Build a 60-Line Python Switchboard to Route AI Tasks Across LLM Providers

A developer has shared a minimal, standard-library-only Python pattern for routing AI tasks to different large language model backends without rewriting test scripts each time a new model launches. The switchboard, around 60 lines of code, maps task types such as summarize or code to a prioritized list of backends, automatically falling back to the next option if one fails. No third-party packages or API keys are required to run the core experiment, making it accessible for quick evaluation. The approach separates what is being asked from which model answers it, so evaluation questions remain stable even as backends change. The tutorial includes a failing fallback fixture to demonstrate exactly where and why routing breaks under edge cases like empty input.