Pattern 4: Multi-stage AI workflow

Many real-world AI applications are not served by a single model or function. Instead, they require a sequence of AI-driven tasks, often interleaved with business logic, validations, or third-party API calls. These multi-stage workflows are common across industries and use cases, including:

• Document analysis pipelines such as optical character recognition (OCR) to classification to summarization to indexing

• Fraud detection systems such as rule-based checks to machine learning (ML) scoring to escalation logic

• Healthcare automation such as imaging to diagnosis to report generation to physician review

• Language processing flows such as transcription to sentiment analysis to response generation

However, these pipelines can be problematic because they often involve the following:

• Heterogeneous services such as OCR, natural language processing (NLP), vector search, and custom ML

• Multiple model types such as traditional ML and generative AI

• Strict audit and error-handling requirements

• Cross-functional ownership such as data science, engineering, and compliance

Traditionally, these workflows are implemented as brittle glue code or static orchestration platforms. This approach leads to poor observability, tight coupling and low agility, and high operational overhead for updates and error recovery.

The multi-stage AI workflow pattern: modular, observable, serverless AI pipelines

The multi-stage AI workflow pattern uses AWS Step Functions as the orchestration backbone. With this pattern, teams can coordinate a sequence of AI tasks as modular, serverless functions, each triggered and managed independently. Each stage of the workflow is observable, supports retries, and is fully decoupled from the other stages. The multi-stage AI workflow pattern enables the following:

• Fine-grained control and error handling

• Plug-and-play model integration such as changing an Amazon Bedrock model without touching orchestration

• Clear separation of concerns between tasks such as enrichment and inference

• Repeatability, traceability, and compliance alignment

The reference architecture implements each layer as follows:

• Event trigger - Initiates a Step Functions state machine through Amazon S3 upload (for example, a PDF file), API call, or scheduled job.

• Processing - Uses AWS Lambda to prepare metadata, classify file type, and enrich input (for example, detect document language).

• Inference – Occurs in multiple stages such as Amazon Textract to Amazon SageMaker classifier to Amazon Bedrock large language model (LLM) summarizer, all chained by using Step Functions.

• Post-processing - Uses Lambda to determine routing such as send to reviewer, escalate to legal, or auto-approve.

• Output - Saves results to Amazon S3 or indexes in Amazon OpenSearch Service. Emits audit events to Amazon EventBridge for logging and alerts.

Use case: Legal document ingestion and summarization

A legal services firm receives hundreds of contracts daily in different formats. They need to extract and classify document types and identify risk clauses. Additionally, they must summarize and index the documents for retrieval and route them to lawyers based on risk score and document type.

In response to this use case, the multi-stage AI workflow solution follows these steps:

1. A PDF upload triggers Amazon S3 to EventBridge to Step Functions.

2. Amazon Textract extracts raw text from the PDF.

3. The SageMaker model classifies the document type, for example, a nondisclosure agreement (NDA) or a master service agreement (MSA).

4. Amazon Bedrock generates a natural language summary and risk explanation.

5. Lambda determines the next action such as flag for review or auto-process.

6. Outputs are logged to Amazon S3. Alerts are emitted by using Amazon Simple Notification Service (Amazon SNS) or EventBridge.

Why Step Functions is ideal for multi-stage AI workflows

Step Functions provides the following features and benefits:

• Visual workflow builder – Enables easy mapping and iteration of business logic

• Built-in retries and timeouts – Handles downstream model failures gracefully

• Parallel execution – Runs multiple in inference models concurrently (for example, multilingual translation)

• Dynamic branching – Routes based on intermediate inference results

• Auditability – Enables fine-grained monitoring and compliance through logs and metrics for each step

Security and governance best practices

To ensure secure, auditable, and policy-aligned AI pipelines, organizations should follow these security and governance best practices:

• Use AWS Identity and Access Management (IAM) per step to enforce the principle of least privilege across all services and Lambda functions.

• Log each input and output to Amazon CloudWatch Logs or Amazon S3 to enable traceability, debugging, and audit.

• Integrate AWS CloudTrail to capture API-level access and invocation history for compliance and forensic analysis.

• Apply schema validation between stages to ensure data integrity, prevent injection or prompt drift, and reduce failure propagation.

Business value of the multi-stage AI workflow pattern

The multi-stage AI workflow pattern delivers value in the following areas:

• Agility – Updates or reorders steps without disrupting the pipeline.

• Scalability – Scales automatically with document volume through serverless architecture.

• Compliance – Provides step-by-step traceability of actions and AI decisions.

• Maintainability – Provides a modular and team-aligned code base. (Separating AI logic from policy logic improves maintainability by allowing dynamic model behavior and deterministic business rules to be managed independently. This approach reduces risk and enables clearer team ownership.)

• Integration – Enables combinations of traditional ML, LLMs, and external APIs without coupling.

The multi-stage AI workflow pattern gives organizations a structured, scalable way to assemble complex AI pipelines, grounded in serverless principles and operational best practices.

This pattern provides the backbone for building enterprise-grade, AI-enhanced workflows that are secure, observable, and easy to evolve over time. It supports various use cases, from ingesting documents and automating onboarding to analyzing risk and composing contextual outputs from multiple models.