AI Agent的下一站：Graph Engineering｜一文读懂

TL;DR - A multi-institution research survey proposes Graph Engineering as a foundation for next-generation LLM agent systems, explicitly representing tasks, agent coordination, and runtime state as evolving graphs. This could help complex multi-agent systems become more scalable, auditable, and recoverable.

• Task graphs encode goal decomposition, dependencies, parallel branches, executable workflows, and validation steps.
• Agent graphs model capabilities, permissions, delegation, communication, resource access, and human oversight.
• Runtime-state graphs track versioned changes and dependencies to support diagnosis, replay, rollback, and recovery from failures.
• Open challenges include unified capability graphs, validated self-evolution, graph-native agent operating systems, and privacy-preserving governance.