ship signed. roll back in one move.

A CI step builds, boot-tests on real silicon, SBOMs, cosigns and TUF-publishes every release — then an invisible agent attests each unit, stages the rollout in rings, and rolls any unit back in a single move.

every signal, on one timeline.

Every unit logs to one time-aligned substrate: metrics, logs, sensor streams and model I/O on a shared timeline, so every signal is comparable across parts and units. It's the single source of truth detect, diagnose and prove all read from.

catch the fault before the field does.

A full bank of detectors — envelope, anomaly, degradation, change-point, peer-outlier, log, and data-drift — judged against three baselines (spec, self, peer) and fused by agents into one ranked, explainable verdict.

the real cause — a part, or a config.

An abductive agent loop wields ML models as discriminating instruments over the unit's causal graph, falsifies the leading hypothesis, and lands on an actionable verdict: replace a part, or change a config to a better, canary-verified value.

reversible, gated, self-verifying.

An agent proposes a typed, reversible action; a deterministic executor runs it under safety gates; a critic verifies before, a validator auto-reverts after. The LLM is never in the actuation path.

compliance falls out of operation.

The signed exhaust of normal operation — CI provenance, SBOMs, attested deploy/rollback records — auto-generates OSCAL, CMMC and cATO evidence, anchored to a tamper-evident transparency log.

every unit, every part, always known.

A live map of every fielded unit, down to the part: each hardware lot, supplier and revision joined to the exact software version running on it over time. You always know what's out there — the question fleets usually can't answer.

every failure sharpens the next.

Every diagnosis and repair is remembered as a config→failure→repair record, and every staged deploy doubles as a controlled experiment — so the next failure is faster to solve than the last, across the whole fleet.