How an AI workforce runs itself
Thirty-six agents write, check and publish four posts a day on five platforms. Every team has its own clock, re-runs itself if a post does not go out, and is tested agent by agent every week.
Seven silos, and no row calls another
Each team has its own exact-time trigger on Cloudflare, its own workflow on GitHub Actions and its own agents. If one team breaks, the other six do not notice.
Every post is written, checked and either published or dropped
The leadership team as it runs in code: a LangGraph state graph. Routing is decided in code, not by the model, so a weak post is skipped instead of published.
Fixed slots, then a calm ripple to every platform
Each post goes to LinkedIn on the minute. The other platforms follow one at a time, at the same offsets every day, and never as a burst of old posts.
A missed slot repairs itself within minutes
After each slot the team's own Worker checks every five minutes. If no run has succeeded and none is running, it starts another. Alerts fire only if healing has not worked.
Every agent is tested on its own, every week
Each judge runs with its production prompt on planted mistakes and on clean answers. It must catch real faults without crying wolf, or the owner is alerted.