Recently, I have read many discussions about on-chain automated execution and suddenly realized an interesting paradox: the smarter the system becomes, the more easily it can be misled by faint signals.

It's not that the system itself has problems, but that the environment has changed. Now, the data flow density on the chain is too high—transaction frequency, event triggers, input signals come one after another, and seemingly insignificant fluctuations can become triggers for execution.

The question is: how does the system determine which signals to prioritize and which are just background noise?

AI models tend to amplify small fluctuations as trends; liquidation engines interpret random behaviors as risks; governance systems mistake scattered opinions for genuine intentions; monitoring tools treat occasional jumps as attack signals; cross-chain modules view short-term latency differences as desynchronization. None of these are bugs; they are inevitable as the number of intelligent agents explodes and the on-chain ecosystem enters a "hyper-sensitive era."

In this context, setting signal priorities becomes crucial. The system needs to understand:

What requires immediate response, and what can wait; what constitutes structural change versus random fluctuation; what reflects genuine trends versus short-term jitters; what needs more context, and what should not trigger any action at all.

This is the core problem that an automated system must truly solve—not making the system more sensitive, but enabling it to filter effectively.