Sinkai – Let AI agents hire humans for real-world tasks

Name: Sinkai – Let AI agents hire humans for real-world tasks
Availability: InStock
Author: tetubrah

by tetubrah·Feb 20, 2026·2 points·2 comments

Visit Project View on HN

AI Analysis

●●●BangerZero to OneBig Brain

Agents can now hire humans via API—solves real autonomous execution bottleneck.

Strengths

•Genuinely novel: bridges agent planning with human execution at explicit handoff boundaries
•MCP and OpenAPI native design respects existing agent architecture; not a bolted-on wrapper
•Thoughtful failure semantics: task_label, acceptance_criteria, structured failure_reason enable reliable loops

Weaknesses

•MVP SLA: 'no guarantees' on task completion timing limits production use cases today
•Early-stage reliability signals unclear; trust requirements for agents spending real money on tasks TBD

Post Description

I built Sinkai to handle tasks that pure software agents cannot complete alone (for example, on-site checks, physical evidence collection, and local human verification).

What it does: - AI agent sends a tool call (`POST /api/call_human`) - Human accepts task and submits photo/video/text proof - Agent receives structured result for downstream workflow

Current focus: - Reliability at handoff boundaries (planner -> executor -> verifier) - Human-in-the-loop operations with explicit failure states - MCP/OpenAPI friendly integration for agent builders

Docs and API: - for agents: https://sinkai.tokyo/for-agents - openapi: https://sinkai.tokyo/openapi.json - repo: https://github.com/tetubrah-del/Tool_Call_For_LLM

I would love feedback on: 1. trust/reliability signals you would require before production use 2. where to draw the boundary between autonomous execution and human escalation 3. failure modes we should expose more clearly in API responses