Amazon Web Services has published a technical guide showing how to construct AI agents using the Strands Agents SDK with models hosted on SageMaker AI endpoints. The post explains how to deploy foundation models from SageMaker JumpStart, integrate them with the Strands framework, and set up production-grade tracking using SageMaker Serverless MLflow for agent monitoring.
The tutorial details steps for deploying models, configuring endpoints, and connecting them to the Strands Agents SDK. It also describes how to implement A/B testing across different model variants and measure agent performance using metrics collected by MLflow. The workflow includes creating multiple model versions, routing traffic between them, and comparing results in real time.
Observability is handled through SageMaker Serverless MLflow, which captures agent interactions, model outputs, and system metrics without requiring infrastructure management. The setup supports tracing for debugging, compliance, and performance tuning. AWS emphasizes the system’s ability to scale automatically based on demand.
The guide targets developers building production-ready agents that require reliability and traceability. It aligns with AWS’s push to simplify deployment of large language models in enterprise environments. The post concludes with links to sample code and configuration files for immediate implementation.
Source: aws.amazon.com