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 why the larger one was chosen in the first place. Knowledge distillation closes that gap by transferring the behavior of the larger “teacher” model into a smaller “student”, training the student to reproduce the teacher’s outputs, so it keeps much of the quality at the smaller model’s speed and cost.

In this post you will learn how to use knowledge distillation, and specifically sequence-level knowledge distillation (SeqKD), to fine-tune a shopping agent on Amazon SageMaker AI . We cover how to generate high-quality multi-step trajectories with a frontier teacher and keep the ones that solve the task, how to encode them for supervised fine-tuning, how to run the training on serverless infrastructure, and how to evaluate the student against a held-out benchmark.

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