Modern AI applications are no longer single prompts. They are pipelines of decisions: retrieval, reasoning, tool use, and execution. However, most systems still default to a single model across all steps, leading to unnecessary cost, latency, and failure modes.

This talk presents a hands-on evaluation of open-source model routing tools in the context of multi-step AI workflows. Instead of comparing models in isolation, we examine how routing decisions change when facing a multi-step pipeline as part of computer-use task execution.

We’ll cover real experiments, architectural patterns, and common pitfalls, and provide a practical approach to step-aware routing that improves both performance and cost efficiency.