@freeman1266: Spec + ReAct + Ralph: These three layers are the true engineering foundation for making long-running Agent tasks work. Many people think just getting the Agent to "run" is enough. Actually, that's just the beginning. The model stopping doesn't mean the task is done. For long-running Agent tasks, you need a three-layer structure: The…

Spec + ReAct + Ralph: These three layers are the true engineering foundation for making long-running Agent tasks actually work. Many people think that just getting the Agent to "run" is enough. Actually, that's just the beginning. The model stopping doesn't mean the task is complete. For long-running Agent tasks, you need a three-layer structure: Layer 1: Spec — Break down the task clearly Without a clear task definition, the Agent will just do whatever until it stops. The core of Spec is to write down the goal, validation conditions, and constraint boundaries clearly — not into the prompt, but attach to the Session, so that the entire task has a persistent completion condition. Layer 2: ReAct — Bring the model into the execution scene The ReAct loop is: Think → Act → Observe → Re-think. The Agent is not "answering questions", but "doing work". PlanAct handles planning, CodeAct handles code actions, MultiAgent handles collaboration — but they all run on this loop at the bottom. Layer 3: Ralph — Wrap an outer layer of LoopControl Ralph is an external controller responsible for judging whether the task is truly complete. The model thinking it's done doesn't mean the task is complete. Ralph continuously checks: continue, pause, complete, or stop and wait for human confirmation. All three layers are indispensable: · Spec solves task definition · ReAct solves execution loop · Ralph solves completion judgment Agent engineering is not about "letting AI take more rounds". It's about structuring task, execution, and control so that each run is more reliable than the last.