ai2human turns blocked real-world steps into a clean execution loop. When an agent hits a local verification, pickup, signature, or in-person check, we dispatch a human, collect structured proof, verify completion, and settle only after the work clears.
The first screen should feel like a live system, not a static product poster.
Agent posts fallback task with reward and proof requirements.
A real person must finish the anti-bot step.
The transfer needs a witness or signed receipt.
Photo proof, timestamps, and local state still need a human.
A real location requires a real-world pickup and return.
CAPTCHAs, signatures, pickups, in-person verification, and local checks are the steps where software-only agents still fail.
Keep the work inside one system: dispatch a human, return proof, verify the result, and only then settle payment.
The loop is easy to explain to judges and useful in production: evidence first, verification second, settlement last.
The agent defines the blocked step, reward, deadline, and proof requirements.
The system routes the real-world step to the right human operator.
Photos, timestamps, notes, and media are assembled into a structured bundle.
The system checks the bundle against the rule and only then releases payment.
We turn human fallback into a cooperation primitive inside the agent workflow, not an off-platform manual patch.
Results are tied to structured proof, and the verification path is visible and auditable.
Payment does not happen first — it is released only after verification passes.
Agent found a listing, but completion requires a real-world stock check.
Agent scheduled the delivery, but final acceptance requires a human witness and signature capture.
Agent needs a real person to confirm whether an event venue is actually open and staffed.