Docker Agent is an open-source Docker tool that lets you define an AI agent in a YAML file — model, instructions, tools — and run it with a single command, with Docker handling the agent loop (call model, run tools, feed results back). The video walks through a minimal agent, then a two-agent incident-debugging setup where one agent holds the logs and another queries it over the A2A protocol, and finishes with three agents playing a full battleship match. The pitch is that agents become versionable config you can push to a registry and expose as HTTP, MCP, or A2A services instead of hand-written Python boilerplate.
- Docker Agent is positioned as 'docker run for AI agents': a YAML file specifies the model, description, instructions, and allowed tools, and `docker agent run agent.yaml` starts a terminal UI you can chat with.
- The model line can point at OpenAI, Anthropic, Gemini, OpenRouter, or a local model via Docker Model Runner, so swapping models is a one-line change.
- Tools come from a toolsets section covering built-ins like file access and shell commands plus any MCP server; agents can also have sub-agents so a coordinator hands work to specialists.
- Three claimed advantages: agents as versionable configuration reviewable in pull requests, distribution by pushing to Docker Hub or any OCI registry, and running an agent as an HTTP API, MCP server, or A2A server with one command.
- The on-call demo shows a security benefit: a log analyst agent has read-only file access scoped to the logs folder, while the on-call assistant has no file access and only knows the analyst's URL, so other teams can ask questions without getting the logs.
- In the demo the analyst is served with `docker agent serve a2a` on port 8080; the on-call agent asks why checkout is throwing 500s since 2 p.m., and the analyst reads app logs and deploy history and returns findings that become an incident summary.
- The battleship setup uses three agents — two players on ports 8080 and 8081 and a separate referee running a different model so neither player judges its own game — with the board on a local server.
- Claude Haiku beat the other model in 34 shots at 50% accuracy with both sides down to their last ship; the presenter explicitly says this is a game, not a benchmark, though both played better than random.
- Docker Agent ships by default with Docker Desktop 4.63 or newer, or can be installed via Homebrew and symlinked into the Docker CLI plugins folder.
This is the core argument for the tool — config, registry distribution, and one-command services — compressed into 40 seconds.
The first is that agents become configuration. You can version them, review them in pull requests, and swap the model by just changing one line. The second is distribution... And third is that agents can run as services.
The clearest practical payoff in the video: agent-to-agent delegation as an access-control boundary between teams.
The first one is a log analyst and it's the only agent that can read the logs... The second one is an on-call assistant and it has no file access at all. The only thing it knows is the analyst's URL.
Docker describes it as being to AI agents what docker run is for containers.
In a real company, the platform team would own the log analyst and run it next to the logs. Any other team's agent could ask it questions without ever getting access to the logs themselves.
This is just a simple game, so I wouldn't call it a benchmark, but both models clearly played better than random.