[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/10-best-practices-for-using-ai-powered-gitlab-duo-chat":3,"navigation-en-us":40,"banner-en-us":440,"footer-en-us":450,"blog-post-authors-en-us-Michael Friedrich":691,"blog-related-posts-en-us-10-best-practices-for-using-ai-powered-gitlab-duo-chat":705,"next-steps-en-us":745,"assessment-promotions-en-us":755},{"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__":39},"blogPosts/en-us/blog/10-best-practices-for-using-ai-powered-gitlab-duo-chat.yml","10 Best Practices For Using Ai Powered Gitlab Duo Chat",[7],"michael-friedrich",null,"ai-ml",{"slug":11,"featured":12,"template":13},"10-best-practices-for-using-ai-powered-gitlab-duo-chat",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"10 best practices for using AI-powered GitLab Duo Chat","Explore tips and tricks for integrating GitLab Duo Chat into your AI-powered DevSecOps workflows. Plus, expert advice on how to refine chat prompts for the best results.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097639/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_77JeTV9gAmbXM0224acirV_1750097638765.png","2024-04-02","Getting into a conversation with AI can be challenging. What question do you start with? How do you frame the question? How much context is needed? Will the conversation provide the best and most efficient results?\n\nIn this tutorial, we explore 10 tips and best practices to integrate GitLab Duo Chat into your AI-powered DevSecOps workflows and refine your prompts for the best results.\n\n[Get started: Keep GitLab Duo Chat open and in sight](#get-started-keep-gitlab-duo-chat-open-and-in-sight)\n\n[10 best practices for using GitLab Duo Chat](#10-best-practices-for-using-gitlab-duo-chat)\n\n1. [Have a conversation](#1.-have-a-conversation)\n2. [Refine the prompt for more efficiency](#2.-refine-the-prompt-for-more-efficiency)\n3. [Follow prompt patterns](#3.-follow-prompt-patterns)\n4. [Use low-context communication](#4.-use-low-context-communication)\n5. [Repeat yourself](#5.-repeat-yourself)\n6. [Be patient](#6.-be-patient)\n7. [Reset and start anew](#7.-reset-and-start-anew)\n8. [Gain efficiency with slash commands in the IDE](#8.-gain-efficiency-with-slash-commands-in-the-ide)\n9. [Refine the prompt for slash commands](#9.-refine-the-prompt-for-slash-commands)\n10. [Get creative with slash commands](#10.-get-creative-with-slash-commands)\n\nBonus content:\n- [Shortcuts](#shortcuts)\n- [Fun exercises](#fun-exercises)\n- [Learn more](#learn-more)\n\n> Live demo! Discover the future of AI-driven software development with our GitLab 17 virtual launch event. [Register today!](https://about.gitlab.com/eighteen/)\n\n## Get started: Keep GitLab Duo Chat open and in sight\n\n[GitLab Duo Chat](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html) is available in the GitLab UI, Web IDE, and supported programming IDEs, for example, VS Code.\n\nIn VS Code, you can open GitLab Duo Chat in the default left pane. You can also drag and drop the icon into the right pane. This allows you to keep Chat open while you write code and navigate the file tree, perform Git actions, etc. To reset the Chat location, open the command palette (by pressing the `Command+Shift+P` (on macOS) or `Ctrl+Shift+P` (on Windows/Linux) keyboard shortcut and then type `View: Reset View Locations`. The following short video shows you how to do it.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/foZpUvWPRJQ\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nThe Web IDE and VS Code share the same framework – the same method works in the Web IDE for more efficient workflows.\n\n![Chat in Web IDE](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097645344.png)\n\n## 10 best practices for using GitLab Duo Chat\n\n### 1. Have a conversation\n\nChats are conversations, not search forms.\n\nFor the first conversation icebreaker, you can start with the same search terms similar to a browser search and experiment with the response and output. In this example, let's start with a C# project and best practices.\n\n> c# start project best practices\n\n![Chat prompt for C# start project best practices and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097646/Blog/Content%20Images/Blog/Content%20Images/image11_aHR0cHM6_1750097645345.png)\n\nThe response is helpful to understand a broad scope of C#, but does not kickstart immediate best practices. Let's follow up with a more focused question in the same context.\n\n> Please show the project structure for the C# project.