Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints
Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain greater control over their deployments, optimize costs, and align with compliance requirements. However, this flexibility introduces a critical technical challenge: response format incompatibility with Strands agents. While these custom …










