workflow-engine

You can orchestrate infrastructure deployment workflows by defining a machine-readable Directed Acyclic Graph (DAG) in JSON format, which routes steps sequentially and validates execution state for tasks like Azure deployment.

A machine-readable DAG for process automation is a JSON structure that defines workflow steps, dependencies, and conditions to enable step-by-step execution, routing, and validation for complex processes.

To resume an interrupted workflow state, you load the session state JSON file to understand the current execution position within the DAG, allowing the orchestrator to continue from the exact step where it left off.

Yes, this workflow orchestration approach applies to application development by defining a JSON workflow graph that manages step routing, conditions, and validation across the development lifecycle.

Yes, managing workflow states requires parsing JSON to read the workflow graph and session state files, ensuring the orchestrator correctly interprets step dependencies and execution conditions.

A limitation of using a JSON DAG for workflow orchestration is that it requires strict acyclic dependency mapping, meaning you cannot define circular dependencies or loops within your workflow steps.