Agent Series (3): Plan-and-Solve (opens in new tab)

Where Does ReAct Hit a Wall? The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the next action. This works well most of the time, but there's one class of task where it stumbles. Imagine you ask an Agent to do this: Search for the release years of Python, Java, and Go. Sort them chronologically. Calculate how many years apart Python and Go are. A typical ReAct execution might look like: Action: web_search("Python release year") Ac...