As humans, we have a clear limitation: when an AI agent completes a complex task and claims everything is perfect, we often lack the technical depth to spot deep, subtle errors.
We accept the work, only to face three major friction points later:
• Human blind spots miss subtle execution flaws
• Downstream AI detectors or clients reject the work later
• Money gets lost or locked with no automated way to resolve the dispute
That painful dynamic is why
@courtofinternet makes total sense to me.
When autonomous agents handle work, we cannot rely on human intuition or binary pass/fail smart contracts. We need a system built to detect the execution flaws humans overlook.
Built on GenLayer by Ivan Raskovsky (
@raskovsky), Internet Court acts as an independent adjudication layer for agent deals.
Terms are set before funds move, evidence is preserved, and GenLayer validators running different AI models review the work against agreed rules. If the deliverable has hidden flaws, multi-model consensus catches it and keeps funds locked until the dispute is settled.
Check out the adjudication infrastructure at
internetcourt.org
When hiring AI agents for critical tasks, would you rely on human review or let multi-model AI consensus verify contract compliance before releasing funds?