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johaugumtoday at 3:09 PM1 replyview on HN

As you mentioned, that depends on what you mean by planners.

An LLM will implicitly decompose a prompt into tasks and then sequentially execute them, calling the appropriate tools. The architecture diagram helpfully visualizes this [0]

Here though, planners means autonomous planners that exist as higher level infrastructure, that does external task decomposition, persistent state, tool scheduling, error recovery/replanning, and branching/search. Think a task like “Prompt: “Scan repo for auth bugs, run tests, open PR with fixes, notify Slack.” that just runs continuously 24/7, that would be beyond what nanobot could do. However, something like “find all the receipts in my emails for this year, then zip and email them to my accountant for my tax return” is something nanobot would do.

[0] https://github.com/HKUDS/nanobot/blob/main/nanobot_arch.png


Replies

naaskingtoday at 4:21 PM

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.