\n\n![Chat prompt for project structure for the C# project and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image9_aHR0cHM6_1750097645346.png)\n\nThis answer is helpful. Next, let's follow up with a Git question, and use the same question structure: Direct request to show something.\n\n> Show an example for a .gitignore for C#\n\n![Chat prompt for a .gitignore for C# and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image25_aHR0cHM6_1750097645347.png)\n\nContinue with CI/CD and ask how to build the C# project.\n\n> Show a GitLab CI/CD configuration for building the C# project\n\n![Chat prompt for GitLab CI/CD configuration for building C# project and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image16_aHR0cHM6_1750097645349.png)\n\nIn this example, Chat encouraged us to request specific changes. Let's ask to use the .NET SDK 8.0 instead of 6.0.\n\n> In the above example, please use the .NET SDK 8.0 image\n\n![Chat prompt to use .NET SDK 8.0 image and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image32_aHR0cHM6_1750097645350.png)\n\nThe CI/CD configuration uses the .NET command line interface (CLI). Maybe we can use that for more efficient commands to create the projects and tests structure, too?\n\n> Explain how to create projects and test structure on the CLI\n\n![Chat prompt to explain how to create projects and test structure on the CLI and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image14_aHR0cHM6_1750097645351.png)\n\nOf course, we could execute these commands in the terminal, but what if we wanted to stay in VS Code? Let's ask Chat.\n\n> Explain how to open a new terminal in VS Code\n\n![Chat prompt to explain how to open a new terminal in VS Code and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750097645351.png)\n\n### 2. Refine the prompt for more efficiency\n\nThink of GitLab Duo Chat as a human, and engage with full sentences that provide as much context into your thoughts and questions.\n\nExperienced browser search users might know this approach to queries: Build up the question, add more terms to refine the scope, and restart the search after opening plenty of tabs.\n\nIn a browser search, this probably would result in four to five different search windows.\n\n```markdown\nc# start project best practices\nc# .gitignore\nc# gitlab cicd\nc# gitlab security scanning\nc# solutions and projects, application and tests\n```\n\nYou can follow this strategy in a chat conversation, too. It requires adding more context, making it a conversational approach. GitLab Duo Chat enables you to ask multiple questions in one conversation request. Example: You need to start with a new C# project, apply best practices, add a `.gitignore` file, and configure CI/CD and security scanning, just like in the above search. In Chat, you can combine the questions into one request.\n\n> How can I get started creating an empty C# console application in VS Code? Please show a .gitignore and .gitlab-ci.yml configuration with steps for C#, and add security scanning for GitLab. Explain how solutions and projects in C# work, and how to add a test project on the CLI.\n\n![Chat prompt adding more context and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image37_aHR0cHM6_1750097645352.png)\n\nIn this response, Chat suggests to ask for specific configuration examples in follow-up questions in the conversation. Async practice: Create follow-up questions. You can omit `C#` as context in the same chat session.\n\n> Please show an example for a .gitignore. Please show a CI/CD configuration. Include the SAST template.\n\n### 3. Follow prompt patterns\n\nFollow the pattern: `Problem statement, ask for help, provide additional requests`. Not everything comes to mind when asking the first question – don't feel blocked, and instead start with `Problem statement, ask for help` in the first iteration.\n\n> I need to fulfill compliance requirements. How can I get started with Codeowners and approval rules?\n\n![Chat prompt to get started with Codeowners and approval rules and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image19_aHR0cHM6_1750097645352.png)\n\nThe answer is helpful but obviously generic. Now, you may want to get specific help for your team setup.\n\n> Please show an example for Codeowners with different teams: backend, frontend, release managers.\n\n![Chat prompt to show an example for Codeowners with different teams: backend, frontend, release managers and reponse ](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image31_aHR0cHM6_1750097645353.png)\n\nAn alternative is to describe the situation you are in and to ask for input. It can feel a bit like a conversation to follow the STAR model (Situation, Task, Action, Results).