Build Strands Agents with SageMaker AI models and MLflow

2026-04-27 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

This post details how to build and manage AI agents using the Strands Agents SDK with models deployed on Amazon SageMaker AI endpoints, integrating SageMaker Serverless MLflow for observability. It addresses enterprise needs for precise control over performance tuning, cost optimization, compliance, and networking. The guide covers deploying foundation models like Qwen3-4B and Qwen3-8B from SageMaker JumpStart, integrating them with Strands Agents, and setting up production-grade observability using SageMaker Serverless MLflow for agent tracing. Furthermore, it demonstrates implementing A/B testing across multiple model variants and evaluating agent performance with MLflow metrics, providing a framework for building, deploying, and continuously improving AI agents on controlled infrastructure.

Key takeaway

For AI Engineers building production-grade AI agents requiring granular control over infrastructure and robust MLOps, consider deploying foundation models on Amazon SageMaker AI endpoints. This approach, combined with Strands Agents SDK and SageMaker Serverless MLflow, enables precise control over compute, cost, and compliance, while providing essential observability and A/B testing capabilities for continuous improvement. You should leverage the provided code examples to set up agent tracing and model evaluation.

Key insights

SageMaker AI and MLflow provide robust control and observability for enterprise AI agent development.

Principles

• Retain architectural control over AI inference.
• Combine model deployment with robust MLOps.
• Use A/B testing for model variant evaluation.

Method

Deploy models via SageMaker JumpStart, integrate with Strands Agents SDK, configure SageMaker Serverless MLflow for tracing, and implement A/B testing with MLflow GenAI evaluation.

In practice

• Deploy Qwen3-4B/8B models on SageMaker AI.
• Use `mlflow.strands.autolog()` for agent tracing.
• Create evaluation datasets for agent performance.

Topics

• Strands Agents SDK
• Amazon SageMaker AI
• SageMaker JumpStart
• SageMaker Serverless MLflow
• AI Agent Observability

Code references

• aws/amazon-sagemaker-examples
• strands-agents/samples

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.