This page explains how the agent uses different model layers to observe the market, reason over signals, and turn decisions into actions. The goal is speed, structure, and clear execution.
Telegram commands, wallet activity, and market data feed the agent in real time.
Models rank signals, evaluate scenarios, and decide whether conditions justify a move.
The agent turns approved decisions into actions, monitoring the position after entry.
Each model role handles a different part of the loop so the system stays fast without losing context.
The first layer reads market structure, wallets, volume shifts, and social velocity to detect unusual movement early.
The reasoning layer compares opportunity, risk, and timing so the agent does not react blindly to noise.
The final layer formats the response into a trade plan, a Telegram update, or an execution-ready instruction.
The models do not work in isolation. They pass context through a fixed sequence so the final action stays coherent.
ValOs collects token activity, wallet behavior, narrative movement, and user instructions from Telegram.
The model weighs momentum strength, crowding risk, and market timing before deciding whether to continue.
If the setup is valid, the agent produces an execution path, risk framing, and a user-facing update.