Build efficient agents with sequence-level knowledge distillation on Amazon SageMaker AI

What can the developer do now?

Developers can adopt the provided SageMaker AI SeqKD pipeline to train lower-cost, in-house agents that retain much of a large teacher’s agentic performance. Requirements include an AWS account, suitable permissions for SageMaker AI and S3, and access to an API-based teacher model (such as through Amazon Bedrock). The official example demonstrates using a shopping assistant scenario, but the same pattern applies to other multi-step tasks such as retrieval-augmented generation or tool-using chatbots.

• Use a large teacher model via API to generate successful multi-step agent trajectories (including reasoning steps and tool calls).
• Filter to keep only successful trajectories using a programmatic benchmark/scorer.
• Encode agent steps as chat turns and perform supervised fine-tuning of a small model on these traces using SageMaker AI customization.
• Evaluate the resulting student model and deploy it behind a real-time managed endpoint for cost-effective agent execution.

Developers should follow the official code repository and walkthrough for details on data preparation, fine-tuning, and deployment. Notably, successful models such as DeepSeek-R1 have used similar techniques to transfer reasoning capabilities from larger to smaller models.