workflow-engine

You orchestrate multi-step workflows by defining a declarative Directed Acyclic Graph (DAG) in YAML. The orchestrator parses this machine-readable definition to manage task sequences, dependencies, conditional branching, and parallel execution without hardcoded logic.

A machine-readable DAG is a Directed Acyclic Graph that defines structured task sequences for an orchestrator to parse. You need it for administrative tasks requiring parallel execution, conditional branching, validation checks, and clear dependency management across complex project stages.

Yes, you define conditional branching and parallel execution declaratively within the YAML workflow structure. The DAG manages these execution paths automatically, ensuring complex project stages with dependencies progress smoothly through validation checks.

Yes, understanding YAML is required to define workflow steps and dependencies. You must write declarative configurations that specify the task sequences, conditions, and parallel execution paths the orchestrator will parse and execute.

The best way to manage task sequences without hardcoding logic is using a declarative workflow definition. By defining dependencies and conditions in a machine-readable DAG, the orchestrator executes structured task sequences dynamically.