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AI Architecture › L5 · Applications & Copilots
L5 · Applications & Copilots

Engineering Copilot: Maintenance Reasoning

Turning condition, history, and neutron-fluence exposure into inspection and intervention plans, with remote-handling constraints made explicit.

THE STACK · click to jumpL7Ecosystem & StrategyL6Experience & VisualizationL5Applications & CopilotsL4OrchestrationL3Twin Modeling & AIL2Data FabricL1Control PlaneL0Foundation▲tlmctl▼L5 · APPLICATIONS & COPILOTSAgentic copilots that reason over the machine.1Plasma Copilotscenario design2Engineering Copilotsubsystem analysis3Operations Copilotrunbooks & procedures4Agentic Toolsbounded action-taking5Knowledge BaseRAG over the fabric6Guardrailssafety-boundedMACHINE TIEReads the twin and fabric; proposes actions that route through L4.KRONOS FUSION ENERGYAI-NATIVE S.M.A.R.T. GENERATORAPPLICATIONS & COPILOTSSHEET 07REV. 2026-08L5 · AI-NATIVE STACK
L5 · Applications & Copilots — its place in the stack (left, click any layer) and its internal components (right). Telemetry rises; control descends.

Condition-based maintenance reasoning

The Engineering Copilot plans maintenance from condition rather than a fixed calendar. It combines subsystem-analysis trends, triaged anomaly events, and cumulative-exposure models — most importantly neutron fluence on the breeder's plasma-facing and structural components driven by the 85.0 MW / 14 MeV neutron output — to estimate remaining margin and recommend when and what to inspect or replace.

Inputs to a maintenance plan

The copilot reasons explicitly about activation and remote handling: after significant neutron exposure many interventions must be performed by remote handling, which reshapes the plan. It cites the activation and damage modeling and neutron-fluence maps rather than asserting exposure levels.

text
plan_maintenance(component):
  cond   = subsystem_analysis(component).trend
  flu    = neutronics.fluence(component)         # cumulative
  life   = material_model.remaining_margin(cond, flu)
  window = schedule_fit(life, campaign_calendar)
  method = 'remote' if activation(component) > hands_on_limit else 'hands_on'
  steps  = rag.retrieve_procedure(component, method)
  return proposal(window, method, steps, cite=[cond, flu, steps])

Both machines

For the burner (Aegis / MetroVolt), maintenance reasoning centers on the high-field plug and throat coils (26.49 T / 17 T), the DEC collector grid, and the lower but nonzero neutron load from the 5.44% neutron fraction. The activation picture is different from the breeder's, and the copilot uses the machine-specific fluence maps.

Maintenance plans are proposals handed to the Operations Copilot for step-by-step procedure generation and to human planners for approval. Because both machines are pre-FOAK design studies, current maintenance reasoning targets the commissioning and first-campaign plan and is validated against the twin, not an operating fleet.

Content reviewed August 2026 · design-and-simulation stage