[{"data":1,"prerenderedAt":792},["ShallowReactive",2],{"/en-us/blog/measuring-ai-effectiveness-beyond-developer-productivity-metrics":3,"navigation-en-us":41,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Taylor McCaslin":690,"blog-related-posts-en-us-measuring-ai-effectiveness-beyond-developer-productivity-metrics":705,"assessment-promotions-en-us":745,"next-steps-en-us":782},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":40},"blogPosts/en-us/blog/measuring-ai-effectiveness-beyond-developer-productivity-metrics.yml","Measuring Ai Effectiveness Beyond Developer Productivity Metrics",[7],"taylor-mccaslin",null,"ai-ml",{"slug":11,"featured":12,"template":13},"measuring-ai-effectiveness-beyond-developer-productivity-metrics",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Measuring AI effectiveness beyond developer productivity metrics ","AI assistants are here, yet measuring AI's impact on productivity isn’t figured out. Here’s why it’s a difficult problem and how GitLab is solving it.",[18],"Taylor McCaslin","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749671994/Blog/Hero%20Images/AdobeStock_700757695.jpg","2024-02-20","AI-powered productivity tools promise to boost productivity by automating repetitive coding and tedious tasks, as well as generating code. How organizations measure the AI impact of these productivity tools has yet to be truly figured out. GitLab is working on a solution: AI Impact is a dashboard grounded in [value stream analytics](https://about.gitlab.com/solutions/value-stream-management/) that will help organizations understand the effect of [GitLab Duo](https://about.gitlab.com/gitlab-duo/), our AI-powered suite of features, on their productivity. AI Impact is the culmination of what we’ve learned at GitLab about measuring the impact of AI, and we wanted to share those lessons with you.\n\n[A report for The Pragmatic Engineer](https://newsletter.getdx.com/p/developer-productivity-metrics-at-top-companies) shows that measuring productivity in general isn’t straightforward, with top engineering teams around the globe all using different metrics. If everyone has a different productivity metric to optimize, how do we even begin to measure the impact of AI productivity tools? Welcome to why measuring AI assistant productivity impact is difficult and commonly misses the mark.\n\n>  Follow the progress of our AI Impact dashboard and [share your feedback](https://gitlab.com/groups/gitlab-org/-/epics/12978).\n\n## Flawed productivity metrics\n\nSimplistic productivity metrics like lines of code contributed per day or acceptance rates of AI suggestions fail to capture downstream costs. For instance, GitClear, according to [an Infoworld article](https://www.infoworld.com/article/3712685/is-ai-making-our-code-stupid.html), “analyzed 153 million lines of changed code between January 2020 and December 2023 and now expects that code churn ('the percentage of lines that are reverted or updated less than two weeks after being authored') will double in 2024.\" Thus, simply measuring lines of code risks technical debt pileup and skill atrophy in developers.  \n\n## Indirect impacts are hard to quantify\n\nThe goal of AI developer tools is to remove toil, allowing developers to focus on higher value tasks like system architecture and design. But how much time is really saved this way versus spent reviewing, testing, and maintaining AI-generated code? These second-order productivity impacts are very difficult to accurately attribute directly to AI, which may give you a false sense of value. One solution to this is to choose who gets to use AI productivity tools carefully.\n\n## Focus should be on business outcomes\n\nUltimately, what matters is real-world business outcomes, not developer activity metrics. Tracking lead time, cycle time, production defects, and user satisfaction better indicate where bottlenecks exist. If AI tools generate usable code faster, and quality teams can’t keep up with changes, the end software product may decrease in quality and lead to customer satisfaction problems. Shipping more sounds great until it causes problems that take even more time, money, and effort to resolve. Measuring business outcomes is also difficult and these measurements frequently are lagging indicators of problems. Measuring quality defects, security issues, and application performance are all ways to identify business impact sooner. \n\n## The need to balance speed and quality\n\nWhile AI code generation has the potential to accelerate development velocity, it should not come at the cost of overall quality and maintainability. Teams must strike the right balance between velocity and writing maintainable, well-tested code that solves actual business problems. Quality should not be sacrificed purely to maximize productivity metrics. This is when measuring lines of code AI generates or number of AI suggestions developers accept can optimize for the problematic outcomes. More code doesn't necessarily mean higher quality or productivity. More code means more to review, test, and maintain – potentially slowing delivery down.