• AUTOMATED
  • INTEGRATED
  • EFFICIENT
  • HUMAN

How to orchestrate build and test pipelines with your AI

Connect Dagger to your AI client once. From there, your agent handles the heavy lifting of executing complex, containerized workflows.

Ask AI about this page

Short answer

Can my AI run CI/CD pipelines using Dagger?

Yes. Your AI uses Dagger to execute containerized tasks and chain them into complex DAG operations. It manages the entire lifecycle by interacting with the Dagger engine through GraphQL. You get a programmable pipeline controlled entirely through natural language.

Pipeline outcomes

Where your builds land.

What happens when your AI takes control of your builds.

AUTOMATED

Containerized execution

The AI triggers specific tasks inside isolated containers. This ensures your builds run in the exact same environment every time.

INTEGRATED

Chained DAG operations

Your agent links multiple steps together into a single directed acyclic graph. It handles the dependencies between build, test, and deploy steps.

EFFICIENT

Smart cache usage

The AI interacts with volumes to reuse data from previous runs. This cuts down on redundant work and speeds up your feedback loop.

HUMAN

Manual intervention

When a build fails or a specific gate is reached, the AI stops and asks you for direction. You decide whether to retry, fix the code, or abort.

The workflow

What your AI does when the pipeline request arrives.

Your AI acts as the orchestrator, translating your intent into executable container instructions.

  1. Inspect the environment

    The AI looks at your local files and directory structure to understand the project context before starting a build.

    query_directory

  2. Execute build tasks

    Your agent sends commands to the Dagger engine to spin up containers and run your specific build or test logic.

    execute_graphql_query

  3. Verify build artifacts

    The AI checks the output of a container to ensure the build succeeded or to inspect logs if it failed.

    query_container

  4. Manage build data

    The AI checks existing volumes to see if it can skip steps by using cached data from a previous run.

    query_cache_volume

Try it

Copy these to start.

Use these prompts to kick off your first Dagger workflow.

Starting points

These are starting points. Swap out the repo names or specific task names to match your actual project.

Dagger Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_jU749naeDKubVrRR38BTiJPB8XzxtT6YXppJtFAW/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Dagger capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "dagger-programmable-ci-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_jU749naeDKubVrRR38BTiJPB8XzxtT6YXppJtFAW/mcp"
    }
  }
}
Copy into chat04

  • Run a build for the 'auth-service' repo and tell me if it passes.

  • Check the contents of the 'dist' directory in my current project.

  • Execute the test suite for the 'frontend-app' using the latest container image.

  • Look at the cache volume to see if we can reuse the build layers from yesterday.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Dagger once, then ask.

Just link Dagger to your MCP-compatible client. Your credentials stay encrypted, and you can start running pipelines immediately.

Connect Dagger to your AI

FAQ

How the Dagger orchestration behaves

  • 01

    Can the AI change my local source code?

    No. The AI can read your directory and execute tasks in containers, but it does not have permission to modify your local files directly.

  • 02

    Does the AI handle the actual deployment to production?

    The AI can execute the deployment steps defined in your Dagger pipeline, but it cannot bypass any manual approval gates you have set up in your workflow.

  • 03

    How does the AI know if a build failed?

    The AI queries the container output and the GraphQL interface to check the exit status of your tasks.

  • 04

    Can the AI see my entire hard drive?

    No. It can only access the specific directories you provide context for through the directory query tool.

  • 05

    Will this replace my existing CI/CD provider?

    It doesn't have to. You can use it alongside your current setup to run local, programmable versions of your pipelines.

  • More questions about Dagger? The Connector page answers them. See everything the Dagger Connector can do

Connect Dagger to Claude, Cursor, ChatGPT & more