\n\n> I have a Kubernetes cluster integrated in GitLab. Please generate a Yaml configuration for a Kubernetes service deployment. Explain how GitOps works as a second step. How to verify the results?\n\n![Chat prompt with multiple questions and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image27_aHR0cHM6_1750097645354.png)\n\n### 4. Use low-context communication\n\nProvide as much context as needed to provide an answer. Sometimes, the previous history or opened source code does not provide that helpful context. To make questions more efficient, apply a pattern of [low-context communication](https://handbook.gitlab.com/handbook/company/culture/all-remote/effective-communication/#understanding-low-context-communication), which is used in all-remote communication at GitLab.\n\nThe following question did not provide enough context in a C++ project.\n\n> Should I use virtual override instead of just override?\n\n![Chat prompt asking if the users should use virtual override instead of just override and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image34_aHR0cHM6_1750097645354.png)\n\nInstead, try to add more context:\n\n> When implementing a pure virtual function in an inherited class, should I use virtual function override, or just function override? Context is C++.\n\n![Chat prompt with more detail and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image36_aHR0cHM6_1750097645355.png)\n\nThe example is also shown in the [GitLab Duo Coffee Chat: Refactor C++ functions into OOP classes for abstract database handling](https://youtu.be/Z9EJh0J9358?t=2190).\n\n### 5. Repeat yourself\n\nAI is not predictable. Sometimes, it may not answer with the expected results, or does not produce source code examples or configuration snippets because it lacked context. It is recommended to repeat the question and refine the requirements.\n\nIn the following example, we want to create a C# application. In the first attempt, we did not specify the application type – C# can be used to create console/terminal but also UI applications. The result also does not provide an empty example source code. The second, repeated prompt adds two more words - `console` and `empty`.\n\n> How can I get started creating an C# application in VSCode?\n>\n> How can I get started creating an empty C# console application in VSCode?\n\nThe results in the prompt differ. The first response is helpful to get started by following the instructions in the VS Code window, but it does not tell us where the source code is located and how to modify it. The repeated prompt with refinements modifies the response and provides instructions how to override the default template with some “hello world” code.\n\n![Chat prompt with repeated prompt with modifications and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image28_aHR0cHM6_1750097645355.png)\n\nYou can also combine repeat and refine strategies, and ask Chat to show an example for application code and tests.\n\n> How can I get started creating an empty C# console application in VSCode? Please show an example for application and tests.\n\n![Chat prompt that asks for example for application and tests and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097645356.png)\n\n#### Repeat yourself after generic questions\n\nWhen asking generic technology questions, GitLab Duo Chat might not be able to help. In the following scenario, I wanted to get a suggestion for Java build tools and framework, and it did not work. There could be many answers: Maven, Gradle, etc., as build tools, and [100+ Java frameworks](https://en.wikipedia.org/wiki/List_of_Java_frameworks), depending on the technology stack and requirements.\n\n![Chat prompt for Java build tools and framework and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097645356.png)\n\nLet's assume that we want to focus on a customer environment with [Java Spring Boot](https://spring.io/projects/spring-boot).\n\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example.\n\n![Chat prompt that asks for more, including a hello world example and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image26_aHR0cHM6_1750097645357.png)\n\nThis provides great results already. As an async exercise, repeat the prompt, and ask how to deploy the application, adding more refinements in each step. Alternatively, you can make it a follow-up conversation.\n\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example. Show how to build and deploy the application in CI/CD.\n>\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example. Show how to build and deploy the application in CI/CD, using container images.