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Build efficient agents with knowledge distillation on Amazon SageMaker AI

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Agentic assistants built on frontier models run a multi-step tool loop, and paying frontier prices on every step is hard to sustain at production scale. This post shows how to use sequence-level knowledge distillation (SeqKD) to transfer a frontier teacher's agentic behavior into a small, lower-cost student on Amazon SageMaker AI.

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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,…

O overfeed.news indexa e aponta. Publicamos um trecho curto — o artigo completo fica em AWS Machine Learning Blog.

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