Best AI Tools for Docker: How Pros Actually Use Them

The #1 Mistake: Treating AI Like a Dockerfile Generator

Here’s what kills productivity: developers ask ChatGPT or Copilot to “create a Dockerfile for my Node app,” copy-paste the result, then spend three hours debugging why it won’t build in CI, bloats to 2GB, or breaks in production.

The real value isn’t generation – it’s the feedback loop. AI tools like Claude operate on textual patterns, while experienced developers understand why Dockerfiles should follow certain principles. The winning move: use AI tools for Docker and containerization as collaborators that help you iterate on context-aware configurations, not as magic wands.

Let’s reverse-engineer how senior engineers actually use these tools.

The Context Problem: Why General AI Fails at Docker

Most AI coding assistants excel at writing functions. Docker’s different – it’s infrastructure as code where a single misplaced instruction tanks build time by 10x or introduces a CVE-riddled base image.

The gap: AI assistants can generate Dockerfiles and docker-compose files in seconds, but AI will happily generate a setup that looks polished and still collapses the moment your app grows or you deploy to a different environment. You need tools that understand Docker’s layering, caching, security models, and multi-stage builds – not just syntax.

Three approaches emerged in 2025-2026:

• Docker-native AI (Gordon, Docker extension for GitHub Copilot): trained exclusively on Docker documentation and patterns
• IDE-embedded AI (Cursor, GitHub Copilot): general coding assistants with Docker awareness through extensions
• Sandboxed AI agents (Claude Code in Docker containers): full autonomy with isolation

Method A: Docker-Native AI (Gordon + GitHub Copilot Extension)

Docker’s own AI assistant Gordon launched in beta in late 2024. Gordon is Docker’s AI assistant available to help with tasks like containerizing apps, and now includes new DevSecOps capabilities. It’s embedded directly in Docker Desktop and the CLI.

What Gordon Actually Does Well

The Docker AI Agent helps developers accelerate their work, providing real-time assistance and actionable suggestions, delivering expert-level guidance on Docker-related questions. Unlike generic LLMs, it has access to your running containers, images, and logs.

Strengths:

• Context-aware troubleshooting: it sees your failed container logs and suggests fixes
• Security scanning integrated with Docker Scout
• Generates Dockerfiles with actual knowledge of official base images

Limitations:

• Still in beta; feature set expanding through 2025-2026
• Best for Docker Desktop users (CLI support is lighter)

GitHub Copilot Docker Extension

The Docker Copilot extension currently supports Node, Python, and Java-based projects (single-language or multi-root/multi-language projects). The extension supports containerizing projects from scratch for Go, Java, JavaScript, Python, Rust, and TypeScript.

The Docker Extension for GitHub Copilot is currently in limited public beta, and you can sign up for the waitlist if you’re a GitHub Copilot subscriber. To use it, invoke @docker in GitHub Copilot Chat: “@docker, how can I start a container with a volume?”

The extension provides summaries of project vulnerabilities using Docker Scout and offers next steps via the CLI, integrating Docker Scout to enhance the user experience.

When to use this approach: You’re already a GitHub Copilot user, work primarily in VSCode or GitHub.com, and want Docker help without switching tools.

Method B: IDE-Embedded AI with Full Docker Environments

Cursor IDE and GitHub Copilot both offer ways to work inside containerized dev environments, but they differ in maturity.

Cursor + DevContainers

DevPod’s latest release, v5.20, comes with Cursor’s experimental integration enabled by default. Both VS Code and Cursor use the Dev Containers extension to detect, build, and connect to devcontainers by reading devcontainer.json in the .devcontainer/ directory.

However, there’s a catch. Multiple developers report that Cursor fails to attach to dev containers, with the issue persisting as of November 2024. Community workarounds exist, but official support remains experimental.

What works:

• Opening projects in DevPod with Cursor as the IDE backend
• Using .devcontainer/ configurations for isolated environments
• Running AI agents (like Claude Code) inside containers to bypass permission prompts

In this setup, you can open your project in Cursor, and by the end you’ll have a secure sandbox where you can run –dangerously-skip-permissions without the actual danger.

GitHub Copilot + Docker Compose

GitHub Copilot extends its expertise to technologies like Terraform, Docker, Kubernetes, GitHub Actions, and customized files. It can generate Docker Compose files through natural language prompts.

A real workflow: Utilize Cursor AI’s natural language processing to convert your service requirements into Docker Compose configurations, and for each service instruct the AI to define the image, build configurations, and necessary parameters.

When to use this approach: You need multi-service local development stacks (frontend + backend + database), want AI to generate docker-compose.yaml files, and prefer working in traditional IDEs.

The Winner for Most Advanced Users: Sandboxed AI Agents

If you’re comfortable with risk and want maximum AI autonomy, run coding agents inside Docker containers. This gives you full “YOLO mode” without endangering your host machine.

Docker Sandboxes with Claude Code

Docker Sandboxes provide disposable, isolated microVM environments purpose-built for coding agents, where each agent runs in a completely isolated version of your development environment.

Setup is straightforward:

docker sandbox run claude my-project

Claude launches with –dangerously-skip-permissions by default in sandboxes, and you can build custom templates based on docker/sandbox-templates:claude-code.

Claude Code requires an Anthropic API key, and the recommended approach is to set the ANTHROPIC_API_KEY environment variable in your shell configuration file.

Pro tip:For coding tasks, context length matters – while models like glm-4.7-flash and qwen3-coder come with 128K context by default, gpt-oss defaults to 4,096 tokens. Repackage models with docker model package --from ai/gpt-oss --context-size 32000 gpt-oss:32k for better results on real codebases.

