Run several AI coding agents in parallel with the use of Dagger containers

In AI development development, coding agents have become essential employees. These autonomous or semi-autonomous tools can write, test and refact the code, considerably accelerating development cycles. However, as the number of agents working on a single basis of code increases, challenges: dependence conflicts, state leaks between agents and the difficulty of following the actions of each agent. THE container The Dagger project takes up these challenges by offering containerized environments adapted to coding agents. By isolating each agent in its container, the developers can perform several agents simultaneously without interference, inspect their activities in real time and intervene directly if necessary.

Traditionally, when a coding agent performs tasks, such as the installation of dependencies, the execution of construction scripts or the launch of servers, he does so in the local developer environment. This approach quickly leads to conflicts: an agent can upgrade a shared library that breaks the workflow of another agent, or an wandering script can leave artifacts that obscure subsequent races. Containerization elegantly solves these problems by encapsulating the environment of each agent. Rather than Babysitting Agents one by one, you can turn completely fresh environments, experiment safely and instantly eliminate failures, while maintaining visibility in exactly what each agent has executed.

In addition, because containers can be managed via familiar tools, Docker, Git and Standard CLI, the consumption of containers is integrated transparently into existing workflows. Instead of locking themselves in a proprietary solution, the teams can take advantage of their favorite technological battery, that this means Python virtual environments, node.js tool chains or system packages. The result is a flexible architecture that allows developers to exploit the full potential of coding agents, without sacrificing control or transparency.

Installation and configuration

The start of the use of containers is simple. The project provides a CLI tool based on Go, “Cu”, which you create and install via a simple “make” command. By default, the build targets your current platform, but the cross compilation is supported via standard environment variables “Targetplatform”.

# Build the CLI tool
make

# (Optional) Install into your PATH
make install && hash -r

After carrying out these orders, the binary “CU” becomes available in your shell, ready to launch containerized sessions for any MCP compatible agent. If you have to compile for a different architecture, for example, Arm64 for a Raspberry Pi, simply prefix the construction with the target platform:

TARGETPLATFORM=linux/arm64 make

This flexibility guarantees that if you are developing on MacOS, the Windows subsystem for Linux or any Linux flavor, you can easily generate an environmental binary.

Integrate with your favorite agents

One of the container-use forces is his compatibility with any agent who speaks the model's context protocol (MCP). The project provides examples of integration for popular tools such as Claude Code, Cursor, Github Copilot and Goose. Integration generally implies the addition of “the use of containers” as a MCP server in the configuration of your agent and activate it:

Claude Code uses NPM help to save the server. You can merge Dagger's recommended instructions in your “Claude.md” so that the execution of “Claude” automatically generates agents in isolated containers:

  npx @anthropic-ai/claude-code mcp add container-use -- $(which cu) stdio
  curl -o CLAUDE.md https://raw.githubusercontent.com/dagger/container-use/main/rules/agent.md

Goose, an agent framework based on a browser, bed from ‘~ / .config / goose / config.yaml'. The addition of a “users” section orders the goose to launch each navigation agent inside his own container:

  extensions:
    container-use:
      name: container-use
      type: stdio
      enabled: true
      cmd: cu
      args:
        - stdio
      envs: {}

The cursor, the AI ​​code assistant can be hung by deleting a rules file in your project. With “Curl”, you recover the recommended rule and place it in “.cursor / rules / contain -use.mdc”.

VSCODE and GITHUB COPILOT users can update their “parameters.json” and “.GitHub / COPILOT-INSTRUCTIONS.MD”, indicating the “CU” command as a MCP server. Copilot then performs his code supplements in the encapsulated environment. Kilo code integrates via a parameter file based on JSON, allowing you to specify the “CU” command and any argument required under “MCPSERVERS”. Each of these integrations guarantees that, whatever the assistant you choose, your agents operate in their sandbox, thus removing the risk of cross -contamination and simplification of cleaning after each round.

Practical examples

To illustrate how containers' consumption can revolutionize your development workflow, the Poignard repository includes several ready -to -use examples. These demonstrate typical use cases and highlight the flexibility of the tool:

• Hello World: In this minimal example, an agent escapes a simple HTTP server, let's say, using the “HTTP” module of Flask or Node, and launches it in its container. You can hit “Localhost” in your browser to confirm that the code generated by the agent runs as expected, fully isolated from your host system.
• Parallel development: here, two agents turn distinct variations from the same application, one using Flask and another using Fastapi, each in its own container and on separate ports. This scenario shows how to assess several approaches side by side without worrying about port collisions or dependence conflicts.
• Safety analysis: In this pipeline, an agent performs routine maintenance, updating vulnerable dependencies, reinstalling the version to ensure that nothing was broken and the generation of a fixing file which captures all the modifications. The entire process takes place in a disposable container, leaving your repository in its original state, unless you decide to merge the fixes.

The execution of these examples is as simple as pipes the example file in your agent order. For example, with Claude Code:

cat examples/hello_world.md | claude

Or with goose:

goose run -i examples/hello_world.md -s

After the execution, you will see each agent engaging their work in a dedicated git branch which represents its container. Inspection of these branches via “Git Checkout” allows you to revise, test or merge the modifications according to your conditions.

A common concern during the agent tasks delegate is to know what they have done, not just what they claim. The use of the container addresses this via a unified journalization interface. When you start a session, the tool saves each command, outlet and change of file in the history “.Git” of your repository under a special remote control called “UST-UST-USE”. You can follow when the container turns upwards, the agent performs orders and the environment evolves.

If an agent meets an error or leaves, you don't have to watch the newspapers in a separate window. A simple command reveals an interactive view:

This live view shows you which branch of containers is active, the latest outings, and even gives you the possibility of passing through the shell of the agent. From there, you can manually debug: inspect the environment variables, run your own commands or modify files on the fly. This direct intervention capacity guarantees that agents remain collaborators rather than impenetrable black boxes.

Although the default container images provided by the container use many cases of use of nodes, python and at the system level, you may have specialized needs, for example, personalized compilers or proprietary libraries. Fortunately, you can control the Dockerfile that underlies each container. By placing a “ containerfile ” (or “dockerfile”) at the root of your project, the CU “Cu” will build a tailor -made image before launching the agent. This approach allows you to pre-install system packages, clone private standards or configure complex tool chains, all without affecting your host environment.

A typical personalized dockerfile can start from an official base, add bone level packages, define environmental variables and install specific language outbuildings:

FROM ubuntu:22.04
RUN apt-get update && apt-get install -y git build-essential
WORKDIR /workspace
COPY requirements.txt .
RUN pip install -r requirements.txt

Once you have defined your container, any agent you invoke will work in this default context, inheriting all the preconfigured tools and libraries you need.

In conclusion, when AI agents undertake more and more complex development tasks, the need for robust isolation and transparency increases in parallel. The use of Dagger containers offers a pragmatic solution: containerized environments that guarantee reliability, reproducibility and real -time visibility. Based on standard tools, including Docker, Git and Shell Scripts and providing transparent integrations with compatible popular MCP agents, it reduces the barrier to safe, scalable and multi-agent workflows.

Sana Hassan, consulting trainee at Marktechpost and double -degree student at Iit Madras, is passionate about the application of technology and AI to meet the challenges of the real world. With a great interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.