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Agents · Aug 15, 2026

AWS demonstrates multi-agent workflows combining SageMaker endpoints with Bedrock AgentCore

A new AWS blog post shows how to integrate OpenAI-compatible SageMaker endpoints with Bedrock AgentCore to route tasks to specialized models and add token-level observability not provided by Strands Agents.

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TL;DR
  • AWS describes an architecture that combines OpenAI-compatible SageMaker endpoints with Bedrock AgentCore to build multi-agent workflows where each agent uses the model best suited to its task.
  • The post details deploying Qwen 3.5 9B on SageMaker, integrating it into a Strands Agents system, and shipping the workflow to Bedrock AgentCore runtime.
  • AWS highlights token-level observability from SageMaker endpoints, which Strands Agents does not provide by default.

Amazon Web Services describes a method to combine OpenAI-compatible endpoints on Amazon SageMaker AI with the Amazon Bedrock AgentCore runtime to build multi-agent workflows where each specialized agent uses the model best suited to its job.

The architecture connects three model-hosting paths through a single Amazon Bedrock AgentCore container: an orchestrator agent using Claude Haiku 4.5 on Bedrock, a budget agent using Claude Sonnet 4.6 on Bedrock, and a third path for custom SageMaker endpoints such as Qwen 3.5 9B.

The post details deploying Qwen 3.5 9B on Amazon SageMaker AI, integrating it into a Strands Agents multi-agent system alongside models on Amazon Bedrock, and shipping the entire workflow to Amazon Bedrock AgentCore runtime.

AWS emphasizes token-level observability from SageMaker endpoints, which Strands Agents does not instrument by default, as a key integration benefit for production monitoring and debugging.

Sources
  1. 01AWS — Machine Learning Blog — Building agentic workflows with SageMaker AI and Bedrock AgentCore
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