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Workflows are operated as persistent execution graphs featuring resumability, retries, and state history. Typical conditions for use: Runs take a long time or can fail; States and retries must be robustly managed. The central trade-off: Production robustness is achieved at the expense of infrastructure and modeling effort.
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Theoretical construct: explains a term, principle, or mental model.
What organizes, connects, or makes decisions possible.
A workflow DAG arranges work steps as a directed acyclic graph with explicit dependencies.
DAGs come from graph theory and project planning; data and build systems turned them into a robust way to orchestrate dependent processing steps without cycles. Workflow systems apply the same model to repeatable agent and data tasks.
Imagine a timetable where each task starts only after its prerequisites are complete, and no subject points back to itself.
A single work step in the workflow.
The condition that one step must finish before another can start.
Running independent steps at the same time.
A DAG exposes order, dependencies, and possible parallelism. It fits poorly when tasks require unpredictable backtracking or intentionally cyclic learning loops.
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