The agent loop, minus the scaffolding #
AI agents promise a powerful new paradigm, but often require significant boilerplate code to manage their core loop: asking a model, invoking tools, returning results, and continuing until a task is complete. Docker Agent simplifies this by acting as a dedicated runtime, handling this iterative cycle automatically. It’s to AI agents what docker run is to containers.
Developers define an agent in a concise YAML file, specifying the model, instructions, and permitted toolsets. This declarative approach replaces much of the orchestration code typically written in Python, allowing teams to version, review, and swap models with a single line change in a pull request.
Docker Agent offers remarkable provider flexibility. It supports hosted models from major providers such as
- [OpenAI](https://www.stork.ai/en/openai-news-partner-api)
- [Anthropic](https://www.stork.ai/en/anthropic-workbench)
- [Google Gemini](https://www.stork.ai/en/google-gemini-1-5-pro)
- [OpenRouter](https://www.stork.ai/en/openrouter-api)
- AWS Bedrock
Additionally, it facilitates local inference via Docker Model Runner (DMR), enabling offline or on-premises execution. Agents can also be distributed via OCI registries like Docker Hub, enabling seamless sharing and execution with a single command.
YAML turns an agent into something teams can own #
Configuration-as-code transforms agent development into a collaborative, auditable process. Teams define agent prompts, model choices, and permitted tools in YAML files, enabling version control, pull request reviews, and easy model switching for different tasks. This declarative approach, akin to infrastructure-as-code, streamlines operational workflows and governance.
Docker Agent extends this with a robust distribution model. Agents package like container images, pushing to OCI registries such as Docker Hub. This allows teams to publish specialized agents, ensuring consistent, reproducible execution across diverse environments with a simple docker agent run <image-name> command.
This setup significantly reduces boilerplate code often seen in traditional agent frameworks. However, the declarative nature presents a trade-off: highly intricate branching logic, custom state management, or specialized, non-standard workflows may still necessitate code-first frameworks like LangChain or AutoGen. For most common agent patterns and team collaboration, Docker Agent offers a compelling, simplified alternative.
One agent investigates; another gets the answer #
Consider a critical production incident: checkout is down, throwing 500 errors. An on-call assistant, lacking direct file access, needs to diagnose the issue. Instead of granting broad permissions, a separate log analyst agent, owned by the platform team, is deployed with read-only access strictly confined to the logs folder.
This specialized log analyst serves as a dedicated expert. The on-call assistant, running without any file access itself, delegates the diagnostic query to the analyst. This communication happens over A2A (Agent-to-Agent), a protocol that enables disparate agents to securely exchange information and requests.
The analyst agent sifts through application logs and deploy history, then returns its findings to the on-call assistant. The assistant then summarizes this information into an incident report, all without ever directly touching sensitive log data. This pattern demonstrates a powerful security boundary.
Separating agents by function and access rights allows teams to narrow potential attack surfaces and enforce the principle of least privilege. While A2A facilitates secure delegation, teams must still meticulously define trust boundaries, permissions, and safe handling of returned information. For deeper exploration of this architecture, visit the Docker Agent GitHub Repository.
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Battleship is a demo, not a benchmark #
Battleship demonstrated how agents interact, not which model reigns supreme. Two player agents, one running GPT-6 Luna and the other Claude Haiku 5.5, competed independently. A third agent, the referee, coordinated turns, ensuring a neutral arbiter for the game logic. This setup provided a concrete test of agent-to-agent communication via the A2A protocol.
Claude Haiku won the match in 34 shots with 50% accuracy. Haiku sank Luna's final ship, a submarine, while Luna had destroyed all but one of Haiku's vessels. This close game was an entertaining demonstration of multi-agent capabilities, but it does not serve as a benchmark for model superiority. Both models exhibited play better than random chance.
Docker Agent makes experimentation and service-style deployment highly approachable. Teams can quickly iterate on agent designs and deploy them as HTTP, MCP, or A2A servers. While agent-to-agent capabilities are evolving rapidly, thoroughly test multi-agent systems for robustness and emergent behaviors before committing them to production.
Frequently Asked Questions #
What is Docker Agent?
Docker Agent is an open-source tool for defining and running AI agents, including their model, instructions, and allowed tools, through configuration files.
How do agents communicate with Docker Agent?
Agents can be served over A2A so other agents can send them requests across a network. Docker Agent can also expose agents through HTTP or MCP.
Can Docker Agent use different AI models?
Yes. It supports multiple hosted model providers and can connect to local models through Docker Model Runner.
Does Docker Agent replace frameworks like LangGraph?
Not in every case. YAML-driven agents can simplify common workflows, while code-first frameworks may suit applications that need highly custom logic.