🧭 Reaching for LangGraph by default? This paper is a practitioner guide that separates where graph orchestration truly helps from where it just gets in the way. The authors argue LangGraph adds value not through model quality but through durable orchestration and governance. So it earns its place when a workflow must pause and resume, or when the next step depends on explicit state like risk, evidence quality, or retry count. Conversely, if a single prompt plus one tool call suffices, keep it simple with a plain SDK, they warn against overuse. The thesis is made concrete with three runnable recipes: SQL analytics that self-repairs from validation and execution errors; a fail-closed RAG that returns "insufficient" instead of fabricating when evidence is weak; and a HITL flow that interrupts only high-risk cases for human review. Each uses typed state, conditional edges, and checkpoints to lift repair and approval out of implicit prompts into testable, explicit steps (with a warning that removing the checkpointer breaks pause/resume, so high-risk cases never complete). Tool selection is crisp too: ReAct for simple cases, schema-first for structured extraction, DSPy for prompt optimization, LangGraph when branching and audit matter. By deliberately skipping benchmarks to discuss when engineering shape changes, Graph-Based Agentic AI with LangGraph is a solid map for design decisions. 🔗 #LangGraph# #AIAgents#