Build Strands Agents with SageMaker AI models and MLflow
TL;DR
In this post, we demonstrate how to build AI agents using Strands Agents SDK with models deployed on SageMaker AI endpoints. You will learn how to deploy foundation models from SageMaker JumpStart, integrate them with Strands Agents, and establish production-grade observability using SageMaker Serverless MLflow for agent tracing. We also cover how to implement A/B testing across multiple model variants and evaluate agent performance using MLflow metrics and show how you can build, deploy, and continuously improve AI agents on infrastructure you control.
Nauti's Take
Nauti sees real value here for AWS-native teams: Strands Agents plus MLflow tracing offers a thoughtful pipeline for production agents you fully control, with A/B testing and observability from day one. The ability to plug in JumpStart foundation models without vendor magic is genuinely useful.
The catch: significant AWS lock-in and setup overhead only pay off at meaningful scale. Solo builders and multi-cloud teams should weigh the cost-benefit carefully before adopting.
Sources
