Skip to main content
The AI agent loop is the part of Perps Agent that picks the grid parameters and gets better at it over time — in a way that is provable, not just claimed.

​
The loop

1

Observe

Pull recent market state (price, volatility, funding, microstructure) plus the user’s risk caps.
2

Recall

Read past attested runs on similar regimes from Mantle. Only attested runs count — backtests do not.
3

Propose

Generate candidate grid configs (range, step, count, sizing) that maximize a risk-adjusted score under the user’s caps.
4

Commit

Hash the chosen config and write it to the Mantle commit registry. The agent is now locked to that config.
5

Trade

Hand control to the grid engine on Bybit.
6

Attest

At the end of the run, write the verified outcome on-chain, linked to the original commit.
7

Learn

The next iteration’s “recall” step reads this new attestation. The loop closes.

​
Why this matters

Most “learning” trading bots train on private data the user can’t see. Perps Agent’s training signal is the public on-chain attestation history. The same data anyone else can verify.

​
What it optimizes

Not raw PnL. The objective is risk-adjusted: returns penalized by realized variance and drawdown, hard-clipped at the user’s risk caps. A run that hits the drawdown cap is treated as a loss for learning purposes, even if it would have recovered.