Building AI Agents with Docker Agent

來源原標題

Building AI Agents with Docker Agent

作者

Shittu Olumide

原始連結

https://www.kdnuggets.com/building-ai-agents-with-docker-agent

摘要原文

Building AI Agents with Docker Agent Docker Agent is an open-source, Apache 2.0-licensed CLI plugin built by Docker Engineering, installed and run as docker agent. Its own tagline states the goal plainly: run AI agents like containers. By Shittu Olumide , Technical Content Specialist on October 8, 2026 in Artificial Intelligence Docker built its reputation on one idea: package software once, run it anywhere, the same way, every time. Docker Agent applies that same idea to AI agents : describe them in a declarative config instead of code, run them through a CLI plugin, and distribute them through the same OCI registries that already store your container images. If you've ever wished an AI agent could be defined, versioned, and shared like a container, that's the gap this tool fills. This is a full, hands-on tutorial, from a completely bare install to a real, working multi-agent team. What Is Docker Agent? Docker Agent is an open-source, Apache 2.0-licensed CLI plugin built by Docker Engineering, installed and run as docker agent . Its own tagline states the goal plainly: run AI agents like containers . Its Go module history on pkg.go.dev shows early tagged releases from March 2026, and it's grown quickly since; the project has already passed 3,300 GitHub stars and nearly 10,000 commits. It didn't appear out of nowhere. Docker spent 2025 building toward exactly this: a July 2025 announcement extended Docker Compose to support agents and AI models directly , and Docker Model Runner shipped as a way to run models locally without a cloud API key. Docker Agent is the product of that groundwork landing in one dedicated tool, rather than a single feature bolted onto Compose. What actually makes it distinctive: agents are defined in YAML (or HCL, if you prefer that syntax), not code, which means no software engineering background is required to build one. It's provider-agnostic, working with OpenAI, Anthropic, Gemini, AWS Bedrock, Mistral, xAI, and fully local models through Docker Model Runner, so a config isn't locked to one vendor. It supports genuine multi-agent orchestration , teams of specialized agents that delegate work to each other. Its tool ecosystem includes built-in tools plus any MCP server , run locally, remotely, or inside its own Docker container for isolation. And, tying back to the container analogy directly, finished agents can be pushed to and pulled from any OCI-compatible registry — the same distribution mechanism Docker images already use. Prerequisites and Installing Docker Agent You need three things: Docker installed on your machine, a way to actually run it, and access to at least one language model. Installation has three real paths. If you're running Docker Desktop 4.63 or newer, the plugin is already there; just run docker agent . Via Homebrew, brew install docker-agent installs the binary directly; run it as docker-agent , or symlink it to ~/.docker/cli-plugins/docker-agent to use the docker agent form instead. For a binary release, download it directly from GitHub Releases and symlink it the same way. Setting up a model comes next, and you have two real options. The simplest is a cloud provider's API key, set as an environment variable: export ANTHROPIC_API_KEY=sk-ant-your-key-here # or OPENAI_API_KEY, GOOGLE_API_KEY, depending on your provider Or skip a cloud key entirely and run a model locally through Docker Model Runner, which the rest of this tutorial will note as an alternative wherever a model is specified. Confirm the install worked with: docker agent --help If that prints a list of commands rather than an error, you're ready for the first real agent. Building Your First Agent The smallest real Docker Agent is a single YAML file. Create agent.yaml : agents: root: model: anthropic/claude-sonnet-4-5 description: A helpful coding assistant instruction: | You are an expert software developer. Help users write clean, efficient code. Explain your reasoning step by step. toolsets: - type: filesystem - type: shell - type: think Code explanation : root is the name of this agent, and every config needs at least one agent with this exact name as its entry point model follows a provider/model-name format, here pointing to Claude Sonnet 4.5 through Anthropic description is a short summary the runtime uses to identify the agent, which becomes important the moment more than one agent exists in a config instruction is the system prompt — the actual behavior you're defining — written in plain language toolsets is a list of capabilities this agent can use: filesystem grants read and write access to files; shell allows running commands think gives the agent a structured space to reason step by step before acting, useful for models without strong native reasoning built in Run it with the interactive terminal UI: docker agent run agent.yaml Or run it non-interactively for a single task, useful in scripts or CI: docker agent run --exec agent.yaml "Create a Dockerfile for a Node.js app" Code explanation: The first command drops you into a live chat session with the agent The --exec flag skips the interactive loop entirely, sends one instruction, prints the result, and exits — the form you'd actually use if you were calling this agent from a script rather than a terminal Giving Your Agent Real Tools A filesystem and a shell are useful, but a genuinely capable agent usually needs to reach outside your machine too. Docker Agent's MCP support is how that happens, and it's worth understanding the recommended pattern specifically: running an MCP server inside its own Docker container, isolated from your host system, rather than as a bare local process. agents: root: model: anthropic/claude-sonnet-4-5 description: Research assistant with memory and web search instruction: | You are a research assistant. Search the web for information, remember important findings, and provide thorough analysis. toolsets: - type: think - type: memory path: ./research.db - type: mcp ref: docker:duckduckgo Code explanation : The memory toolset gives the agent a persistent store at ./research.db , so it can recall facts across turns in a session rather than starting fresh every message The mcp toolset with ref: docker:duckduckgo is the detail worth pausing on — that docker: prefix tells Docker Agent to run the DuckDuckGo MCP server inside its own container , which is the officially recommended way to use MCP tools specifically because it's secure and isolated by default rather than trusting an arbitrary local process with access to your system. This config also validated cleanly against the real schema, confirming the field names and structure are exactly right Building a Multi-Agent Team This is the part that actually shows what Docker Agent is built for. Rather than one agent trying to do everything, you define a small team — each member with a narrow role — and a coordinator that delegates between them. The project for this section: a content research team, a coordinator that hands a topic to a researcher, then passes the findings to a writer for a final report. agents: root: model: anthropic/claude-sonnet-4-5 description: Coordinator for a content research team instruction: | You are a content lead coordinating a small research team. When given a topic, delegate web research to the researcher, then pass the findings to the writer to produce a short, well-organized report. Review the final output before presenting it to the user. sub_agents: [researcher, writer] toolsets: - type: think researcher: model: openai/gpt-5 description: Web researcher who gathers and summarizes findings instruction: | Search the web for current, credible information on the given topic. Summarize the key findings in a structured list, noting the source for each claim. toolsets: - type: mcp ref: docker:duckduckgo - type: memory path: ./research.