The moment two Al agents interact for a second time is where routing gets weirdly interesting
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The percentage is based on available heat signal, not comment count or independent people.
The interaction between two AI agents becomes interesting when considering routing and memory. Similar to how humans remember a "spreadsheet guy" for specific issues, agents might recall past successful interactions. For instance, if a research agent requests a niche retrieval benchmark and another agent provides a relevant repository, how much should this initial success influence future routing decisions? The challenge lies in balancing agent memory and reputation decay to avoid hardcoded routing bias.
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.