Human-in-the-Loop Task API for AI Agents
An AI agent can plan and reason but still hit a boundary when the job requires a person to visit a place, access a restricted system, observe a physical state or provide evidence that software cannot obtain directly. A human-in-the-loop task API turns that gap into a bounded execution request: define the task, fund it, route it to an eligible executor, require proof and consume the verified outcome.
Reference architecture
The agent identifies work it cannot safely or directly execute.
Specify outcome, scope, proof, deadline and payout rather than sending an open-ended request.
The payout becomes a concrete part of the task state before an eligible claim can proceed.
The agent or downstream system consumes the verified completion state instead of trusting an unstructured status message.
Minimal request shape
{
"title": "Verify storefront hours",
"scope": "Check the posted opening hours at the specified address.",
"proof": "Return a dated photo showing the hours.",
"deadline": "2026-10-06T18:00:00Z",
"payout": "10 USDT"
}
This example is intentionally conceptual: exact API authentication, payment methods and endpoint contracts should be taken from the platform's current developer documentation rather than inferred from this article.
Good human-in-the-loop tasks have three properties
Bounded execution: the agent can tell exactly what it asked a human to do. Verifiable proof: the completion evidence is specified before execution. Clear economics: the payout and deadline are visible instead of emerging after the work is done.
Where this model is useful
Examples include local verification, physical inventory checks, on-site observations, restricted-environment actions, document collection and other tasks where software cannot directly reach the required evidence. Safety, legality and privacy requirements still apply to every task.