When we ask a large language model (LLM) to solve a hard problem, one well-crafted prompt no longer carries the job. In practice the work is a sequence of actions: search, read, write code, check, fix. The agent has to plan its steps, use tools and remember what it did before. Yet most agents today are either hardwired into rigid scripts that adapt badly to new conditions, or they demand…