Artificial Intelligence · 14.08.2026, 16:10 UTC
Building agentic workflows with SageMaker AI and Bedrock AgentCore
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | AWS Machine Learning ↗ |
| Veröffentlicht | 14.08.2026 UTC |
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A common challenge in building agentic workflows is mixing managed foundation models (FMs) with your own cost-optimized or domain-specific models, without rewriting your agent framework to do it. In this post, we show you how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime, a capability of Amazon Bedrock AgentCore, and its managed deployment. Specialized agents can collaborate on complex tasks while each uses the model best suited to its job. This combination gives you cost optimization, data residency, and model flexibility in a single production-ready architecture. We walk through 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. The focus is on the integration mechanics including how to get token-level observability from SageMaker endpoints, which Strands doesn’t provide by default. Solution overview The architecture connects three model-hosting paths through a single Amazon Bedrock AgentCore container: Orchestrator agent (Claude Haiku 4.5 on Bedrock) – Classifies user intent and routes tasks through Global cross-Region inference. Budget agent (Claude Sonnet 4.6 on Bedrock) – Handles 50/30/20 budget breakdowns with structured Pydantic output. Financial analysis agent (Qwen 3.5 9B on Amazon SageMaker AI) – Stock analysis and portfolio construction using tool-calling. Amazon Bedrock model availability varies by AWS Region. See Supported models by AWS …