay aligned within a single, secure system of record, helping them build applications faster and ship with confidence.\n\nThe GitLab and Google Cloud collaboration has been building since 2018. Today, it represents one of the most comprehensive paths for organizations moving from AI experiments to fully governed, agentic software development on Google Cloud. As both platforms continue to advance — GitLab expanding its agent orchestration and developer context, and Vertex AI pushing the boundaries of model capability and agent infrastructure — the value for joint customers will continue to grow.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/free-trial/) to experience the power of GitLab and Vertex AI on Google Cloud.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2026-04-14",[23,275,737,738,723],"google","news",{"featured":27,"template":13,"slug":740},"gitlab-and-vertex-ai-on-google-cloud",{"content":742,"config":752},{"heroImage":743,"title":744,"description":745,"authors":746,"date":748,"category":9,"tags":749,"body":751},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643639/sapu29gmlgtwvhggmj6k.png","Extend GitLab Duo Agent Platform: Connect any tool with MCP","Learn how to connect external tools to GitLab Duo Agent Platform using MCP. Step-by-step setup with three practical workflow demos.",[747],"Albert Rabassa","2026-03-05",[9,723,750],"tutorial","Managing software development often means juggling multiple tools: tracking issues in Jira, writing code in your IDE, and collaborating through GitLab. Context switching between these platforms disrupts focus and slows down delivery.\n\nWith GitLab Duo Agent Platform's [MCP](https://about.gitlab.com/topics/ai/model-context-protocol/) support, you can now connect Jira or any tool that supports MCP directly to your AI-powered development environment. Query issues, update tickets, and sync your workflow — all through natural language, without ever leaving your IDE.\n\n## What you'll learn\n\nIn this tutorial, we'll walk you through:\n\n* **Setting up the Jira/Atlassian OAuth application** for secure authentication\n* **Configuring GitLab Duo Agent Platform** as an MCP client\n* **Three practical use cases** demonstrating real-world workflows\n\n## Prerequisites\n\nBefore getting started, ensure you have the following:\n\n| Requirement | Details |\n| ---- | ----- |\n| **GitLab instance** | GitLab 18.8+ with Duo Agent Platform enabled |\n| **Jira account** | Jira Cloud instance with admin access to create OAuth applications |\n| **IDE** | Visual Studio Code with GitLab Workflow extension installed |\n| **MCP support** | MCP support enabled in GitLab |\n\n\n## Understanding the architecture\n\nGitLab Duo Agent Platform acts as an **MCP client**, connecting to the Atlassian MCP server to access your Jira project management data. Atlassian  MCP server handles authentication, translates natural language requests into API calls, and returns structured data back to GitLab Duo Agent Platform — all while maintaining security and audit controls.\n\n## Part 1: Configure Jira OAuth application\n\nTo securely connect GitLab Duo Agent Platform to your Jira instance, you'll need to create an OAuth 2.0 application in the Atlassian Developer Console. This grants to GitLab the MCP server authorized access to your Jira data.\n\n### Setup steps\n\nIf you prefer to configure manually, follow these steps:\n\n1. **Navigate to the Atlassian Developer Console**\n\n   * Go to [developer.atlassian.com/console/myapps](https://developer.atlassian.com/console/myapps)\n\n   * Sign in with your Atlassian account\n\n2. **Create a new OAuth 2.0 app**\n\n   * Click **Create** → **OAuth 2.0 integration**\n\n   * Enter a name (e.g., \"gitlab-dap-mcp\")\n\n   * Accept the terms and click **Create**\n\n3. **Configure permissions**\n\n   * Navigate to **Permissions** in the left sidebar.\n\n   * Add **Jira API** and configure the following scopes:\n\n     * `read:jira-work` — Read issues, projects, and boards\n\n     * `write:jira-work` — Create and update issues\n\n     * `read:jira-user` — Read user information\n\n4. **Set up authorization**\n\n   * Go to **Authorization** in the left sidebar\n\n   * Add a callback URL for your environment (`https://gitlab.com/oauth/callback`)\n\n   * Save your changes\n\n5. **Retrieve credentials**\n\n   * Navigate to **Settings**\n\n   * Copy your **Client ID** and **Client Secret**\n\n   * Store these securely — you'll need them for the MCP configuration\n\n\n### Interactive walkthrough: Jira OAuth setup\n\nClick on the image below to get started.\n\n\n[![Jira OAuth setup tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772644850/wnzfoq43nkkfmgdqldmr.png)](https://gitlab.navattic.com/jira-oauth-setup)\n\n\n## Part 2: Configure GitLab Duo Agent Platform MCP client\n\nWith your OAuth credentials ready, you can now configure GitLab Duo Agent Platform to connect to the Atlassian MCP server.