Amazon Bedrock Flows—referred to as “Amazon Bedrock Prompt Flows” in the question—is the lowest-complexity solution because the workflow consists primarily of generative-AI processing stages and one straightforward conditional branch. Bedrock Flows provides a managed visual workflow environment in which nodes represent steps that invoke Amazon Bedrock or related resources. This eliminates the need to build a separate orchestration service merely to connect multiple model interactions.
A prompt node defines a prompt, receives values as input variables, invokes the configured model, and produces the model completion as its output. Therefore, separate prompt nodes can represent document analysis, structured data extraction, fraud analysis, and recommendation generation.
A condition node provides deterministic branching. AWS documents relational operators including > , > = , < , < = , == , and != , and the node can direct its input to different downstream nodes based on the configured condition. The application can therefore test whether the claim amount is greater than $10,000. Claims above that threshold are routed through the fraud-analysis node before reaching the recommendation stage; other claims can go directly to recommendation generation.
A Step Functions implementation in A would work technically and is appropriate when orchestration spans a broad set of distributed AWS services. However, for a Bedrock-centric GenAI workflow, it adds another orchestration layer and therefore is not the least-complex solution. B unnecessarily introduces autonomous agent reasoning for deterministic sequential processing. D requires custom Lambda invocation code, error handling, routing logic, deployment, and maintenance.
Because the required workflow maps directly to Bedrock ' s managed prompt and condition nodes, C minimizes infrastructure and custom orchestration while preserving deterministic control over the $10,000 fraud-analysis requirement.
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