AI 代理工具发现与执行效率问题
问题概述
AI 代理在使用大量工具时常陷入上下文污染，缺乏高效的工具检索和组织机制，导致 token 浪费和执行延迟。
痛点标签
- 上下文污染：大量不必要的工具填充上下文
- 缺乏层次化工具检索，如基于 splay tree 的热用工具优先排序
- 低效的同步工具调用导致 token 浪费和执行瓶颈
待验证问题
- 你是否愿意为此类工具发现和执行优化支付订阅费用？
- 目前的开发流程中，你是否经常遇到上下文污染或 token 浪费的问题？
- 你更倾向于使用层次化检索还是异步/程序化工具调用？
证据
原话 · @anonymous
I think this space is very untapped. Models are interesting, but I am absolutely obsessed with some things I've been researching\\/working on for the past few years:Fractal tool discovery: tool taxonomy where an agent can "drill deeper" to find what specific tool it's looking for. Helps if\\/when polluting context with a zillion (mostly unnecessary) tools.
原话 · @anonymous
Leveraging splay trees: this is my favorite data structure and I think relatively unused in the context of agents\/harnesses. A lot of times, recently-used workflows\/tool-chains will be used again, so having those at the top of the search hierarchy is an awesome optimization.
原话 · @anonymous
Virtual containerized notebooks: models working in sandboxed (WASI) Python notebooks is incredible. Even local models (if given enough time) will usual
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