dag-orchestration

You orchestrate multi-step workflows with dependencies by defining nodes in a YAML file and executing it through topological scheduling to coordinate tasks across agents with reliable outcomes.

Topological scheduling for directed acyclic graph workflows is a mechanism that sequences nodes based on their dependencies, ensuring controlled parallelism and blocking downstream tasks until upstream ones complete.

You handle task failures during complex workflow automation by applying configurable failure strategies like stop, skip, or retry, which provide clear provenance and support recovery for reliable execution.

Yes, you can track the state of running, completed, and blocked nodes during DAG execution because the orchestrator maintains state and provides visibility throughout the inline workflow process.

The best way to define multi-step tasks for reproducible automation is creating a YAML workflow definition that specifies nodes and dependencies, allowing the orchestrator to execute the directed acyclic graph consistently.

You need a DAG-based orchestration approach when your complex projects involve multi-step tasks with strict dependencies across agents, requiring parallel execution, state tracking, and specific failure strategies.