Directed acyclic graph (DAG)
A structure that models steps as nodes with directed dependency edges and no cycles, used to express which workflow steps depend on which others.
In more detail
A directed acyclic graph represents a workflow as nodes (steps) connected by directed edges (dependencies), with no cycles, meaning you can never loop back to an earlier step. The structure captures exactly which steps depend on which, so the engine knows what must run before what and what can run at the same time.
The practical benefit is correct ordering and parallelism. Steps with no dependency between them can run concurrently, while dependent steps wait for their inputs, which makes a workflow both faster and correct. The DAG is the common backbone of data pipelines and complex orchestration for this reason.
Where this shows up at Ceven
The dependency structure a DAG expresses, run independent steps in parallel, order dependent steps correctly, is the kind of orchestration Ceven handles under the hood. You describe the outcome, and Ceven works out which steps can run together and which must wait, so a multi-step workflow completes efficiently and in the right order.