workflow-engine

To design multi-step AI workflow orchestration, you define a declarative JSON-based DAG schema representing the process. The schema is then validated to ensure dependency management and branching logic execute correctly across all stages.

Yes, you can automate deployment pipelines with human gates, conditional branching, and parallel execution. The workflow engine manages routing based on session state and approvals to ensure proper execution order.

A JSON-based DAG schema is a declarative graph structure representing multi-step processes with directed dependencies. You need it when orchestrating complex multi-agent processes like data pipelines or approval workflows requiring validation and branching.

You determine the next step by loading the workflow graph and analyzing the current session data. The engine evaluates conditional logic and session state to route the process to the appropriate subsequent stage.

Yes, the workflow engine supports error handling and resumption capability for data pipelines. It manages dependency graphs to track execution state, allowing interrupted multi-stage processes to resume correctly.

Using a declarative graph structure requires providing a valid JSON-based DAG schema and supporting documentation for full operation. Complex branching logic and dependency management must be explicitly defined within this structural constraint.