Build Strands Agents with SageMaker AI models and MLflow
AWS just made it dead simple to build production AI agents. Here's what changed.
Why it matters
AWS is lowering the friction for enterprises to build and deploy agentic AI systems on their own infrastructure, combining Strands Agents SDK with SageMaker endpoints and MLflow observability. This matters because it positions AWS as a viable alternative to managed agent platforms while giving builders control over models and data.
The key facts
11 to knowStrands Agents SDK integration with SageMaker AI endpoints
Foundation models deployable via SageMaker JumpStart
Production-grade observability using SageMaker Serverless MLflow
Agent tracing capability
A/B testing across multiple model variants
MLflow metrics for agent performance evaluation
Infrastructure control (self-managed vs. managed platform)
Foundation models deployed via SageMaker JumpStart
Production-grade observability via SageMaker Serverless MLflow
Agent tracing and MLflow metrics integration
Infrastructure control (deploy on customer-managed infrastructure)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: 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…