BAML turns structured LLM output into a testable interface

Production agents eventually fail if every extraction, classification, and tool-preparation step depends on a free-form prompt and best-effort JSON parsing. Boundary's BAML documentation exposes a more explicit interface layer for LLM calls: a BAML source tree, generated clients, language setup paths for Python, TypeScript, Go, Ruby, Rust, REST, and Elixir, editor integrations for VSCode, Cursor, JetBrains, Zed, and Claude Code, plus framework support for React and Next.js hooks. The docs index also makes operational features visible: function tests, streaming, error handling, timeouts, concurrent calls, abort signals, token collectors, LLM client registries, dynamic types, reusable prompt snippets, prompt caching metadata, checks and asserts, prompt optimization, OpenAPI deployment, and Boundary Studio usage tracking. Its agent instructions are themselves agent-readable: append `.md` for clean Markdown, append `/llms.txt` for section indexes, or connect to the docs MCP server at `https://docs.boundaryml.com/_mcp/server`. For agent builders, the practical signal is to treat structured output as a typed, testable, observable contract. The evaluation question is not whether a model can usually emit JSON, but whether extraction interfaces can be versioned, tested, streamed, retried, validated, observed, and swapped across model providers without burying production logic inside prompt text.