\n\n### Create your MCP configuration file\n\nCreate the MCP configuration file in your GitLab project at `.gitlab/duo/mcp.json`:\n\n\n```json\n{\n  \"mcpServers\": {\n    \"atlassian\": {\n      \"type\": \"http\",\n      \"url\": \"https://mcp.atlassian.com/v1/mcp\",\n      \"auth\": {\n        \"type\": \"oauth2\",\n        \"clientId\": \"YOUR_CLIENT_ID\",\n        \"clientSecret\": \"YOUR_CLIENT_SECRET\",\n        \"authorizationUrl\": \"https://auth.atlassian.com/oauth/authorize\",\n        \"tokenUrl\": \"https://auth.atlassian.com/oauth/token\"\n      },\n      \"approvedTools\": true\n    }\n  }\n}\n```\n\nReplace `YOUR_CLIENT_ID` and `YOUR_CLIENT_SECRET` with the credentials you generated in Part 1.\n\n### Enable MCP in GitLab\n\n1. Navigate to your **Group Settings** → **GitLab Duo** → **Configuration**\n2. Make sure “Allow external MCP tools” is checked\n\n### Verify the connection\n\nOpen your project in VS Code and ask in GitLab Duo Agent Platform chat:\n\n```text\nWhat MCP tools do you have access to?\n```\n\nThen\n\n```text\nTest the MCP JIRA configuration in this project\n```\n\nAt this point you'll be redirected from the IDE to the MCP Atlassian website to approve access:\n\n![Redirect to MCP Atlassian website](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/z5acqjgguh0damnnde9g.png \"Redirect to MCP Atlassian website\")\n\n\u003Cbr>\u003C/br>\n\n![Approve access](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/rwowamm8nsubhpixtn3i.png \"Approve access\")\n\n\u003Cbr>\u003C/br>\n\n![Select your JIRA instance and approve](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643461/chuzqd0jeptfwvoj7wjr.png \"Select your JIRA instance and approve\")\n\n\u003Cbr>\u003C/br>\n\n![Success!](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/bsgti5iste2bzck19o5y.png \"Success!\")\n\n\u003Cbr>\u003C/br>\n\n### Verify with the MCP Dashboard\n\nGitLab also provides a built-in **MCP Dashboard** directly in your IDE for this.\n\nIn VS Code or VSCodium, open the Command Palette (`Cmd+Shift+P` on macOS, `Ctrl+Shift+P` on Windows/Linux) and search for **\"GitLab: Show MCP Dashboard\"**. The dashboard opens in a new editor tab and gives you:\n\n* **Connection status** for each configured MCP server\n* **Available tools** exposed by the server (e.g., `jira_get_issue`, `jira_create_issue`)\n* **Server logs** so you can see exactly which tools are being called in real time\n\n![MCP servers dashboard and status](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/mmvdfchucacsydivowvn.png \"MCP servers dashboard and status\")\n\n\u003Cbr>\u003C/br>\n\n![Server details and permissions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643462/tcocgdvovp2dl42pvfn8.png \"Server details and permissions\")\n\n\u003Cbr>\u003C/br>\n\n\n![MCP Server logs](https://res.cloudinary.com/about-gitlab-com/image/upload/v1772643466/mougvqqk1bozchaufsci.png \"MCP Server logs\")\n\n\u003Cbr>\u003C/br>\n\n### Interactive walkthrough: Testing MCP\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005495?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Testing MCP\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Part 3: Use cases in action\n\nNow that your integration is configured, let's explore three practical workflows that demonstrate the power of connecting Jira to GitLab Duo Agent Platform.\n\n### Planning assistant\n\n**Scenario:** You're preparing for sprint planning and need to quickly assess the backlog, understand priorities, and identify blockers.\n\nThis demo shows you how to:\n\n* Query the backlog\n* Identify unassigned high-priority issues\n* Get AI-powered sprint recommendations\n\n#### Example prompts\n\nTry these prompts in GitLab Duo Agent Platform Chat:\n\n```text\nList all the unassigned issues in JIRA for project GITLAB\n```\n\n```text\nSuggest the two top issues to prioritize and summarize them. Assign them to me.\n```\n\n### Interactive walkthrough: Project planning\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005462?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Project Planning\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player. js\">\u003C/script>\n\n### Issue triage and creation from code\n\n**Scenario:** While reviewing code, you discover a bug and want to create a Jira issue with relevant context — without leaving your IDE.\n\nThis demo walks you through:\n\n* Identifying a bug while coding\n* Creating a detailed Jira issue via natural language\n* Auto-populating issue fields with code context\n* Linking the issue to your current branch\n\n#### Example prompts\n\n```text\nSearch in JIRA for a bug related to: Null pointer exception in PaymentService.processRefund().\nIf it does not exist create it with all the context needed from the code. Find possible blockers that this bug may cause.\n```\n\n```text\nCreate a new branch called issue-gitlab-18, checkout, and link it to the issue we just created. Assign the JIRA issue to me and mark it as in-progress.