Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one workload to another—or even within the same workload. For example, an agentic task may need classification for one step, reasoning for the next, and a smaller model for routine follow-up tasks. Sending every request to the largest model can increase cost and latency, while sending every request to a smaller model can reduce quality on complex tasks.

Model routing addresses this challenge by orchestrating specialized and frontier models so that each task uses the model best suited to each task.…