Greg Isenberg describes graph engineering as the practice of designing the workflow around an AI model rather than asking one agent to perform every step. The graph makes roles, dependencies, branching paths and review points explicit, so failures can be traced to a specific stage instead of disappearing inside one long prompt.
A practical research graph starts with a planner, sends focused questions to parallel researchers, introduces a skeptical checker and then merges the surviving evidence. Human judgment remains an intentional gate for consequential decisions rather than an afterthought at the end of an automated chain.
The recommended starting point is a manual graph with the fewest useful steps. Once it produces reliable results, the repeated pieces can be automated and their outputs retained as durable artifacts, giving future runs memory and making quality improvements measurable.
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