Running Claude Code with Local Models

Claude Code supports custom API endpoints through the ANTHROPIC_BASE_URL environment variable, and since Docker Model Runner exposes an Anthropic-compatible API, integrating the two is simple.

If you are running Docker Model Runner via Docker Desktop, make sure TCP access is enabled – once enabled, Docker Model Runner will be accessible at http://localhost:12434.

export ANTHROPIC_BASE_URL=http://localhost:12434
claude "refactor this Dockerfile to use multi-stage builds"

This setup means your code never leaves your machine. No API costs. Full privacy.

Security Reality Check

The container’s enhanced security measures allow you to run claude –dangerously-skip-permissions to bypass permission prompts, but when executed with this flag, devcontainers don’t prevent a malicious project from exfiltrating anything accessible in the devcontainer including Claude Code credentials.

Use sandboxes for trusted projects only. They isolate your host OS, but they can’t protect against a compromised codebase.

Docker MCP Toolkit: The 2026 Game-Changer

Docker MCP makes it easy for AI agents to securely call MCP servers, with access to 200+ verified MCP servers for tools like Stripe, Notion, GitHub, and more with one-click setup.

The Model Context Protocol bridges Claude Code and Docker Desktop, giving Claude real-time access to Docker’s tools – instead of context-switching between Docker, terminal commands, and YAML editors, you describe your requirements once and Claude handles the infrastructure details.

Real Example: Generating Docker Compose from Natural Language

With Claude Code connected to Docker MCP Toolkit:

Generate a Docker Compose file (docker-compose.yaml) with:
- app: runs on port 3000, bind mounts ./app into /usr/src/app
- db: running on port 5432 using a named volume
Include environment variables for Postgres, a shared bridge network, and healthchecks

Claude will search images through MCP, inspect the app directory, and generate a Compose file that mounts and runs your local code.

The output includes pinned image versions, digest verification, proper networking, and production-ready patterns – not just a “hello world” template.

Edge Cases and Gotchas Nobody Mentions

1. GitHub Copilot + Docker Context Limits

When using GitHub Co-Pilot with Docker MCP servers, you may encounter an error: “You may not include more than 128 tools in your request” – the fix is to reduce the number of tools in your request to 128 or fewer.

If you enable too many MCP servers, Copilot chokes. Stick to 3-5 relevant tools per session.

2. AI-Generated Dockerfiles Miss Image Versioning

Claude’s Dockerfile wasn’t using the latest Python image as of June 2025, and it’s helpful to lock it down to a patch release to keep the build as reproducible as possible.

Always review AI-generated FROM statements. Pin to patch versions (python:3.11.6-slim, not python:latest).

3. Multi-Service Stacks Become Buggy Fast

As the application got more complex and deviated from the original prompt, it became buggier – this makes sense, since the first few changes were large-scale and required hundreds of lines of code to be updated.

Start with a working MVP. Make incremental changes. AI assistants drift when context grows beyond initial scope.

4. Containerized AI Infrastructure Needs Orchestration

Tools like JupyterLab, Airflow, MLflow, Redis, and FastAPI form the backbone of a modern MLOps architecture that’s clean, scalable, and endlessly adaptable – if you’re serious about implementing an AI infrastructure, don’t start with the models; start with the containers.

For production AI workloads, pair AI-generated Dockerfiles with proper orchestration (Kubernetes, Docker Swarm, or managed services). Don’t run inference APIs from a laptop.

Your Next Action

Pick one tool based on your current workflow:

• Already use GitHub Copilot? Join the Docker extension waitlist and start asking @docker questions in Copilot Chat.
• Want maximum control? Install Docker Model Runner, set up Claude Code with local models, and run your first sandboxed agent session today.
• Need production-ready stacks? Connect Claude Code to Docker MCP Toolkit (requires Docker Desktop 4.48+) and generate a Compose file for your current project.

The real skill isn’t prompting AI to write Dockerfiles – it’s knowing when AI’s output is subtly wrong, and how to fix it before it reaches production. Build that skill by running AI-generated configs locally, breaking them intentionally, and learning Docker’s actual behavior.

For more advanced patterns, explore the Docker official documentation and experiment with Docker’s AI platform.

FAQ

Can I use GitHub Copilot to generate production-ready Dockerfiles?

Yes, but not without review. GitHub Copilot and similar tools can generate syntactically correct Dockerfiles, but they often miss optimization patterns (multi-stage builds, layer caching, minimal base images) and security best practices (pinned versions, non-root users). Use AI to accelerate the first draft, then apply Docker knowledge to refine. The Docker extension for GitHub Copilot (currently in beta) is trained specifically on Docker patterns and performs better than generic Copilot for containerization tasks.

What’s the difference between Docker Sandboxes and devcontainers for AI coding agents?

Docker Sandboxes use microVM-based isolation and are purpose-built for running autonomous AI agents like Claude Code, Copilot CLI, and Gemini CLI with full permissions in a disposable environment. Devcontainers (used by VSCode/Cursor) are Docker containers that provide consistent development environments but don’t offer the same level of hypervisor-based isolation. Sandboxes are better for “YOLO mode” agent autonomy; devcontainers are better for reproducible team development setups.

How do I prevent AI-generated Docker configs from introducing security vulnerabilities?

Three steps: First, use Docker-native AI tools like Gordon or the GitHub Copilot Docker extension, which integrate Docker Scout for automatic vulnerability scanning. Second, always pin base image versions to specific digests (not tags like latest) to ensure reproducibility and prevent supply chain attacks. Third, review AI-generated configs for common mistakes: running as root, installing unnecessary packages, exposing secrets in ENV variables, or using outdated base images. Tools like the Docker extension for GitHub Copilot will flag some of these automatically, but manual review is essential for production workloads.