Sure, instruction tuned models implicitly plan, but they can easily lose the plot on long contexts. If you're going to have an agent running continuously and accumulating memory (parsing results from tool use, web fetches, previous history, etc.), then plan decomposition, persistence and error recovery seems like a good idea, so you can start subagents with fresh contexts for task items and they stay on task or can recover without starting everything over again. Also seems better for cost since input and output contexts are more bounded.