Is Graph Engineering just reinventing systems architecture for the AI age?

In a recent discussion with Annie Wang, the concept of 'graph engineering' is explored as a method to bring backend predictability to multi-agent AI systems. Moving away from unpredictable black-box loops, graph engineering applies traditional software architecture principles—such as microservices, strict schemas, and fan-out/join pipelines—to agentic workflows. By utilizing tools like Google's Agent Development Kit (ADK), developers can orchestrate shared state and conditional routing, effectively treating AI agents with the same rigor as standard backend services. The article argues that this shift allows for greater debuggability and control in complex AI applications. The piece encourages developers to move beyond simple agent loops and adopt structured graph patterns to improve the reliability of their AI-driven systems. Readers are also invited to explore the practical implementation of these concepts through the provided ADK Codelab.