Introducing Reflection Beam: an open-weight model for agentic coding on AWS
How-To How to actually use this
What changed: Reflection AI's Beam, an open-weight model for agentic coding, is now deployable on AWS via SageMaker JumpStart or HyperPod with SGLang/vLLM support.
How to use it:
- Go to Amazon SageMaker JumpStart and search for "Reflection Beam" to deploy a real-time endpoint.
- For distributed GPU control, use Amazon SageMaker HyperPod Inference with JumpStart integration.
- Configure the deployment using SGLang or vLLM backends for Kubernetes-native orchestration.
- Test the endpoint with coding prompts to verify agentic task handling.
- Monitor performance and scale via AWS CloudWatch or HyperPod metrics.
Good for: developers building agentic coding workflows on AWS infrastructure.
Reflection AI's Beam is an open-weight model for agentic coding and long-horizon tasks. This post shows how to deploy Beam on AWS: start with an Amazon SageMaker JumpStart real-time endpoint, or use Amazon SageMaker HyperPod Inference with JumpStart, SGLang, or vLLM for Kubernetes-native control over distributed GPU infrastructure.
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