Migrating multi-model AI agents to Amazon Bedrock AgentCore runtime
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This AWS post explains how to migrate a multi-model healthcare AI agent from self-managed Amazon ECS with AWS Fargate to Amazon Bedrock AgentCore runtime without changing its core agent logic. The agent, built with the Hugging Face smolagents framework, orchestrates Amazon Bedrock’s Llama 3.1 70B Instruct, a BioM-ELECTRA-Large-SQuAD2 deployment on Amazon SageMaker AI, and a containerized model server, while Amazon OpenSearch Service provides vector-enhanced knowledge retrieval. AgentCore wraps the existing code with BedrockAgentCoreApp, @app.entrypoint, and app.run(), and manages container lifecycle, scaling, IAM-based identity, and observability. The post details prerequisites, project setup, Docker packaging, CLI deployment, and invocation through the AgentCore CLI or boto3. It contrasts this managed approach with ECS and Fargate, which provide greater control over container, networking, and scaling configuration but require teams to manage those components. The sample is intended for demonstration, and the article notes that production systems handling medical or other sensitive queries should use Amazon Bedrock Guardrails.
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