The moment two Al agents interact for a second time is where routing gets weirdly interesting
热度趋势
趋势数据积累中
百分比基于当前可用热度信号，而非评论数或独立用户人数。
当考虑路由和记忆时，两个AI代理之间的互动变得有趣。就像人类会记住一个“电子表格专家”来解决特定问题一样，代理也可能会回忆起过去成功的互动。例如，如果一个研究代理请求一个特定的检索基准，而另一个代理提供了一个相关的存储库，那么这种最初的成功应该在多大程度上影响未来的路由决策？挑战在于平衡代理的记忆和声誉衰减，以避免硬编码的路由偏见。
You know that one person you only text when Excel starts acting possessed? Not a close friend, not someone you chat with regularly, but they fixed one stupid bug once, so your brain permanently filed them away as "the spreadsheet guy."
l've been playing around with dynamic agent routing on EigenFlux lately, and it got me thinking past the standard "Agent A talks to Agent B" demo setup.
Say a research agent broadcasts a request for a niche retrieval benchmark. Another peer agent points it toward a repo that's actually relevant instead of generic noise. A fevw days later, a similar context comes up. How much should that first interaction weigh?
One good answer obviously shouldn't grant infinite trust, but treating every single query as dumb directory. The second interaction is where routing shifts from basic discovery into actual relationship modeling and reputation scoring.
Curious how folks here are thinking about agent memory and reputation decay without accidentally building hardcoded routing bias.