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File 03 · AMD Developer Hackathon Act III · Track 1 · Oct 2026

Operion

A maintenance copilot that catches a pump fault 36 hours early, explains it with checked evidence, and tells the supervisor whether to repair or replace.

Operion plant home: four machines in 3D, each with its live health score
  • 36.4 hmedian warning, vs 6.2 h for the legacy alarmSimulated
  • 40/40faults caught before failureSimulated
  • 97.5%correct diagnoses, 0 false alarms in 720 hSimulated

02:40, night shift. A coolant pump starts losing suction. Nothing on the panel is red: pressure has dipped a little, flow is slightly down, both well inside their limits. The fixed alarm will not fire for another day, and when it does it will say what tripped, not why.

I owned the agent. It reads the sensor trends, the maintenance history, the pump manual and the spare-parts stock, then names the fault: cavitation, 85% confidence, about 7 hours to failure. Stock levels always come from inventory, never from the model.

The supervisor then gets a five-year comparison: €142,559 to replace against €194,694 to keep repairing, paying back in 20 months.

What it refuses to doPaging anyone on unverified evidence. Every reference the agent cites is checked against plant data, and a bad one goes back to the model for a retry.

How it runs

  1. DetectLoad-regressed robust z-score plus EWMA. Plain math, every number readable.
  2. DiagnoseThe agent investigates on AMD and cites evidence for every claim.
  3. VerifyThe backend checks each reference against plant data before anyone is paged.
  4. AssignRoutes to the right person for that fault category, area and shift.
  5. DecideFive-year repair-versus-replace, with the numbers computed in code.

Honest caveatThe plant is simulated and the team designed the fault signatures. The repo says so too.

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