Most AI prompts fail in real business settings because a single chat window tries to plan, calculate math, write copy, and overwrite files all at once. Click each node in the 5-stage state machine below to inspect what happens and why this design prevents hallucinations and broken apps.
Raw notes, CSV exports, or feature requests enter through a quarantined staging folder (`_Inbox_Staging/`) or a scoped task queue entry (`TASKS.md`) with an explicit file-touch boundary.
{
"stage": "01_INBOX_STAGING",
"input_path": "white-collar-cowork/_Inbox_Staging/sample_q4_automation_intake.md",
"allowed_writes": ["white-collar-cowork/_Inbox_Staging/"],
"master_files_protected": true
}
Running 100% of your tasks through a heavyweight frontier model wastes 70% to 85% of your token budget on routine formatting. By splitting the Supervisor Router (which plans once) from Specialist Sub-Agents (which execute bounded steps using fast models and deterministic Python MCP tools), your error rate drops while your API cost falls by 4x.
When Agent A hands work to Agent B, 90% of failures happen because Agent A passes messy chat history. Every worker in this kit emits a structured switchyard.digest/1 JSON envelope listing exact files touched, verification results, and residual risks so you never suffer from debugging blindness.