\n\nLet’s look at an example: AI-generated code output is scoped to the area a developer is currently working on. Current AI tools lack the ability to assess the broader architecture of the application (amplified in a microservices architecture). This means that even if the quality of the generated code is good, it may lead to repetition and code bloat because it will be inserted into the area targeted rather than making wider systematic changes. This is problematic in languages that are architected with object-oriented languages that use DRY (don't-repeat-yourself) principles. This is an active area of research and we’re excited to adopt new approaches and technologies to increase the context awareness of our AI features.\n\nAcceptance rate can be particularly misleading, and unfortunately is becoming the primary way AI productivity tools measure success. Developers may accept an AI-generated suggestion but then need to heavily edit or rewrite it. Thus, the initial acceptance gives no indication of whether the suggestion was actually useful. Acceptance rate is fundamentally a proxy for AI assistant quality, yet it is misconstrued as a productivity measure. This is especially misleading when all vendors are measuring acceptance rate differently and marketing based on this number. GitLab intentionally does not use this kind of data in our marketing. What we’ve seen in practice is that developers use AI-generated code similar to how an actor uses a cue – they look at the generated code and say, \"oh, right, that's the nudge I needed, I'll take it from here.\" \n\n## Implementation and team dynamics play a key role\n\nHow productivity gains materialize depends on how AI tools are implemented and developer dynamics. If some developers distrust the technology or reviews become lax expecting AI to catch errors, quality may suffer. Additionally, introducing AI tools often necessitates changes to processes like code reviews, testing, and documentation. Productivity could temporarily decline as teams adjust to new workflows before seeing gains. Organizations must ensure that when implementing AI tools, that they allow teams time to figure out how it works and how it fits into their workflows, knowing that this trial-and-error period may lead to reduced productivity metrics before seeing productivity gains. \n\nTo get this balance right, it’s important to define the tasks that are highly accurate and consistent and train the team to use AI for those use cases (at least, at first). We know that AI code generation is useful for producing scaffolding, test generation, and syntax corrections, as well as generating documentation. Have teams start there and they will see better results and learn to use the tool more effectively. Remember you can’t measure AI’s impact in a week. You have to give teams time to find their rhythm with their AI assistants. \n\n## Challenges exist, but AI is the future\n\nNow that we’ve talked about the challenges of measuring AI impact and potential risks, we do want to say at GitLab we do believe AI has a huge role to play in the evolution of DevSecOps platforms. That’s why we’re building GitLab Duo. But we are not rushing into productivity measurement by showing acceptance rates, or lines of code generated. We believe these are a step backwards to previous ways of thinking about productivity. Instead we’re looking at the data we have within our unified DevSecOps platform to present a more complete picture of AI Impact.  \n\n## What to measure instead\n\nMeasuring the productivity impacts of AI developer tools requires nuance and a focus on end-to-end outcomes rather than isolated productivity metrics. For these reasons, simple quantitative metrics tend to miss the nuances of measuring productivity with AI developer tools. The key is to combine quantitative data from across the software development lifecycle (SDLC) with qualitative feedback from developers on how AI actually impacts their day-to-day experience and shapes long-term development practices. Only then can we get an accurate picture of the productivity gains these tools can offer. We view AI as an augmentor to DevSecOps adoption, rather than a replacement for doing things the right way. Organizations focusing on building the right muscles in their SDLC practice are the ones best positioned to actually take advantage of any potential gains in developer coding productivity.