Graph Engineering for AI Agents: From Prompts and Loops to Workflows

Single-agent AI loops, where one model controls an entire task, are often flawed because the model judges its own work without any external checks. Graph engineering provides a more robust solution by shifting control from the AI model to a predefined, structured workflow. This approach breaks down complex tasks into smaller units called nodes, which can be AI calls, human approvals, or simple calculations. The graph's edges then dictate the flow between these nodes, while a shared state carries information through the process, ensuring a more reliable and auditable outcome.