Build efficient agents with knowledge distillation on Amazon SageMaker AI

The story discusses how to improve the efficiency of agentic applications using knowledge distillation on Amazon SageMaker AI. An agentic assistant is not a single model call, but rather a loop consisting of search, read results, refine, verify, and decide. A single completed task may involve multiple calls to the frontier model, and these per-task costs can compound and erode the project's ROI at production scale.

One way to make the agentic application more efficient is to use a smaller model, which is faster to respond and more cost-effective to host. However, smaller models often fall short on task quality, which is why larger models were chosen initially. Knowledge distillation closes this gap by transferring the behavior of the larger "teacher" model into a smaller "student" model, training the student to reproduce the teacher's outputs while maintaining much of the quality at the smaller model's speed and cost.

Sequence-level knowledge distillation (SeqKD) is the simplest applicable form of distillation, using a teacher model to generate high-quality responses, then training a student model on those responses with supervised fine-tuning. SeqKD requires only the teacher's text outputs, without access to teacher logits or reinforcement-learning infrastructure.

The teacher generates trajectories, which are ordered runs of reasoning, tool calls, and observations, and the student trains on these sequences to learn the reasoning and multi-step course of action. This makes agentic tasks a clean illustration of SeqKD, as the sequence being copied is the behavior itself. The post covers generating high-quality multi-step trajectories with a frontier teacher, encoding them for supervised fine-tuning, running the training on serverless infrastructure, and evaluating the student against a held-out benchmark.