\n\nSo what metric should we use instead? At GitLab we already have [value stream analytics](https://about.gitlab.com/solutions/value-stream-management/), which examine the end-to-end flow of work from idea to production to determine where bottlenecks exist. Value stream analytics isn’t a single measurement, it’s the ongoing tracking of metrics like lead time, cycle time, deployment frequency, and production defects. This keeps the focus on business outcomes rather than developer activity. By taking a holistic view across code quality, collaboration, downstream costs, and developer experience, teams can steer these technologies to augment (rather than replace) human abilities over the long run. \n\n## Introducing GitLab's AI Impact approach\n\nGitLab has the whole picture being a unified DevSecOps platform that spans the entire SDLC. We built [Value Stream Management](https://about.gitlab.com/solutions/value-stream-management/) to empower teams with metrics and insights to ship better software faster. Blending GitLab [Value Stream Analytics](https://about.gitlab.com/solutions/value-stream-management/) and [DORA metrics](https://about.gitlab.com/solutions/value-stream-management/dora/), and GitLab Duo usage data, we can provide organizations with the complete picture of how AI is impacting their SDLC. We’re calling this dashboard AI Impact, and it’s coming in an upcoming release to measure GitLab Duo’s impact on productivity. Follow our progress and [share your feedback](https://gitlab.com/groups/gitlab-org/-/epics/12978). \n\n_Disclaimer: This blog contains information related to upcoming products, features, and functionality. It is important to note that the information in this blog post is for informational purposes only. Please do not rely on this information for purchasing or planning purposes. As with all projects, the items mentioned in this blog and linked pages are subject to change or delay. 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statement",{"items":680},[681,684,687],{"text":682,"config":683},"Terms",{"href":511,"dataGaName":512,"dataGaLocation":459},{"text":685,"config":686},"Cookies",{"dataGaName":521,"dataGaLocation":459,"id":522,"isOneTrustButton":12},{"text":688,"config":689},"Privacy",{"href":516,"dataGaName":517,"dataGaLocation":459},[691],{"id":692,"title":693,"body":8,"config":694,"content":696,"description":8,"extension":27,"meta":700,"navigation":12,"path":701,"seo":702,"stem":703,"__hash__":704},"blogAuthors/en-us/blog/authors/taylor-mccaslin.yml","Taylor Mccaslin",{"template":695},"BlogAuthor",{"name":18,"config":697},{"headshot":698,"ctfId":699},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667996/Blog/Author%20Headshots/tmccaslin-headshot.png","tmccaslin",{},"/en-us/blog/authors/taylor-mccaslin",{},"en-us/blog/authors/taylor-mccaslin","7SPWjJi2CicE7Or_JC0bA95HjfK2vx1NkcbRlLVcgCk",[706,719,732],{"content":707,"config":717},{"title":708,"description":709,"authors":710,"heroImage":712,"date":713,"body":714,"category":9,"tags":715},"10 AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[711],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[23,716],"DevOps platform",{"featured":31,"template":13,"slug":718},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":720,"config":730},{"title":721,"description":722,"heroImage":723,"authors":724,"date":726,"body":727,"category":9,"tags":728},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[725],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[23,729],"security",{"featured":12,"template":13,"slug":731},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":733,"config":743},{"title":734,"description":735,"authors":736,"category":9,"tags":738,"date":740,"heroImage":741,"body":742},"Secure and fast deployments to Google Agent Engine with GitLab","Follow this step-by-step guide to build an AI agent with Google's Agent Development Kit and deploy to Agent Engine using GitLab.",[737],"Regnard Raquedan",[23,739,110,25],"google","2026-02-26","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","In this tutorial, you'll learn how to deploy an AI agent built with Google's Agent Development Kit ([ADK](https://google.github.io/adk-docs/)) to [Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using GitLab's native Google Cloud integration and CI/CD pipelines. We'll cover IAM configuration, pipeline setup, and testing your deployed agent.