Build Strands Agents with SageMaker AI models and MLflow
Deploy custom models, trace agent actions, and A/B test variants on your own infrastructure.
Enterprises building AI agents often hit limits with managed foundation model (FM) services: they need precise control over performance tuning, cost optimization at scale, compliance and data residency, model selection, and networking configurations that integrate with existing security architectures. Amazon SageMaker AI endpoints address these needs by giving organizations control over compute resources, scaling behavior, and infrastructure placement, while still benefiting from AWS's managed operational layer. Now, SageMaker AI integrates with the open-source Strands Agents SDK, enabling developers to build agents using models deployed on SageMaker endpoints—whether from JumpStart (like Llama, Mistral) or custom fine-tuned variants.
Strands Agents SDK takes a model-driven approach, letting you create agents in just a few lines of code by combining a model, a system prompt, and tools. The integration supports SageMaker AI MLflow for production-grade observability: you can trace agent actions, log metrics, and evaluate performance across model variants. This also enables A/B testing—deploy multiple model versions, route traffic, and compare results using MLflow metrics. For enterprises with strict latency SLAs, high-volume workloads, or advanced MLOps requirements, this combo delivers cost predictability (via reserved instances or spot pricing) and full architectural control over inference, all while leveraging SageMaker's managed infrastructure.
- Deploy foundation models from SageMaker JumpStart (e.g., Llama, Mistral) or custom variants with full control over compute, networking, and scaling.
- Build AI agents in a few lines of code using Strands Agents SDK, integrating models, system prompts, and tools.
- Achieve production-grade observability with SageMaker Serverless MLflow for agent tracing, A/B testing, and performance evaluation.
Why It Matters
Gives enterprises full infrastructure control for AI agents, combining cost predictability, MLOps, and observability beyond managed FM services.