Aws Step Functions

AWS Step Functions

Overview

AWS Step Functions uses Amazon States Language (ASL) to define state machines as JSON. With AWS Step Functions, you can create workflows, also called state machines, to build distributed applications, automate processes, orchestrate microservices, and create data and machine learning pipelines.

This skill provides comprehensive guidance for writing state machines in ASL, covering:

• ASL structure and JSONata expression syntax
• Details on the eight available workflow states
• The $states reserved variable
• Workflow variables with Assign
• Error handling
• AWS Service integration patterns
• Example code for data transformation and architecture
• Validation and testing of state machines
• How to migrate from JSONPath to JSONata

The AWS MCP server is recommended for sandboxed execution and audit logging when following this skill, but all steps use AWS CLI syntax and work without it.

When to Load Reference Files

Load the appropriate reference file based on what the user is working on:

• ASL structure, state types, Task, Pass, Choice, Wait, Succeed, Fail, Parallel, Map → see references/asl-state-types.md
• Error handling, troubleshooting, Retry, Catch, fallback, error codes, States.Timeout, States.ALL → see references/error-handling.md
• Service integrations, Lambda invoke, DynamoDB, SNS, SQS, SDK integrations, Resource ARN, sync, async → see references/service-integrations.md
• Migrating from JSONPath to JSONata, migration, JSONPath to JSONata, InputPath, Parameters, ResultSelector, ResultPath, OutputPath, intrinsic functions, Iterator, payload template → see references/migrating-from-jsonpath-to-jsonata.md
• Validation, linting, testing, TestState, test state, mock, mocking, unit test, inspection level, DEBUG, TRACE, validate state, test in isolation → see references/validation-and-testing.md
• Architecture patterns, examples, polling, saga, compensation, scatter-gather, semaphore, lock, human-in-the-loop, escalation, Express to Standard → see references/architecture-patterns.md
• Data transformation, JSONata expressions, filtering, aggregation, string operations, $reduce, $lookup, $toMillis, $partition, $parse, $hash, $uuid → see references/transforming-data.md
• State input/output, $states, Assign, Output, Arguments, variable scope, variable limits, evaluation order, passing data between states → see references/processing-state-inputs-and-outputs.md

Quick Reference

Standard vs Express Workflows

Choose Standard for: payment processing, order fulfillment, compliance workflows, anything that must never execute twice.

Choose Express for: IoT data ingestion, streaming transformations, mobile backends, high-throughput short-lived processing.

When recommending Express, the single limitation you must always state — even for fire-and-forget / high-throughput pipelines — is that Express does NOT support `.sync` or `.waitForTaskToken` (no callbacks, no nested .sync waits, no human-approval or job-completion waits). Also note: 5-minute max duration, no queryable execution history (CloudWatch Logs only), and at-least-once (async) / at-most-once (sync) execution — so non-idempotent work can run twice. If any of these matter, choose Standard (exactly-once, up to 1 year, full history).

Setting the State Machine Query Language

JSONata is the preferred way to reference and transform data in ASL. It replaces the five JSONPath I/O fields (InputPath, Parameters, ResultSelector, ResultPath, OutputPath) with just two: Arguments (inputs) and Output.

Enable at the top level to apply to all states:

{ "QueryLanguage": "JSONata", "StartAt": "...", "States": {...} }

Or per-state to migrate from JSONPath incrementally:

{ "Type": "Task", "QueryLanguage": "JSONata", ... }

JSONPath is supported and is the default if QueryLanguage is omitted — existing state machines do not need to be migrated.

Field mapping (JSONPath → JSONata):

A state uses one query language, not both. Never mix JSONPath fields (InputPath/Parameters/ResultSelector/ResultPath/OutputPath) with JSONata fields (Arguments/Output) in the same state — this is the most common migration error. See references/migrating-from-jsonpath-to-jsonata.md for full details.

How Assign and Output Are Evaluated (Parallel, Not Sequential)

Within a single state, Assign and Output are evaluated at the same time — in parallel — both reading the same data (the state input plus the task result). They are NOT evaluated one after the other. Because they run together, a variable you set in Assign is not visible in that same state's Output: there is no ordering in which Output could observe the just-assigned value. The assigned value becomes available only to subsequent states.

So if you set a variable in Assign and reference it in the same state's Output, you get the old/undefined value — not because Output runs "before" Assign, but because both evaluate concurrently from the same snapshot. To use the value immediately, reference it in the next state (variables persist across states); to shape the current state's output from the task result, use $states.result directly in Output.

Unit Testing a State with TestState

Test a single state without deploying the state machine or calling the real service using the TestState API (aws stepfunctions test-state) with --mock. A complete answer covers all four points:

• Mock the service response exactly — the --mock result MUST match the target AWS service's API response schema exactly (field names are case-sensitive). For a Lambda invoke Task that is StatusCode and Payload: --mock '{"result":"{\"StatusCode\":200,\"Payload\":{...}}"}'.
• All three inspection levels (--inspection-level): INFO (default — output, status, nextState), DEBUG (adds data flow: afterArguments, result, variables — use to debug JSONata/data flow), TRACE (adds raw HTTP request/response, for HTTP Task).