Autonomous Task Executor
Plans, approves, schedules, and runs long-running workflows — with cross-agent delegation, anomaly detection, chart-backed reports, and multi-step chains.
Why it matters
Plant operations run on recurring rhythms: hourly anomaly checks, daily sensor reports, weekly investigations. Writing and maintaining those as cron jobs is nobody’s favorite task. The executor turns one sentence — “send me sensor anomalies for pressure loops every half hour for the next day” — into a scheduled job with human approval, persistence across restarts, and a full audit trail.
Capabilities
- Natural-language planning — the model parses both the schedule and the task type from a single sentence, converting “every half hour for two hours” into interval plus max-run count.
- Human-in-the-loop approval — every plan pauses for explicit sign-off before it touches the scheduler.
- Persistent jobs — scheduled work survives process restarts via durable storage plus an append-only audit log.
- Rich task vocabulary — email, report, monitoring, data collection, notification, analysis, investigation, and multi-step chain, each with tailored execution semantics.
- Anomaly detection plus trend analysis — statistical thresholds and regression, with chart attachments rendered for email-ready reports.
- DAG chains — sequential or parallel steps with per-step failure policy (abort, skip, continue) and natural-language guards evaluated by the model.
What makes it hold up
Schedulers look simple on day one and get ugly on day thirty. The one thing this design got right early was making every job auditable by default — every run, every failure, every skipped step ends up in the log whether anyone reads it or not. Debugging a cron job you can’t see is a special kind of suffering. This doesn’t have that.
Enterprise project. Official writeup and demo link will be added once online.