\n```\n\n### Interactive walkthrough: Bug review and task automation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005368?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Bug Review\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Cross-system incident investigation\n\n**Scenario:** A production incident occurs, and you need to correlate information from Jira (incident ticket), GitLab Project Management, your codebase, and merge requests to identify the root cause.\n\nThis demo demonstrates:\n\n* Fetching incident details from Jira\n* Correlating with recent merge requests in GitLab\n* Identifying potentially related code changes\n* Generating an incident timeline\n* Design a remediation plan and create it as a work item in GitLab\n\n#### Example prompts\n\n```text\n\"We have a production incident INC-1 about checkout failures. Can you help me investigate with all available context?\"\n```\n\n```text\nCreate a timeline of events for incident INC-1 including related Jira issues and recent deployments\n```\n\n```text\nPropose a remediation plan\n```\n\n### Interactive walkthrough: Cross-system troubleshooting and remediation\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1170005413?badge=0&autopause=0& player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Cross System Investigation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## Troubleshooting\n\nThese are some common setup issues and quick fixes:\n\n| Issue | Solution |\n| ----- | ----- |\n| \"MCP server not found\" | Verify the `mcp.json` file is in the correct location and properly formatted |\n| \"Authentication failed\" | Re-check your OAuth credentials and ensure scopes are correctly configured in Atlassian |\n| \"No Jira tools available\" | Restart VS Code after updating `mcp.json` and ensure MCP is enabled in GitLab |\n| \"Connection timeout\" | Check your network connectivity to `mcp.atlassian.com` |\n\n\u003Cbr/> For detailed troubleshooting, see the [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/).\n\n\n## Security considerations\n\nWhen integrating Jira with GitLab Duo Agent Platform:\n\n* **OAuth tokens** — Make sure credentials remain secure\n* **Principle of least privilege** — Only grant the minimum required Jira scopes\n* **Token rotation** — Regularly rotate your OAuth credentials as part of security hygiene\n\n\n## Summary\n\nConnecting GitLab Duo Agent Platform to different tools through MCP transforms how you interact with your development lifecycle. In this article, you have learned how to:\n\n* **Query issues naturally** — Ask questions about your backlog, sprints, and incidents in natural language.\n* **Create and update issues on all your DevSecOps environment** — File bugs and update tickets without leaving your IDE.\n* **Correlate across systems** — Combine Jira data with GitLab project management, merge requests, and pipelines for complete visibility.\n* **Reduce context switching** — Keep your focus on code while staying connected to project management.\n\nThis integration exemplifies the power of MCP: standardized, secure access to your tools through AI, enabling developers to work more efficiently without sacrificing governance or security.\n\n\n## Read more\n\n* [GitLab Duo Agent Platform adds support for Model Context Protocol](https://about.gitlab.com/blog/duo-agent-platform-with-mcp/)\n\n* [What is Model Context Protocol?](https://about.gitlab.com/topics/ai/model-context-protocol/)\n\n* [Agentic AI guides and resources](https://about.gitlab.com/blog/agentic-ai-guides-and-resources/)\n\n* [GitLab MCP clients documentation](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_clients/)\n\n* [Get started with GitLab Duo Agent Platform: The complete guide](https://about.gitlab.com/blog/gitlab-duo-agent-platform-complete-getting-started-guide/)",{"featured":12,"template":13,"slug":753},"extend-gitlab-duo-agent-platform-connect-any-tool-with-mcp",{"promotions":755},[756,769,780,792],{"id":757,"categories":758,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[723,36],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":784,"text":760,"button":785,"image":789},"security-modernization",[783],"security","Are you trading speed for security?",{"text":786,"config":787},"Get your security maturity score",{"href":788,"dataGaName":765,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":793,"paths":794,"header":797,"text":798,"button":799,"image":804},"github-azure-migration",[795,796],"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":800,"config":801},"See how GitLab compares to GitHub",{"href":802,"dataGaName":803,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":805},{"src":779},{"header":807,"blurb":808,"button":809,"secondaryButton":814},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":810,"config":811},"Get your free trial",{"href":812,"dataGaName":49,"dataGaLocation":813},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":815},{"href":53,"dataGaName":54,"dataGaLocation":813},1777302645642]