Build efficient agents with knowledge distillation on Amazon SageMaker AI | Amazon Web Services
You built a shopping assistant on a frontier model, and it works. A shopper asks for “a black waist bag under twenty dollars from a shop with at least 4.5 stars,” and the agent searches the catalog, inspects candidates, checks the constraints, and commits to a recommendation. But what does that interaction actually cost? An agentic assistant is not one model call, it is a loop: search, read results, refine, verify, decide. A single completed task can be multiple calls to that frontier model, and you pay for every one, on every shopper. At production scale, that per-task cost compounds, erodes the project’s ROI, and stops the project from shipping.
One way to make an agentic application more efficient is to use a smaller model, which is faster to respond and more cost-effective to host. The catch is quality: a smaller model usually falls short on the task, which is...
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