DAG Workflow

Advantage over a linear pipeline

A linear pipeline can only do A→B→C. A DAG allows: one step's output fanning out to several downstream steps at once (parallel), several steps' results merging into one downstream step (join), and independent branches proceeding separately. "Acyclic" guarantees no circular dependency, so the graph can be topologically sorted, batched for parallelism, and checked for deadlock.

Place in agents

Data orchestrators like Airflow and Prefect have long used DAGs. Recent agent-orchestration frameworks (graph-modelled ones) use a similar structure for "which agent/step depends on which." Compared with a model improvising the order, a DAG makes the whole process visualisable, testable, reproducible — at the cost of flexibility, since changing the structure mid-run is awkward.

Common misconceptions

• "Acyclic" is a hard constraint: retries/loops must be expressed by another mechanism (e.g. an outer controller), not by drawing a cycle in the graph.
• A DAG describes dependencies; it does not automatically mean parallel execution — a scheduler must support it.

Related terms

Sources

1. Directed acyclic graph — overview
2. Apache Airflow — DAGs concept