Building agentic workflows with SageMaker AI and Bedrock AgentCore
AWS shows how to chain specialized agents across SageMaker and Bedrock — practical multi-agent orchestration with token-level visibility.
Why it matters
A vendor deep-dive on agent infrastructure: how to deploy heterogeneous agents (each using the model best suited to its task) and get observability that commercial agent frameworks don't provide by default. Useful for practitioners building production agent stacks.
The key facts
9 to knowMulti-agent workflow combining SageMaker AI (OpenAI-compatible endpoints) and Bedrock AgentCore runtime
Token-level observability from SageMaker endpoints — instrumentation gap the post addresses
Agent specialization pattern: each agent uses the model best suited to its job
AWS blog; published Aug 14, 2026
AWS combines SageMaker AI (OpenAI-compatible endpoints) with Bedrock AgentCore runtime for multi-agent workflows
Pattern: specialized agents selected by model suitability for their task
Addresses token-level observability gap—Strands Agents does not instrument by default
Implies Bedrock AgentCore as a standardized agent orchestration layer
Published Aug 14, 2026 (AWS blog — vendor content but with actionable technical depth)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Learn how to combine OpenAI-compatible endpoints on Amazon SageMaker AI with Amazon Bedrock AgentCore runtime to build a multi-agent workflow where each specialized agent uses the model best suited to its job. This post also shows how to get token-level observability from SageMaker endpoints that…