\n\n## What is Agent Engine and why does it matter?\n\nAgent Engine is Google Cloud's managed runtime specifically designed for AI agents. Think of it as the production home for your agents — where they live, run, and scale without you having to manage the underlying infrastructure. Agent Engine handles infrastructure, scaling, session management, and memory storage so you can focus on building your agent — not managing servers. It also integrates natively with Google Cloud's logging, monitoring, and IAM.\n\n## Why use GitLab to deploy to Agent Engine?\n\nAI agent deployment is typically difficult to configure correctly. Security considerations, CI/CD orchestration, and cloud permissions create friction that slows down development cycles.\n\nGitLab streamlines this entire process while enhancing security:\n\n- **Built-in security scanning** — Every deployment is automatically scanned for vulnerabilities without additional configuration.\n- **Native Google Cloud integration** — Workload Identity Federation eliminates the need for service account keys.\n- **Simplified CI/CD** — GitLab's templates handle complex deployment logic.\n\n## Prerequisites\n\nBefore you begin, ensure you have:\n\n- A Google Cloud project with the following APIs enabled:\n  - Cloud Storage API\n  - Vertex AI API\n- A GitLab project for your source code and CI/CD pipeline\n- A Google Cloud Storage bucket for staging deployments\n- Google Cloud IAM integration configured in GitLab (see Step 1)\n\nHere are the steps to follow.\n\n## 1. Configure IAM integration\n\nThe foundation of secure deployment is proper IAM configuration between GitLab and Google Cloud using Workload Identity Federation.\n\nIn your GitLab project:\n\n1. Navigate to **Settings > Integrations**.\n2. Locate the **Google Cloud IAM** integration.\n3. Provide the following information:\n   - **Project ID**: Your Google Cloud project ID\n   - **Project Number**: Found in your Google Cloud console\n   - **Workload Identity Pool ID**: A unique identifier for your identity pool\n   - **Provider ID**: A unique identifier for your identity provider\n\nGitLab generates a script for you. Copy and run this script in Google Cloud Shell to establish the Workload Identity Federation between platforms.\n\n**Important:** Add these additional roles to your service principal for Agent Engine deployment:\n\n- `roles/aiplatform.user`\n- `roles/storage.objectAdmin`\n\nYou can add these roles using gcloud commands:\n\n```bash\nGCP_PROJECT_ID=\"\u003Cyour-project-id>\"\nGCP_PROJECT_NUMBER=\"\u003Cyour-project-number>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003Cyour-pool-id>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/aiplatform.user'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/storage.objectAdmin'\n```\n\n## 2. Create the CI/CD pipeline\n\nNow for the core of the deployment — the CI/CD pipeline. Create a `.gitlab-ci.yml` file in your project root:\n\n```yaml\nstages:\n  - test\n  - deploy\n\ncache:\n  paths:\n    - .cache/pip\n  key: ${CI_COMMIT_REF_SLUG}\n\nvariables:\n  GCP_PROJECT_ID: \"\u003Cyour-project-id>\"\n  GCP_REGION: \"us-central1\"\n  STORAGE_BUCKET: \"\u003Cyour-staging-bucket>\"\n  AGENT_NAME: \"Canada City Advisor\"\n  AGENT_ENTRY: \"canada_city_advisor\"\n\nimage: google/cloud-sdk:slim\n\n# Security scanning templates\ninclude:\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n\ndeploy-agent:\n  stage: deploy\n  identity: google_cloud\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n  before_script:\n    - gcloud config set core/disable_usage_reporting true\n    - gcloud config set component_manager/disable_update_check true\n    - pip install -q --no-cache-dir --upgrade pip google-genai google-cloud-aiplatform -r requirements.txt --break-system-packages\n  script:\n    - gcloud config set project $GCP_PROJECT_ID\n    - adk deploy agent_engine \n        --project=$GCP_PROJECT_ID \n        --region=$GCP_REGION \n        --staging_bucket=gs://$STORAGE_BUCKET \n        --display_name=\"$AGENT_NAME\" \n        $AGENT_ENTRY\n```\n\nThe pipeline consists of two stages:\n\n**Test stage** — GitLab's security scanners run automatically. The included templates provide dependency scanning, static application security testing (SAST), and secret detection without additional configuration.