How are workflows deployed for production?

Flows are managed through a lifecycle of preparation, versioning, and alias creation, allowing for stable API-based invocation and safe rollbacks.

What is the primary purpose of Amazon Bedrock Flows?

It is a visual workflow builder designed to orchestrate generative AI applications by linking foundation models, prompts, knowledge bases, and other AWS services without writing extensive glue code.

What kind of flow control is available?

It supports a variety of logic nodes including Condition nodes for branching, Iterators for processing arrays, and DoWhile loops for repetitive operations.

Can I use Amazon Bedrock Flows for RAG applications?

Yes, it includes dedicated Knowledge Base nodes that allow you to query vector stores and generate responses using retrieved context directly within the flow.

Does this skill support custom logic within workflows?

Absolutely. You can integrate AWS Lambda functions or use inline code nodes to perform custom data transformations or trigger external API calls.

Amazon Bedrock Flows

Name: Amazon Bedrock Flows
Author: adaptationio

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Orchestrates multi-step generative AI workflows using Amazon Bedrock's visual flow builder and specialized logic nodes.

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Amazon Bedrock Flows provides a structured framework for building, deploying, and managing complex generative AI applications within the AWS ecosystem. This skill enables Claude to architect end-to-end pipelines that link foundation models, prompts, and knowledge bases with advanced logic components like conditional branching, iterators, and DoWhile loops. It is particularly useful for developers building production-grade RAG applications, multi-agent systems, and automated data processing pipelines that require seamless integration with AWS services like Lambda and S3 while maintaining robust version control.

Características Principales

Advanced flow control using condition branching and iterative loops
Production-ready deployment with versioning and alias management
Native connectivity to AWS Lambda and S3 for custom logic and storage
0 GitHub stars
Visual orchestration of foundation models and prompt templates
RAG integration via native Bedrock Knowledge Base nodes

Casos de Uso

Automating multi-model AI pipelines with error handling and parallel execution
Creating dynamic content routing systems based on real-time sentiment or intent
Building complex RAG applications with multi-step retrieval and synthesis

Acerca de

Características Principales

Advanced flow control using condition branching and iterative loops
Production-ready deployment with versioning and alias management
Native connectivity to AWS Lambda and S3 for custom logic and storage
0 GitHub stars
Visual orchestration of foundation models and prompt templates
RAG integration via native Bedrock Knowledge Base nodes

Casos de Uso

Automating multi-model AI pipelines with error handling and parallel execution
Creating dynamic content routing systems based on real-time sentiment or intent
Building complex RAG applications with multi-step retrieval and synthesis