Graph engineering is the coordination layer that connects multiple agent loops, using nodes (units of work), edges (routing decisions), and shared state. The post explains when graphs are appropriate versus overkill, four hard problems in graph design (node justification, shared state hygiene, trustworthy routing, and agent agreement), and best practices drawn from LangGraph, Google ADK 2.0, and Cognition's Devin. Key advice: separate agent memory from state, use deterministic code for routing where possible, scope memory per agent, checkpoint state between nodes, and only reach for a graph when tasks genuinely require parallel fan-out, specialization, or failure isolation. The newsletter also briefly covers agent memory vs. state distinctions using CrewAI, and links to a deep dive on diffusion LLMs.
