
The evolution of AI development has moved rapidly from simple single-prompt interactions to complex “chains.” While standard LangChain is excellent for Directed Acyclic Graphs, where data flows in one direction, it struggles with tasks that require recursion. In our experience building production agents, I found that a real-world agent often needs to execute a task and evaluate the result. Consequently, if the outcome fails, it must loop back to a previous step to self-correct. LangGraph AI workflows solve this by treating cycles as first-class citizens. By modeling processes as state machines, developers create cyclic graphs where an agent iterates on