\n>\n> I want to create a Java Spring Boot application. Please explain the project structure and show a hello world example. Show how to build and deploy the application in CI/CD, using container images. Use Kubernetes and GitOps in GitLab.\n\n### 6. Be patient\n\nSingle words or short sentences might not generate the desired results, [as shown in this video example](https://youtu.be/JketELxLNEw?t=1220). Sometimes, GitLab Duo Chat is able to guess from available data, but sometimes also might insist on providing more context.\n\nExample: `labels` matches the GitLab documentation content.\n\n![Chat prompt about labels and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image12_aHR0cHM6_1750097645357.png)\n\nRefine the question to problem statements and more refinements for issue board usage.\n\n> Explain labels in GitLab. Provide an example for efficient usage with issue boards.\n\n![Chat prompt that includes asking for an example and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image21_aHR0cHM6_1750097645358.png)\n\nOr use a problem statement, followed by a question and the ask for additional examples.\n\n> I don't know how to use labels in GitLab. Please provide examples, and how to use them for filters in different views. Explain these views with examples.\n\n![Chat prompt with problem statement and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image10_aHR0cHM6_1750097645358.png)\n\nAlso, avoid `yes/no` questions and instead add specific context.\n\n> Can you help me fix performance regressions?\n\n![Chat promptt that asks for help with fixing performance regressions and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image18_aHR0cHM6_1750097645359.png)\n\nInstead, provide the context of the performance regression, including the programming languages, frameworks, technology stack, and environments. The following example uses an environment from some years ago, which can still be accurate today.\n\n> My PHP application encounters performance regressions using PHP 5.6 and MySQL 5.5. Please explain potential root causes, and how to address them. The app is deployed on Linux VMs.\n\n![Chat prompt that includes more detail and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image24_aHR0cHM6_1750097645360.png)\n\n### 7. Reset and start anew\n\nSometimes, the chat history shows a different learning curve and provides the wrong context for follow-up questions. Or, you asked specific questions where GitLab Duo Chat cannot provide answers. Since generative AI is not predictable, it might also lack the ability to provide certain examples, but think it gave them in a future response (observed in Chat Beta). The underlying large language models, or LLMs, sometimes might insist on giving a specific response, in an endless loop.\n\n> How can I get started creating an empty C# console application in VSCode? Please show a .gitignore and .gitlab-ci.yml configuration with steps for C#, and add security scanning for GitLab. Explain how solutions and projects in C# work, and how to add a test project on the CLI.\n\nAfter asking the question above with an example configuration, I wanted to reduce the scope of the question to get a more tailored response. It did not work as expected, since Chat knows about the chat history in context, and refers to previous answers.\n\n> How can I get started creating an empty C# console application in VSCode? Please show a .gitignore and .gitlab-ci.yml configuration with steps for C#.\n\n![Chat prompt that asks for configuration examples and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image23_aHR0cHM6_1750097645360.png)\n\nTo force Chat into a new context, use `/reset` as slash command to reset the session, and repeat the question to get better results. You can also use `/clean` or `/clear` to delete all messages in the conversation.\n\n### 8. Gain efficiency with slash commands in the IDE\n\n#### Explain code\n\n- Q: Generated code? Existing code? Legacy code?\n- A: Use the [`/explain` slash command in the IDE](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html#explain-code-in-the-ide).\n- A2: Refine the prompt with more focused responses, for example: `/explain focus on potential shortcomings or bugs`.\n\n![Chat prompt with /explain slash command](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/gitlab_duo_chat_slash_commands_explain_01_aHR0cHM6_1750097645361.png)\n\n![Chat prompt with refined prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image6_aHR0cHM6_1750097645361.png)\n\n#### Refactor code\n\n- Q: Unreadable code? Long spaghetti code? Zero test coverage?\n- A: Use the [`/refactor` slash command in the IDE](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html#refactor-code-in-the-ide).