AWS Step Functions
What are AWS Step Functions?
AWS Step Functions is an Amazon Web Services tool used to orchestrate and coordinate Lambda functions (small pieces of code) in a workflow. It provides built-in support for error handling and automatic retries, allowing developers to manage sequences of tasks and determine the best retry path based on errors. Step Functions are particularly useful for building automated workflows that connect multiple API calls or code executions together in a reliable, coordinated way.
Why use AWS Step Functions for AI workflows?
Step Functions shine when building AI products because they handle the orchestration complexity that comes with multi-step AI workflows. An AI product might need to retrieve data from a database, make multiple LLM calls, format responses, store results, and handle various error conditions along the way. Step Functions coordinate these steps, automatically retry operations when transient errors occur, and provide clear visibility into where workflows succeed or fail.
The built-in error handling becomes especially valuable with AI workflows. When an LLM API returns a rate limit error, Step Functions can automatically retry with backoff. When data validation fails, the workflow can branch to handle the error appropriately. This infrastructure support lets teams focus on building their AI features rather than writing custom orchestration code.
What makes Step Functions different from other workflow tools?
Step Functions are designed specifically for the AWS ecosystem, integrating natively with Lambda functions and other AWS services. They generate clear error messages and provide detailed execution logs, making debugging easier than many alternatives. However, this tight AWS integration means code written for Step Functions stays within the AWS environment—unlike more portable tools like Zapier or Make. Teams choose Step Functions when they need production-grade reliability, detailed error handling, and deep AWS integration for their automated workflows.
Learn more:
- Product in Practice: Getting Started with Several APIs (And the Challenges I Faced)
- Behind the Scenes: Building the Product Talk Interview Coach
- 21 Ways to Use AI at Work (And Build Your AI Product Toolbox)
Related terms: