A new paper argues that individual AI agents hit a ceiling on tasks requiring parallel execution, specialized expertise, and persistent state management, and proposes Graph Engineering as a paradigm to move past it. The approach structures task decomposition and workflows as explicit graphs, organizes heterogeneous agents into coordinated teams via typed relationship graphs, and maintains auditable runtime state for fault detection and recovery across distributed execution. The authors frame this as a shift from optimizing individual agent behavior toward system-level “System Intelligence” for agent networks.