\n- A2: Refine the prompt for more targeted actions, for example object-oriented patterns: `/refactor into object-oriented classes with methods and attributes`.\n\n![Chat prompt with /refactor slash command](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image35_aHR0cHM6_1750097645362.png)\n\n![Chat prompt with refined prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image30_aHR0cHM6_1750097645362.png)\n\n#### Generate tests\n\n- Q: Testable code but writing tests takes too much time?\n- A: Use the [`/tests` slash command in the IDE](https://docs.gitlab.com/ee/user/gitlab_duo_chat.html#write-tests-in-the-ide).\n- A2: Refine the prompt for specific test frameworks, or test targets. You can also instruct the prompt to focus on refactoring, and then generate tests: `/tests focus on refactoring the code into functions, and generate tests`.\n\n![Chat prompt with /tests slash command](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image29_aHR0cHM6_1750097645363.png)\n\n![Chat prompt with refined prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750097645363.png)\n\nMore practical examples in complete development workflows are available in the [GitLab Duo examples](https://docs.gitlab.com/ee/user/gitlab_duo_examples.html) documentation.\n\n### 9. Refine the prompt for slash commands\n\nYou will see refined prompts tips in this blog post a lot. It is one of the ingredients for better AI-powered workflow efficiency. Slash commands are no different, and allow for better results in GitLab Duo Chat.\n\nA customer recently asked: \"Can code explanations using `/explain` create comments in code?\" The answer is: no. But you can use the Chat prompt to ask follow-up questions, and ask for a summary in a code comment format. It requires the context of the language.\n\nThe following example with a [C++ HTTP client code using the curl library](https://gitlab.com/gitlab-da/use-cases/ai/ai-workflows/gitlab-duo-prompts/-/blob/5cc9bdd65ee8ee16c548bea0402c18f8209d4d06/chat/slash-commands/c++/cli.cpp) needs more documentation. You can refine the `/explain` prompt by giving more refined instructions to explain the code by adding code comments, and then copy-paste that into the editor.\n\n> /explain add documentation, rewrite the code snippet\n\n![Chat prompt to add documentation and rewrite code snippet and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image13_aHR0cHM6_1750097645363.png)\n\nAlternatively, you can ask Chat to `/refactor` the source code, and generate missing code comments through a refined prompt.\n\n> /refactor add code comments and documentation\n\n![Chat prompt to refactor source code and generate code comments](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image15_aHR0cHM6_1750097645364.png)\n\n### 10. Get creative with slash commands\n\nWhen the Chat prompt does not know an answer to a question about the source code or programming language, look into the slash commands `/explain`, `/refactor`, and `/tests` and how much they can help in the context.\n\nIn the following example, an SQL query string in C++ is created in a single line. To increase readability, and also add more database columns in the future, it can be helpful to change the formatting into a multi-line string.\n\n> std::string sql = \"CREATE TABLE IF NOT EXISTS users (id INTEGER PRIMARY KEY AUTOINCREMENT, name TEXT NOT NULL, email TEXT NOT NULL)\";\n\nYou can ask GitLab Duo Chat about it, for example, with the following question:\n\n> How to create a string in C++ using multiple lines?\n\nChat may answer with an explanation and optional, a source code example. In this context, it can interpret the question to create a C++ string value with multiple lines, for example, using the `\\n` character, assigned to a variable.\n\nThe requirement instead is to only format the written code, and variable value assignment in multiple lines. The string value itself does not need to contain a multi-line string representation.\n\nThere is an alternative for additional context in VS Code and the Web IDE: Select the source code in question, right-click, and navigate into `GitLab Duo Chat > Refactor`. This opens the Chat prompt and fires the `/refactor` code task immediately.\n\nAlthough, the code task might not bring the expected results. Refactoring a single-line SQL string can mean a lot of things: Use multiple lines for readability, create constants, etc.\n\nCode tasks provide an option to refine the prompt. You can add more text after the `/refactor` command, and instruct GitLab Duo Chat to use a specific code type, algorithm, or design pattern.