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AI workflow
Mission Control
I ran five scheduled AI agents drafting, publishing, measuring and re-planning social content across seven platforms, with no way to tell whether any of them were healthy short of reading log files.
So I built a single dark-mode console that reads the agents’ own output and renders the whole loop: every agent’s schedule, run history, exit code and cost, with one human approval gate before anything publishes.
It has tracked 25 posts published, 21 measured, 5,082 impressions and 58 outcome events without a database, at $0.02 in total agent spend across 481 tool calls.
Six-stage social pipeline: one manual gate, five agent cards with schedule and health, plus the approval queue. 25 published, 21 measured.Full social section: the post feed with status tags and vault paths for every draft and published item.Faceless video pipeline: nine stages from script to thumbnail, each flagged not started, in progress, or needs you.YouTube launch kit: turns a raw transcript into a publish-ready doc in five steps, with a table of what shipped and view counts.Blog section: unpublished drafts with word count and age, each with a preview and one-click send to WordPress.HeyGen section: avatar video and voiceover generation wired in, with a credit check before anything spends.
The dashboard sits behind a private mesh network by design, so there is no public link.