Writing AI agents in YAML instead of Python

This article walks through Docker's open-source docker-agent tool, which defines an AI agent's model, instructions, and toolsets in a single YAML file and runs it with one command. Using a Polars documentation Q&A agent, it demonstrates a built-in RAG toolset with local SQLite vector storage, per-run model overrides, prompt files, flavors for variants, multiple agents sharing tools, JSON-based evals, and pushing agents to a registry. It notes that code-first frameworks like LangGraph suit cases requiring custom branches or retries.