I just closed a small stBTC borrow earlier than planned. Rates started creeping up, and it felt smarter to step aside early rather than push it.
The Kite agent I’d delegated finished its run not long after and flagged a mild inversion forming on the enzoBTC curveover the next two weeks.
My coffee had already gone cold, but the chart held my attention.
How I’m Using Kite Agents Right Now
First rule: delegate to prediction-focused agents with solid attribution histories.
These agents run Monte Carlo-style simulations using live data, and when you bind them directly to the pools you’re watching, they often surface signals faster than dashboards update.
Second rule: don’t go overboard on depth.
Letting simulations branch endlessly burns gas without really improving accuracy. There’s a sweet spot.
Think of It Like Rivers
I picture the simulations like branching rivers.
The main stream is the current state: utilization, liquidity depth, oracle feeds.
From there, agents branch off into scenarios—liquidity stress, parameter changes, sudden inflows. Each path is weighted based on how accurate that agent has been in the past. Over time, those paths narrow back toward the most likely outcome.
Governance acts like riverbanks. Proposals decide how many forks agents can explore, how attribution is rewarded, and which paths get pruned.
On