\n\n**Deploy stage** — Uses the ADK CLI to deploy your agent directly to Agent Engine. The staging bucket temporarily holds your application workload before Agent Engine picks it up for deployment.\n\n### Key configuration notes\n\n- The `identity: google_cloud` directive enables keyless authentication via Workload Identity Federation.\n- Security scanners are included as templates, meaning they run by default with no setup required.\n- The `adk deploy agent_engine` command handles all the complexity of packaging and deploying your agent.\n- Pipeline caching speeds up subsequent deployments by preserving pip dependencies.\n\n## 3. Deploy and verify\n\nWith your pipeline configured:\n\n1. Commit your agent code and `.gitlab-ci.yml` to GitLab.\n2. Navigate to **Build > Pipelines** to monitor execution.\n3. Watch the test stage complete security scans.\n4. Observe the deploy stage push your agent to Agent Engine.\n\nOnce the pipeline succeeds, verify your deployment in the Google Cloud Console:\n\n1. Navigate to **Vertex AI > Agent Engine**.\n2. Locate your deployed agent.\n3. Note the **resource name** — you'll need this for testing.\n\n## 4. Test your deployed agent\n\nTest your agent using a curl command. You'll need three pieces of information:\n\n- **Agent ID**: From the Agent Engine console (the resource name's numeric identifier)\n- **Project ID**: Your Google Cloud project\n- **Location**: The region where you deployed (e.g., `us-central1`)\n\n```bash\nPROJECT_ID=\"\u003Cyour-project-id>\"\nLOCATION=\"us-central1\"\nAGENT_ID=\"\u003Cyour-agent-id>\"\nTOKEN=$(gcloud auth print-access-token)\n\ncurl -X POST \\\n  -H \"Authorization: Bearer $TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  \"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines/${AGENT_ID}:streamQuery\" \\\n  -d '{\n    \"input\": {\n      \"message\": \"I make $85,000 per year and I prefer cities with mild winters and a vibrant cultural scene. I also want to be near the coast if possible. What Canadian cities would you recommend?\",\n      \"user_id\": \"demo-user\"\n    }\n  }' | jq -r '.content.parts[0].text'\n```\n\nIf everything is configured correctly, your agent will respond with personalized city recommendations based on the budget and lifestyle preferences provided.\n\n## Security benefits of this approach\n\nThis deployment pattern provides several security advantages:\n\n- **No long-lived credentials**: Workload Identity Federation eliminates service account keys entirely.\n- **Automated vulnerability scanning**: Every deployment is scanned before reaching production.\n- **Complete audit trail**: GitLab maintains full visibility of who deployed what and when.\n- **Principle of least privilege**: Fine-grained IAM roles limit access to only what's needed.\n\n## Summary\n\nDeploying AI agents to production doesn't have to be complex. By combining GitLab's DevSecOps platform with Google Cloud's Agent Engine, you get:\n\n- A managed runtime that handles scaling and infrastructure\n- Built-in security scanning without additional tooling\n- Keyless authentication via native cloud integration\n- A streamlined deployment process that fits modern AI development workflows\n\nWatch the full demo:\n\n\n\u003Cfigure class=\"video_container\"> \u003Ciframe src=\"https://www.youtube.com/embed/sxVFa2Mk-x4?si=Oi3cUjhgd7FT2yEd\" frameborder=\"0\" allowfullscreen=\"true\" title=\"Deploy AI Agents to Agent Engine with GitLab\"> \u003C/iframe> \u003C/figure>\n\n> Ready to try it yourself? Use this tutorial's [complete code example](https://gitlab.com/gitlab-partners-public/google-cloud/demos/agent-engine-demo) to get started now. Not a GitLab customer yet? Explore the DevSecOps platform with [a free trial](https://about.gitlab.com/free-trial/).\n",{"featured":31,"template":13,"slug":744},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":746},[747,760,771],{"id":748,"categories":749,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[26,39],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":774,"text":751,"button":775,"image":779},"security-modernization",[729],"Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":756,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":783,"blurb":784,"button":785,"secondaryButton":790},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":786,"config":787},"Get your free trial",{"href":788,"dataGaName":52,"dataGaLocation":789},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":791},{"href":56,"dataGaName":57,"dataGaLocation":789},1772652080500]