In plain words
In agent systems, graph engineering is an emerging label for deliberately structuring work as a graph. Nodes represent tasks, agents, or operations; edges represent dependencies, information flow, or possible transitions.
A closer look
A graph can express a sequence, parallel branches, a handoff to a reviewer, or a loop that returns work for revision. The engineering includes deciding what state travels between steps, which conditions permit progress, where approval is needed, and how interrupted work resumes. A graph can be fixed in advance or change as a task develops.
The terminology is not standardized. Some people use “graph engineering” more broadly for knowledge graphs, where nodes represent entities and edges represent relationships. An execution graph describes how work proceeds; a knowledge graph describes what is connected in a body of information. A system can use both.
In practice
A report workflow splits into two research branches, combines their findings, sends the draft to a checker, and returns it for revision if a claim lacks evidence. The graph makes those dependencies and return paths explicit.
A useful distinction
Drawing boxes and arrows does not make a system reliable. Each transition still needs clear conditions, valid state, meaningful verification, and a stopping rule. Graph engineering is a design approach, not a single tool or protocol.