AI Agent Graphs: Loops, Workflows & Production Setups

Here's the thing: AI agent graphs don't replace loops; they give loops a place to work. This distinction changes how we approach practical problems. You're deciding if a task is a single coherent job for one agent, or several independent jobs needing different stages. What's interesting is that a graph makes queues, hand-offs, waiting states, and human decisions visible. It also highlights awkward engineering problems. A loop is what one worker does, like inspecting code, making a change, running a test, and retrying if it fails. A graph, on the other hand, is the wider map around that worker. It defines states work can be in and the routes between them. A useful production setup typically has both: a loop inside an agent node for reasoning and retries, and a graph around the nodes for routing, governing, and observing work. The bottom line is that graphs arrange loops, providing a structured system for AI agents to operate within.