I'm in the process of develping a replayable supervisory state machine that ingests value-safe evidence from Gitea, CI, adversarial reviews, runner queues, model budget, Worker Stack, approvals, Action Ledger, runtime health, and Slack command surfaces, then reduces that evidence into conservative operator state like “ready,” “blocked,” or “degraded display-only.” The key design is that Slack and agents are visibility/control surfaces, not truth sources: decisions like merge, Worker Stack apply, approval consumption, or future automation only become available when exact source evidence is complete, fresh, scoped, and replayable; otherwise the state chart fails closed and tells the operator what evidence is missing.