Orchestrate multiple AI agents with cagent by Docker to create coding assistant (tobiasfenster.io)

Docker has released “cagent,” an experimental multi-agent runtime designed to orchestrate AI agents with specialized roles and tool access, demonstrated here as a coding assistant for Microsoft Dynamics 365 Business Central (AL language). In this setup, a root agent coordinates three subagents tasked respectively with code development, review, and Git operations like branching and commits. These agents leverage filesystem and shell access, alongside integration with tools like the AL MCP Server for understanding compiled AL packages and a Git MCP server for source control tasks. This modular approach allows each agent to focus on distinct coding or operational goals while collaborating seamlessly under the root agent’s supervision. The significance of cagent lies in its simplicity, configurability via YAML files, and transparency in execution—qualities that strongly appeal to developers wanting to experiment with multi-agent AI workflows. By allowing straightforward definition of agents, their models (e.g., OpenAI’s GPT-5-chat), and tools, cagent supports flexible, terminal-based pipeline orchestration easily version-controlled alongside source code. Furthermore, it supports Azure OpenAI and multi-arch Docker builds, facilitating easy local or cloud deployment. While not perfect and still early-stage, cagent showcases the practical potential for AI-driven development assistants capable of autonomous collaboration on coding tasks and beyond, providing visibility into each agent’s decisions and tool calls that aids debugging and iterative improvement. This approach extends well beyond coding assistants, opening pathways to build multi-agent systems specialized for varied AI applications, underscoring the growing importance of composable, transparent AI agent orchestration frameworks within the AI/ML community.