You hand money over to an AI agent—what you fear most is that it will waste it. But once everything can be programmed into rules, another problem emerges: who can change those rules? Public strategies don’t mean safety; centralized update permissions are the real backdoor.

After reading the Newton Protocol website, today Awais, who is ranked No. 2, noticed a clear temperature gap. The homepage talks about a digital workforce—like an AI agent doing on-chain chores for you. But the feature list underneath is more like something written for compliance teams and institutional vaults: investor eligibility, sanctions screening, velocity limits, and jurisdictional rules.

This mismatch isn’t a bad thing—it actually shows the product first landed in enterprise scenarios. But it also moves the risk around: users used to worry about whether AI has permissions; now they also have to see whether the policy provider is overly concentrated—who can change spending caps, and who can adjust approved payees.

Newton Protocol’s mechanism is to go through policy before the transaction. Rego checks the rules; if they pass, it generates an attestation to allow it. Newton Mainnet Beta puts these actions into the real network. But if the rule source and update process aren’t transparent, the proof can only show that it “passes the current rules,” not who changed that rule.

So look at $NEWT—don’t just read the stories about autonomous agents. #Newt You should instead check the policy provider address, the update process, the audit trail, the rule versions, and whether each attestation can be traced back to the specific modification record. @NewtonProtocol