In agentic systems, a Dependency Graph is a directed acyclic graph (DAG) used to model the relationships between subtasks in a complex workflow. Each node in the graph represents a task or an agent responsible for a task, and a directed edge from node A to node B signifies that task B has a true dependency on the output of task A. This structure makes the order of operations explicit and is fundamental to effective task orchestration.
The primary purpose of a dependency graph is to enable efficient and reliable execution by revealing the workflow’s underlying structure. By analyzing the graph, an orchestrator can identify which tasks must be performed sequentially and, more importantly, which tasks are independent and can be executed in parallel. This allows for significant reductions in overall latency compared to a purely sequential approach.
Building an accurate dependency graph is a critical part of the planning phase. It requires distinguishing between true, structural dependencies and artificial ones that arise from habit or convention. An incorrect or incomplete graph can lead to race conditions, deadlocks (if it contains cycles), or failures when an agent attempts to execute a task without its necessary inputs. Modern agentic frameworks like LangGraph use this concept to manage state and coordinate complex, multi-agent collaborations effectively.