\n\nLet's try it again: Select the source code, change focus into Chat, and type the following prompt, followed by `Enter`.\n\n> /refactor into a multi-line written string. Show different approaches for all C++ standards.\n\n![Chat prompt to refactor into a multi-line written string and response](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image17_aHR0cHM6_1750097645364.png)\n\n**Tip:** You can use GitLab Duo Code Suggestions to refine the source code even more after refactoring, or use alternative `/refactor` prompt refinements.\n\n>/refactor into a multi-line written string, show different approaches\n>\n> /refactor into multi-line string, not using raw string literals\n>\n> /refactor into a multi-line written string. Make the table name parametrizable\n\nAn alternative approach with the `stringstream` type is shown in the [GitLab Duo Coffee Chat: Refactor C++ functions into OOP classes for abstract database handling](https://www.youtube.com/watch?v=Z9EJh0J9358), [MR diff](https://gitlab.com/gitlab-da/use-cases/ai/gitlab-duo-coffee-chat/gitlab-duo-coffee-chat-2024-01-23/-/commit/7ea233138aed46d77e6ce0d930dd8e10560134eb#4ce01e4c84d4b62df8eed159c2db3768ad4ef8bf_33_35).\n\n#### Explain vulnerabilities\n\nIt might not always work, but the `/explain` slash command can be asked about security vulnerability explanations, too. In this example, the [C code](https://gitlab.com/gitlab-da/use-cases/ai/ai-workflows/gitlab-duo-prompts/-/blob/5a5f293dfbfac7222ca4013d8f9ce9b462e4cd3a/chat/slash-commands/c/vuln.c) contains multiple vulnerabilities for strcpy() buffer overflows, world writable file permissions, race condition attacks, and more.\n\n> /explain why this code has multiple vulnerabilities\n\n![Chat prompt about the code's multiple vulnerabilities](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image20_aHR0cHM6_1750097645365.png)\n\n#### Refactor C code into Rust\n\nRust provides memory safety. You can ask Duo Chat to refactor the vulnerable [C code](https://gitlab.com/gitlab-da/use-cases/ai/ai-workflows/gitlab-duo-prompts/-/blob/5a5f293dfbfac7222ca4013d8f9ce9b462e4cd3a/chat/slash-commands/c/vuln.c) into Rust, using `/refactor into Rust`. Practice with more refined prompts to get better results.\n\n> /refactor into Rust and use high level libraries\n\n![Chat prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image8_aHR0cHM6_1750097645366.png)\n\n### Shortcuts\n\nGive these shortcuts a try in your environment, and practice async using GitLab Duo Chat.\n\n1. Inspect vulnerable code from CVEs, and ask what it does, and how to fix it, using `/explain why is this code vulnerable`.\n**Tip:** Import open-source projects in GitLab to take advantage of GitLab Duo Chat code explanations.\n1. Try to refactor code into new programming languages to help legacy code migration plans.\n1. You can also try to refactor Jenkins configuration into GitLab CI/CD, using `/refactor into GitLab CI/CD configuration`.\n\n### Fun exercises\n\nTry to convince Chat to behave like Clippy.\n![Chat prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image22_aHR0cHM6_1750097645366.png)\n\nAsk about GitLab's mission: \"Everyone can contribute.\"\n\n![Chat prompt](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097645/Blog/Content%20Images/Blog/Content%20Images/image33_aHR0cHM6_1750097645367.png)\n\n### Learn more\n\nThere are many different environments and challenges out there. 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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",[23,739,109,556],"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",{"header":746,"blurb":747,"button":748,"secondaryButton":753},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":749,"config":750},"Get your free trial",{"href":751,"dataGaName":51,"dataGaLocation":752},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":496,"config":754},{"href":55,"dataGaName":56,"dataGaLocation":752},{"promotions":756},[757,770,782],{"id":758,"categories":759,"header":760,"text":761,"button":762,"image":767},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":763,"config":764},"Get your AI maturity score",{"href":765,"dataGaName":766,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":771,"categories":772,"header":774,"text":761,"button":775,"image":779},"devops-modernization",[773,559],"product","Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":766,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":785,"text":761,"button":786,"image":790},"security-modernization",[729],"Are you trading speed for security?",{"text":787,"config":788},"Get your security maturity score",{"href":789,"dataGaName":766,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",1772652048872]