Amazon Bedrock Flows provides a visual and API-defined way to build end-to-end generative AI workflows from connected nodes. A flow can combine prompts, Bedrock Agents, knowledge-base retrieval, Lambda functions, S3 retrieval, inline code, conditions, iterators, collectors, and other supported nodes. The result is an explicit execution graph that can be versioned, aliased, and invoked from an application.<\/p>\n
Within Generative AI on AWS<\/a>, Flows sits between hand-written orchestration and a fully model-driven agent. It is useful when the application needs a visible graph of deterministic and generative steps rather than one opaque reasoning loop.<\/p>\n The strongest flow is not the most complicated one. It is the one where each node exists because that responsibility is clearer and safer as a separate step.<\/p>\n A prompt node can use an inline prompt or a prompt managed in Amazon Bedrock Prompt management. Inputs fill prompt variables, and the node returns a model-generated response for the next step.<\/p>\n This makes model generation composable. One prompt can classify, another can summarize, and a deterministic node can validate between them. The workflow does not have to treat one giant prompt as the entire application.<\/p>\n Prompt versions and model selection should still be managed through release discipline because a small prompt change can alter every downstream node.<\/p>\n A flow can retrieve from a Bedrock Knowledge Base and pass the results to later nodes. This is useful when retrieval should happen at a known stage rather than being left to an autonomous agent to decide.<\/p>\n The existing Amazon Bedrock Knowledge Bases<\/a> article provides the wider retrieval context. Flow design should still define what happens when retrieval returns weak or no results.<\/p>\n The planned Testing RAG Quality on AWS<\/a> article matters because a workflow can execute perfectly while retrieving the wrong evidence.<\/p>\n Flows can call Lambda functions and execute supported inline code nodes. These steps are appropriate when the workflow needs deterministic transformation, validation, API calls, or business logic that should not be left to probabilistic text generation.<\/p>\n This separation improves testing. A currency calculation, permission check, schema transform, or database lookup can be validated with ordinary unit tests while the model remains responsible for language or reasoning tasks.<\/p>\n The existing Lambda event-driven design<\/a> article provides useful serverless context.<\/p>\n A flow can branch based on conditions rather than asking a model to narrate which branch it thinks should be followed. This is valuable for known business rules, thresholds, or result categories that can be expressed deterministically.<\/p>\n The more consequential the branch, the stronger the case for deterministic conditions. A model can still generate the data being evaluated, but the actual routing rule can remain visible and testable.<\/p>\n Condition design should avoid hidden defaults where an unexpected input silently falls into a business-critical branch.<\/p>\n Bedrock Flows can include a Bedrock Agent node. This lets a larger deterministic workflow delegate one part of the problem to an agent that can reason and use tools.<\/p>\n That is often stronger than turning the entire process into one agent. The workflow can keep fixed sequencing, approvals, or data movement outside the agent while using autonomy for the step that genuinely benefits from flexible reasoning.<\/p>\n The planned Step Functions for Agentic Workflows<\/a> article extends this pattern into broader AWS workflow orchestration.<\/p>\n To invoke a production flow, AWS requires an alias that points to a specific flow version. The alias can later be repointed to another version for upgrade or rollback.<\/p>\n This is an important operational boundary. Applications call a stable alias while the flow definition evolves through immutable versions. A release can therefore be evaluated, promoted, and rolled back without changing every caller.<\/p>\n The deployment process should record which flow version an alias referenced at the time of an incident or quality evaluation.<\/p>\n Bedrock Flows supports asynchronous execution for workloads that should not hold one synchronous request open from start to finish. This is useful when nodes call slower systems, process larger inputs, or coordinate steps that exceed interactive response expectations.<\/p>\n The application should expose execution state clearly. \u201cFlow accepted\u201d is not the same as \u201cbusiness process completed.\u201d Callers need an execution identifier and a way to retrieve final status or failure details.<\/p>\n Retries at the application layer should not start a second flow blindly when the first execution may still be running.<\/p>\n One advantage of explicit graphs is that operations can reason about which step failed. Monitoring should capture node duration, error type, input\/output shape where safe, model invocation metrics, and external dependency status.<\/p>\n If a prompt node becomes slower after a model change, that is different from a Lambda timeout or a knowledge-base retrieval failure. The flow should make those distinctions visible enough that each owning team can respond.<\/p>\nPrompt nodes keep model generation as one step in a larger workflow<\/h3>\n
Knowledge-base nodes make retrieval an explicit dependency<\/h3>\n
Lambda and inline code nodes handle deterministic business logic<\/h3>\n
Condition nodes make branching policy visible<\/h3>\n
Agent nodes are useful when one step genuinely requires autonomous reasoning<\/h3>\n
Flow versions and aliases create a deployable production contract<\/h3>\n
Asynchronous flow executions fit long-running workloads<\/h3>\n
Flow observability should identify the failing node<\/h3>\n